TY - JOUR TI - A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 1424-8220 T2 - Sensors DO - 10.3390/s23177472 UR - https://doi.org/10.3390/s23177472 DA - 2023-08-28 AU - Deng, Minyi C1 - Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China AU - Yang, Qingqing C1 - Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China AU - Peng, Yi C1 - Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China LA - en KW - Waypoint KW - Obstacle KW - Motion planning KW - Obstacle avoidance KW - Computer science KW - Real-time computing KW - Simulated annealing KW - Path (computing) KW - Smoothness KW - Mathematical optimization KW - Simulation KW - Algorithm KW - Artificial intelligence KW - Mobile robot KW - Mathematics KW - Robot KW - Geography VL - 23 IS - 17 SP - 7472 EP - 7472 AB - To solve the problem of poor real-time performance in path planning algorithms for unmanned aerial vehicles (UAVs) in low-altitude urban logistics, a path planning method combining modified Beetle Antennae Search (BAS) with the Simulated Annealing (SA) algorithm is proposed. Firstly, based on the requirements of task execution and constraints of UAV flight, a fitness function for real-time search of waypoints is designed while ensuring the safety and obstacle avoidance of the UAV. Then, to improve the search accuracy and real-time performance, determining the initial search direction in the BAS algorithm is improved, while the search step size and antennae sensing length are updated in real-time according to the distance between the UAV and the obstacle. Finally, the SA algorithm is combined with the BAS algorithm to update the waypoints, expanding the search range of each waypoint, avoiding the process of updating the waypoints from becoming trapped in the local optimal waypoints. Meanwhile, the effectiveness of the next waypoint is evaluated based on the Metropolis criterion. This paper generates a virtual urban logistics distribution environment based on the density and distribution of urban buildings, and compares the performance of algorithms in obstacle-sparse, obstacle-moderate, and obstacle-dense environments. The simulation results demonstrate that the improved method in this paper has a more significant capacity for environmental adaptation. In terms of the path length, waypoints, safety obstacle avoidance, and smoothness, the planned path outperforms the original BAS method. It satisfies the needs of real-time path planning for UAVs involved in urban low-altitude logistics. ER - TY - JOUR TI - Potentialities of drones and ground autonomous delivery devices for last-mile logistics PY - 2021 PB - Elsevier BV SN - 1366-5545 T2 - Transportation Research Part E Logistics and Transportation Review DO - 10.1016/j.tre.2021.102325 UR - https://doi.org/10.1016/j.tre.2021.102325 DA - 2021-04-13 AU - Lemardelé, Clément C1 - Civil Engineering School of Barcelona. Universitat Politècnica de Catalunya – Barcelona TECH, C. Jordi Girona 1-3, Edificio Omega, Planta 0 Oficina 05, 08034 Barcelona, Spain AU - Estrada, Miquel C1 - Civil Engineering School of Barcelona, Universitat Politècnica de Catalunya – Barcelona TECH, C. Jordi Girona 1-3, B1-110 08034 Barcelona, Spain AU - Pagès, Laia C1 - CARNET Barcelona. C. Jordi Girona 1-3, Edificio Omega, Planta 0 Oficina 05, 08034 Barcelona, Spain AU - Bachofner, Mónika C1 - CARNET Barcelona. C. Jordi Girona 1-3, Edificio Omega, Planta 0 Oficina 05, 08034 Barcelona, Spain LA - en KW - Drone KW - Last mile (transportation) KW - Boom KW - Truck KW - Externality KW - Context (archaeology) KW - Computer science KW - Transport engineering KW - Operations research KW - City logistics KW - Mile KW - Engineering KW - Geography KW - Economics KW - Microeconomics VL - 149 SP - 102325 EP - 102325 ER - TY - JOUR TI - An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace PY - 2021 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su132313079 UR - https://doi.org/10.3390/su132313079 DA - 2021-11-25 AU - Li, Shan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China AU - Li, Zhuolun C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China AU - Liu, Hao C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China LA - en KW - Drone KW - Intersection (aeronautics) KW - Transport engineering KW - Terminal (telecommunication) KW - Redundancy (engineering) KW - Computer science KW - Route planning KW - Air traffic control KW - Operations research KW - Engineering KW - Computer network KW - Aerospace engineering VL - 13 IS - 23 SP - 13079 EP - 13079 AB - Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future. ER - TY - JOUR TI - Multi-UAV Urban Logistics Task Allocation Method Based on MCTS PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones7110679 UR - https://doi.org/10.3390/drones7110679 DA - 2023-11-17 AU - Ma, Zeyuan C1 - State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China AU - Chen, Jing C1 - Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430072, China C1 - State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China LA - en KW - Computer science KW - Task (project management) KW - Energy consumption KW - Process (computing) KW - Node (physics) KW - Monte Carlo tree search KW - Selection (genetic algorithm) KW - Energy (signal processing) KW - Tree (set theory) KW - Distributed computing KW - Real-time computing KW - Monte Carlo method KW - Artificial intelligence KW - Systems engineering KW - Engineering VL - 7 IS - 11 SP - 679 EP - 679 AB - Unmanned aerial vehicles (UAVs) open new methods for efficient and rapid transportation in urban logistics distribution, where task allocation is a significant issue. In urban logistics systems, the energy status of UAVs is a critical factor in ensuring mission fulfillment. While extensive literature addresses the energy consumption of UAVs during tasks, the feasibility of energy replenishment must be addressed, which introduces additional uncertainty to the task allocation. This paper realizes multi-tasking, considering the energy consumption and replenishment of UAVs, to ensure that the tasks can be accomplished while reducing energy consumption. This paper proposes uniform distribution K-means to realize balanced multi-task grouping. Based on the Monte Carlo tree search (MCTS), a task-allocation-oriented MCTS method is proposed, including improving the selection and simulation process of MCTS. The aim was to collaborate with multiple trees for node selection and record historical simulation information to guide subsequent simulations for better results. Finally, the optimality of the proposed method was validated by comparing it with other relevant MCTS methods through several randomized experiments. ER - TY - JOUR TI - Extending Delivery Range and Decelerating Battery Aging of Logistics UAVs Using Public Buses PY - 2022 PB - IEEE Computer Society SN - 1536-1233 T2 - IEEE Transactions on Mobile Computing DO - 10.1109/tmc.2022.3167040 UR - https://doi.org/10.1109/tmc.2022.3167040 DA - 2022-04-13 AU - Pan, Yan C1 - National University of Defense Technology C1 - Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 58294 Changsha, Hunan, China AU - Chen, Qianwu C1 - Northwestern Polytechnical University C1 - School of Computer Science, Northwestern Polytechnical University, 26487 Xi'an, Shaanxi, China AU - Zhang, Nan C1 - Northwestern Polytechnical University C1 - school of computer science, Northwestern Polytechnical University, 26487 Xi'an, Shaanxi, China AU - Li, Zhigang C1 - Northwestern Polytechnical University C1 - School of Computer Science, Northwestern Polytechnical University, 26487 Xi'an, Shaanxi, China AU - Zhu, Ting C1 - University of Maryland at Baltimore County C1 - Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States AU - Han, Qingye C1 - Chongqing University C1 - School of Management Science and Real Estate, Chongqing University, 47913 Chongqing, chongqing, China LA - en KW - Computer science KW - Battery (electricity) KW - Battery capacity KW - Scheduling (production processes) KW - Range (aeronautics) KW - Real-time computing KW - Public transport KW - Truck KW - Automotive engineering KW - Simulation KW - Transport engineering KW - Engineering KW - Power (physics) VL - 22 IS - 9 SP - 5280 EP - 5295 ER - TY - JOUR TI - Integrating wind field analysis in UAV path planning: Enhancing safety and energy efficiency for urban logistics PY - 2025 PB - Elsevier BV SN - 1000-9361 T2 - Chinese Journal of Aeronautics DO - 10.1016/j.cja.2025.103605 UR - https://doi.org/10.1016/j.cja.2025.103605 DA - 2025-05-31 AU - Gu, R. C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Zhao, Yifei C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Ren, Xinhui C1 - College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Field (mathematics) KW - Path (computing) KW - Motion planning KW - Energy (signal processing) KW - Wind power KW - Environmental science KW - Marine engineering KW - Aerospace engineering KW - Computer science KW - Transport engineering KW - Engineering KW - Physics KW - Electrical engineering KW - Computer network KW - Mathematics KW - Artificial intelligence VL - 39 IS - 1 SP - 103605 EP - 103605 AB - Shenzhen, a major city in southern China, has experienced rapid advancements in Unmanned Aerial Vehicle (UAV) technology, resulting in extensive logistics networks with thousands of daily flights. However, frequent disruptions due to its subtropical monsoon climate, including typhoons and gusty winds, present ongoing challenges. Despite the growing focus on operational costs and third-party risks, research on low-altitude urban wind fields remains scarce. This study addresses this gap by integrating wind field analysis into UAV path planning, introducing key innovations to the classical model. First, UAV wind resistance and turbulence constraints are analyzed, mapping high-wind-speed and turbulence-prone zones in the airspace. Second, wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation, optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency. Additionally, a wind-aware Theta* algorithm is proposed, leveraging wind vectors to expedite search process, while Computational Fluid Dynamics (CFD) techniques are employed to calculate wind fields. A case study of Shenzhen, examining wind patterns over the past decade, demonstrates a 6.23% improvement in groundspeed and a 7.69% reduction in energy consumption compared to wind-agnostic models. This framework advances UAV logistics by enhancing route safety and energy efficiency, contributing to more cost-effective operations. ER - TY - JOUR TI - Efficient Path-Following for Urban Logistics: A Fuzzy Control Strategy for Consumer UAVs Under Disturbance Constraints PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 0098-3063 T2 - IEEE Transactions on Consumer Electronics DO - 10.1109/tce.2025.3564412 UR - https://doi.org/10.1109/tce.2025.3564412 DA - 2025-04-25 AU - Shao, Xingling C1 - School of Electrical and Control Engineering and the State Key Laboratory of Extreme Environment Optoelecronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan, China C1 - School of Electrical and Control Engineering, North University of China, Taiyuan, China AU - Du, Jun C1 - Department of Electronic Engineering and the State Key laboratory of Space Network and Communications, Tsinghua University, Beijing, China C1 - Department of Electronic Engineering, Tsinghua University, Beijing, China AU - 山下, 一平 C1 - College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China AU - Zhang, Zekai C1 - Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China AU - Hou, Xiangwang C1 - Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Debbah, Mérouane C1 - KU 6G Research Center, Khalifa University of Science and Technology, Abu Dhabi, UAE C1 - KU6G Research Center, Khalifa University of Science and Technology, Abu Dhabi, UAE LA - en KW - Disturbance (geology) KW - Path (computing) KW - Computer science KW - Fuzzy logic KW - Control theory (sociology) KW - Control (management) KW - Fuzzy control system KW - Engineering KW - Control engineering KW - Artificial intelligence VL - 71 IS - 2 SP - 7117 EP - 7128 ER - TY - JOUR TI - Hazardous flight region prediction for a small UAV operated in an urban area using a deep neural network PY - 2021 PB - Elsevier BV SN - 1270-9638 T2 - Aerospace Science and Technology DO - 10.1016/j.ast.2021.107060 UR - https://doi.org/10.1016/j.ast.2021.107060 DA - 2021-08-25 AU - Jeong, Shinkyu C1 - Department of Mechanical Engineering, Kyunghee University, Yongin 17104, Republic of Korea AU - You, Kangkuk C1 - Department of Mechanical Engineering, Kyunghee University, Yongin 17104, Republic of Korea AU - Seok, Donghoon C1 - Department of Mechanical Engineering, Kyunghee University, Yongin 17104, Republic of Korea LA - en KW - Trajectory KW - Wind speed KW - Terrain KW - Computer science KW - Simulation KW - Meteorology KW - Environmental science KW - Real-time computing KW - Geography VL - 118 SP - 107060 EP - 107060 ER - TY - JOUR TI - Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app13095370 UR - https://doi.org/10.3390/app13095370 DA - 2023-04-25 AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Wu, Shixin C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Feng, Ouge C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Tian, Tian C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Huang, Yuting C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Zhong, Gang C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China LA - en KW - Computer science KW - Scheduling (production processes) KW - Scheme (mathematics) KW - Real-time computing KW - Simulated annealing KW - Low altitude KW - Operations research KW - Mathematical optimization KW - Distributed computing KW - Altitude (triangle) KW - Engineering KW - Algorithm KW - Mathematics VL - 13 IS - 9 SP - 5370 EP - 5370 AB - Aiming at the problem of the scheduling scheme of urban low-altitude logistics unmanned aerial vehicles (UAVs), this paper establishes a demand-based UAV scheduling scheme model using an improved simulated annealing algorithm, taking minimizing the cost of distribution as the objective function and considering restrictions such as UAV performance constraints, airspace constraints, and distribution constraints, among others. For verification, actual express data and airspace constraints in Shanghai are taken as examples. Two urban air traffic networks are constructed using road and building data. The analysis results show that the planning scheme of this model is superior to other forecasting models in terms of delivery cost and delivery time. In addition, this model can flexibly calculate the optimal scheduling scheme under the constraints of multiple parameters, according to the requirements of delivery volume, delivery distance, UAV performance, etc. ER - TY - JOUR TI - Autonomous Delivery Solutions for Last-Mile Logistics Operations: A Literature Review and Research Agenda PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su15032774 UR - https://doi.org/10.3390/su15032774 DA - 2023-02-03 AU - Engesser, Valeska C1 - Research Group Mobi, Mobility, Logistics and Automotive Technology Research Centre, Department BUTO—Business Technology and Operations, Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium C1 - Research Group Mobilise, Department BUTO—Business Technology and Operations, Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium AU - Rombaut, Evy C1 - Research Group Mobi, Mobility, Logistics and Automotive Technology Research Centre, Department BUTO—Business Technology and Operations, Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium AU - Vanhaverbeke, Lieselot C1 - Research Group Mobi, Mobility, Logistics and Automotive Technology Research Centre, Department BUTO—Business Technology and Operations, Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium AU - Lebeau, Philippe C1 - Research Group Mobilise, Department BUTO—Business Technology and Operations, Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium LA - en KW - Last mile (transportation) KW - Work (physics) KW - Traffic congestion KW - Service (business) KW - Transport engineering KW - Humanitarian Logistics KW - Business KW - Risk analysis (engineering) KW - Computer science KW - Process management KW - Mile KW - Marketing KW - Engineering VL - 15 IS - 3 SP - 2774 EP - 2774 AB - The implementation of autonomous delivery solutions in last-mile logistics operations is considered promising. Autonomous delivery solutions can help in tackling urban challenges related to last-mile logistics operations. Urbanization creates higher mobility and transportation demand, which contributes to increased congestion levels, traffic, air pollution, and accident rates. Moreover, mega-trends, such as e-commerce, demand that logistics companies react to increased customer expectations in terms of delivery time and service. Concerning service, electrified autonomous delivery solutions have the potential to operate 24/7 and can help to overcome driver shortages. This paper conducts a systematic literature review. Based on the literature set, a snowballing procedure was applied. Complementary gray literature was included. This work discusses different autonomous delivery solutions such as Autonomous Delivery Robots (ADRs), Unmanned Aerial Vehicles (UAVs), two- or multi-tiered systems, and the concept of passenger and freight integration. The work presents advantages and disadvantages, enabling the comparison of solutions. Furthermore, a research agenda is provided, from which practical-managerial and theoretical implications can be derived. The research agenda can help researchers, manufacturers, businesses, and governmental institutions to prepare for the arrival and subsequent implementation of autonomous delivery services. Various implications related to energy demand, legislation, implementation strategy, training, and risk and safety are presented. The outcome of this work calls for collaboration among various stakeholders, encourages mutual learning, and hints at the importance of national and international development projects. ER - TY - JOUR TI - AI-Enhanced Eco-Efficient UAV Design for Sustainable Urban Logistics: Integration of Embedded Intelligence and Renewable Energy Systems PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 1996-1073 T2 - Energies DO - 10.3390/en18195242 UR - https://doi.org/10.3390/en18195242 DA - 2025-10-02 AU - Bibbò, Luigi C1 - Department of Civil, Energy, Environment and Materials (DICEAM), Mediterranea University of Reggio Calabria, Via Zehender, 89124 Reggio Calabria, Italy AU - Laganà, Filippo C1 - Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science “Magna Græcia” University, Viale Europa, Località Germaneto, snc, 88100 Catanzaro, Italy AU - Bilotta, Giuliana C1 - Department of Civil, Energy, Environment and Materials (DICEAM), Mediterranea University of Reggio Calabria, Via Zehender, 89124 Reggio Calabria, Italy AU - Meduri, Giuseppe C1 - Department of Civil, Energy, Environment and Materials (DICEAM), Mediterranea University of Reggio Calabria, Via Zehender, 89124 Reggio Calabria, Italy AU - Angiulli, Giovanni C1 - Department of Information Engineering, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, Via R. Zehender, 89124 Reggio Calabria, Italy AU - Cotroneo, Francesco C1 - Nophys srl, Via Maddaloni, 74, 00177 Roma, Italy LA - en KW - Payload (computing) KW - Drone KW - Energy consumption KW - Software portability KW - Renewable energy KW - Efficient energy use KW - Scalability KW - Testbed KW - Smart city VL - 18 IS - 19 SP - 5242 EP - 5242 AB - The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO2 emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. ER - TY - JOUR TI - Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning PY - 2024 PB - Multidisciplinary Digital Publishing Institute SN - 2624-6511 T2 - Smart Cities DO - 10.3390/smartcities7050110 UR - https://doi.org/10.3390/smartcities7050110 DA - 2024-10-06 AU - Rinaldi, Marco C1 - Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy AU - Primatesta, Stefano C1 - Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy AU - Bugaj, Martin C1 - Air Transport Department, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia AU - Rostáš, Ján C1 - Air Transport Department, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia AU - Guglieri, Giorgio C1 - Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy LA - en KW - Scheduling (production processes) KW - Route planning KW - Computer science KW - Task (project management) KW - Chromosome KW - Aeronautics KW - Transport engineering KW - Engineering KW - Operations management KW - Biology KW - Systems engineering KW - Gene KW - Genetics VL - 7 IS - 5 SP - 2842 EP - 2860 AB - In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. ER - TY - JOUR TI - Advancing Social Equity in Urban UAV Logistics: Insights from the Academic Literature and Social Media PY - 2024 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones8110688 UR - https://doi.org/10.3390/drones8110688 DA - 2024-11-19 AU - Zhang, Dong C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong AU - Yang, Perry C1 - College of Architecture, Georgia Institute of Technology, Atlanta, GA 30308, USA AU - Tsou, Jin C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong LA - en KW - Equity (law) KW - Social media KW - Business KW - Social equality KW - Sociology KW - Regional science KW - Public relations KW - Computer science KW - Political science KW - World Wide Web VL - 8 IS - 11 SP - 688 EP - 688 AB - In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders. ER - TY - GEN TI - Research on Path Planning and Evaluation Method of Urban Logistics UAV PY - 2021 T2 - 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST) DO - 10.1109/iaecst54258.2021.9695662 UR - https://doi.org/10.1109/iaecst54258.2021.9695662 DA - 2021-12-10 AU - Li, Bowen C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Zhang, Liandong C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Feng, Dikun C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Fei, Yuhan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China LA - en KW - Analytic hierarchy process KW - Motion planning KW - Computer science KW - Path (computing) KW - Plan (archaeology) KW - Operations research KW - Process (computing) KW - Fuzzy logic KW - Transport engineering KW - Engineering KW - Artificial intelligence KW - Robot KW - Computer network SP - 1465 EP - 1470 ER - TY - JOUR TI - Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su151512021 UR - https://doi.org/10.3390/su151512021 DA - 2023-08-05 AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Tian, Tian C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Feng, Ouge C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Wu, Shixin C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Zhong, Gang C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China LA - en KW - Voronoi diagram KW - Obstacle KW - Transport engineering KW - Computer science KW - Route planning KW - Grid KW - Flow network KW - Motion planning KW - Drone KW - Geography KW - Engineering KW - Mathematical optimization KW - Artificial intelligence VL - 15 IS - 15 SP - 12021 EP - 12021 AB - As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized. ER - TY - JOUR TI - Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera PY - 2019 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2019.2929760 UR - https://doi.org/10.1109/access.2019.2929760 DA - 2019-01-01 AU - Zhao, Xiaoyue C1 - School of Electronic Information, Wuhan University, Wuhan, China AU - Pu, Fangling C1 - School of Electronic Information, Wuhan University, Wuhan, China C1 - ORCiD AU - Wang, Zhihang C1 - School of Electronic Information, Wuhan University, Wuhan, China AU - Chen, Hongyu C1 - School of Electronic Information, Wuhan University, Wuhan, China AU - Xu, Zhaozhuo C1 - Electrical Engineering Department, Stanford University, Stanford, CA, USA LA - en KW - Geolocation KW - Computer vision KW - Computer science KW - Artificial intelligence KW - Tracking (education) KW - Monocular KW - Vehicle tracking system KW - Kalman filter VL - 7 SP - 101160 EP - 101170 AB - Unmanned aerial vehicles (UAVs) have been widely used in urban traffic supervision in recent years. However, the detection, tracking, and geolocation of moving vehicle based on the airborne platform suffer from small object sizes, complex scenes, and low-accuracy sensors. To address these problems, this paper develops a framework for moving vehicle detecting, tracking, and geolocating based on a monocular camera, a GPS receiver, and inertial measurement units (IMUs) sensors. First, the method based on YOLOv3 was employed for vehicle detection due to its effectiveness and efficiency for small object detection in complex scenes. Then, a visual tracking method based on correlation filters is introduced, and a passive geolocation method is presented to calculate the GPS coordinates of the moving vehicle. Finally, a flight control method in terms of the previous image processing results is introduced to lead the UAV that is following the interesting moving vehicle. The proposed scheme has been built on a DJI M100 platform on which a monocular camera and a microcomputer Jetson TX1 are added. The experimental results show that this scheme is capable of detecting, tracking, and geolocating the interesting moving vehicle with high precision. The framework demonstrates its capacity in automatic supervision on target vehicles in real-world experiments, which suggests its potential applications in urban traffic, logistics, and security. ER - TY - JOUR TI - Air Route Network Planning Method of Urban Low-Altitude Logistics UAV with Double-Layer Structure PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9030193 UR - https://doi.org/10.3390/drones9030193 DA - 2025-03-06 AU - Li, Zhendong C1 - Jiangsu Provincial Key Laboratory of Urban Intelligent Transportation, Southeast University, Nanjing 211189, China C1 - School of Transportation, Southeast University, Nanjing 211189, China AU - Li, Shan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China C1 - College of Design and Engineering, National University of Singapore, Singapore 119077, Singapore AU - Lu, Jian C1 - Jiangsu Provincial Key Laboratory of Urban Intelligent Transportation, Southeast University, Nanjing 211189, China C1 - School of Transportation, Southeast University, Nanjing 211189, China AU - Wang, S. C1 - Jiangsu Provincial Key Laboratory of Urban Intelligent Transportation, Southeast University, Nanjing 211189, China C1 - School of Transportation, Southeast University, Nanjing 211189, China LA - en KW - Layer (electronics) KW - Altitude (triangle) KW - Route planning KW - City logistics KW - Low altitude KW - Transport engineering KW - Business KW - Environmental science KW - Computer science KW - Automotive engineering KW - Materials science KW - Engineering KW - Nanotechnology VL - 9 IS - 3 SP - 193 EP - 193 AB - With the rapid development of e-commerce, logistics UAVs (unmanned aerial vehicles) have shown great potential in the field of urban logistics. However, the large-scale operation of logistics UAVs has brought challenges to air traffic management, and the competitiveness of UAV logistics is still weak compared with traditional ground logistics. Therefore, this paper constructs a double-layer route network structure that separates logistics transshipment from terminal delivery. In the delivery layer, a door-to-door distribution mode is adopted, and the transshipment node service location model is constructed, so as to obtain the location of the transshipment node and the service relationship. In the transshipment layer, the index of the route betweenness standard deviation (BSD) is introduced to construct the route network planning model. In order to solve the above model, a double-layer algorithm was designed. In the upper layer, the multi-objective simulated annealing algorithm (MOSA) is used to solve the transshipment node service location issue, and in the lower layer, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the network planning model. Based on real obstacle data and the demand situation, the double-layer network was constructed through simulation experiments. To verify the network rationality, actual flights were carried out on some routes, and it was found that the gap between the UAV’s autonomous flight route time and the theoretical calculations was relatively small. The simulation results show that compared with the single-layer network, the total distance with the double-layer network was reduced by 62.5% and the structural intersection was reduced by 96.9%. Compared with the minimum spanning tree (MST) algorithm, the total task flight distance obtained with the NSGA-II was reduced by 42.4%. The BSD factors can mitigate potential congestion risks. The route network proposed in this paper can effectively reduce the number of intersections and make the UAV passing volume more balanced. ER - TY - JOUR TI - A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9070512 UR - https://doi.org/10.3390/drones9070512 DA - 2025-07-21 AU - Wen, Junfu C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Wang, Fei C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Su, Yu C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Depot KW - City logistics KW - Layer (electronics) KW - Computer science KW - Transport engineering KW - Process management KW - Business KW - Engineering KW - Materials science KW - Nanotechnology KW - Geography VL - 9 IS - 7 SP - 512 EP - 512 AB - To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. ER - TY - JOUR TI - Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su142013421 UR - https://doi.org/10.3390/su142013421 DA - 2022-10-18 AU - Li, Yafei C1 - School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Liu, Minghuan C1 - School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Energy consumption KW - Obstacle KW - Motion planning KW - Takeoff KW - Voronoi diagram KW - Path (computing) KW - Computer science KW - Energy (signal processing) KW - Obstacle avoidance KW - Battery (electricity) KW - Automotive engineering KW - Simulation KW - Engineering KW - Mobile robot KW - Mathematics KW - Geography KW - Robot KW - Artificial intelligence KW - Electrical engineering VL - 14 IS - 20 SP - 13421 EP - 13421 AB - As a new mode of transportation in the future, electric vertical take-off and landing unmanned aerial vehicles (eVTOL UAV) can undertake the task of logistics distribution and carry people in urban areas. It is challenging to carry out research designed to plan the path of eVTOL UAVs which can have a safe and sustainable operation mode in urban areas. Therefore, this work proposes a method for planning an obstacle-free path for eVTOL UAVs in urban areas with the goal of minimizing energy consumption. It aims to improve the safety and sustainability of eVTOL UAV operations. Based on variations of air density with height, a more accurate formula for calculating battery energy consumption of eVTOL UAV is derived. It is used in the vertical takeoff and landing phase and horizontal flight phase, respectively. Considering the influence of buildings on eVTOL UAV operation, a path planning method applicable to complex urban environments is proposed. The safe nodes of eVTOL UAV flight are obtained by using Voronoi diagrams based on building locations. Then, the complete shortest and obstacle-free path is obtained by using a Dubins geometric path and Floyd algorithm. After obtaining the obstacle-free paths for all flight height zones, the battery energy consumption of the eVTOL UAV in each flight height zone is calculated. Then, the flight height with the minimum energy consumption is obtained. The simulation results show that the path length obtained by the proposed path planning method is shorter than that obtained by particle swarm optimization; the total battery energy consumption changes in the same pattern in the low-altitude areas and high-altitude areas; the difference between the maximum and minimum energy consumption in the small area enables the eVTOL UAV to cover about 350 m more, and about 420 m more in the large area. Therefore, in future high-frequency UAV mission flights, choosing the altitude with the lowest energy consumption can reduce UAV operator costs. It can also significantly increase UAV transport range and make UAVs operate more sustainably. ER - TY - JOUR TI - 6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles PY - 2024 PB - Elsevier BV SN - 1874-4907 T2 - Physical Communication DO - 10.1016/j.phycom.2024.102479 UR - https://doi.org/10.1016/j.phycom.2024.102479 DA - 2024-09-03 AU - Nawaz, Muhammad C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Zhang, Wanquan C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Flynn, David C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Zhang, Lei C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Swash, Mohammad C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Abbasi, Qammer C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Imran, Muhammad C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom AU - Popoola, Olaoluwa C1 - University of Glasgow, Autonomous System and Connectivity, James Watt School of Engineering, United Kingdom LA - en KW - Computer science KW - Distributed computing KW - Efficient energy use KW - Energy consumption KW - Latency (audio) KW - Overhead (engineering) KW - Reinforcement learning KW - Scalability KW - Architecture KW - Artificial intelligence KW - Telecommunications VL - 67 SP - 102479 EP - 102479 AB - The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge, as well as with a centralized system. ER - TY - JOUR TI - Multiobjective Scheduling of Logistics UAVs Based on Variable Neighborhood Search PY - 2020 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app10103575 UR - https://doi.org/10.3390/app10103575 DA - 2020-05-21 AU - Li, Yixuan C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China AU - Yuan, Xiaoxiang C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China AU - Zhu, Jie C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China AU - Huang, Haiping C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China AU - Wu, Min C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China LA - en KW - Variable neighborhood search KW - Computer science KW - Mathematical optimization KW - Integer programming KW - Scheduling (production processes) KW - Robustness (evolution) KW - Linear programming KW - Job shop scheduling KW - Metaheuristic KW - Algorithm KW - Mathematics KW - Schedule VL - 10 IS - 10 SP - 3575 EP - 3575 AB - This study focuses on the issue of logistics Unmanned Aerial Vehicle (UAV) distribution in urban environment and an automatic delivery system to support the delivery of packages. It can effectively integrate existing facilities and be easily deployed. There is a scheduling problem in this system with multiple UAVs and multiple flights. We manage to optimize the two objectives of customer satisfaction and total completion time. The scheduling problem is formulated to a Mixed Integer Linear Programming (MILP), and we propose a multiple objectives decision-making method. A special encoding method suitable for the small scale problem is presented and Variable Neighborhood Search (VNS) algorithm framework is used to generate the approximate optimal solution for this problem. In experiments, we calibrate the important parameter and analyze the robustness of the algorithm. The experimental results show that the proposed algorithms are efficient for this problem. ER - TY - JOUR TI - Considering both energy effectiveness and flight safety in UAV trajectory planning for intelligent logistics PY - 2025 PB - Elsevier BV SN - 2214-2096 T2 - Vehicular Communications DO - 10.1016/j.vehcom.2025.100885 UR - https://doi.org/10.1016/j.vehcom.2025.100885 DA - 2025-01-18 AU - Liu, Zhiyang C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China AU - Li, Liuhuan C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China AU - Zhang, Xiao C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China AU - Tang, Wan C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China AU - Yang, Zhen C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China AU - Yang, Ximin C1 - College of Computer Science, South-Central Minzu University, Wuhan, 430074, China C1 - Key Laboratory of Cyber-Physical Fusion Intelligent Computing, State Ethnic Affairs Commission, Wuhan, 430074, China LA - en KW - Computer science KW - Trajectory KW - Energy (signal processing) KW - Operations research KW - Real-time computing KW - Aeronautics VL - 52 SP - 100885 EP - 100885 ER - TY - JOUR TI - An Operational Capacity Assessment Method for an Urban Low-Altitude Unmanned Aerial Vehicle Logistics Route Network PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones7090582 UR - https://doi.org/10.3390/drones7090582 DA - 2023-09-15 AU - Jia, Yi C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China AU - Wang, Fei C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China AU - Ning, Changyuan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China AU - Liu, Hao C1 - College of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China AU - Zhong, Gang C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, No. 29 General Avenue, Nanjing 211106, China LA - en KW - Computer science KW - Transport engineering KW - Dynamism KW - Key (lock) KW - Sorting KW - Operations research KW - Engineering VL - 7 IS - 9 SP - 582 EP - 582 AB - The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed. ER - TY - JOUR TI - A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics PY - 2025 T2 - Preprints.org DO - 10.20944/preprints202506.1002.v1 UR - https://doi.org/10.20944/preprints202506.1002.v1 DA - 2025-06-12 AU - Wen, Junfu AU - Wang, Fei AU - Su, Yu LA - en KW - Depot KW - Layer (electronics) KW - Computer science KW - Process management KW - Transport engineering KW - Business KW - Engineering KW - Geography KW - Materials science KW - Nanotechnology AB - To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple Unmanned Aerial Vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic cost, delivery delay, and the number of UAVs deployed. An Enhanced Non-dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved Particle Swarm Optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic cost and 71.4% in UAV deployment quantity, while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average Inverted Generational Distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. ER - TY - JOUR TI - RETRACTED ARTICLE: Knowledge-Driven Logistics Transformation: Complex Networks and UAVs in Distribution PY - 2024 PB - Springer Science+Business Media SN - 1868-7865 T2 - Journal of the Knowledge Economy DO - 10.1007/s13132-024-01984-z UR - https://doi.org/10.1007/s13132-024-01984-z DA - 2024-05-16 AU - Huang, Longyang C1 - College of Air Traffic Management, Civil Aviation Flight University of China, 618307, Guanghan, China AU - Li, Siyi C1 - College of Air Traffic Management, Civil Aviation Flight University of China, 618307, Guanghan, China AU - Zou, Xiang C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, 610041, Chengdu, Sichuan, China AU - Zhao, Bozhi C1 - College of Air Traffic Management, Civil Aviation Flight University of China, 618307, Guanghan, China AU - Li, Chenglong C1 - College of Air Traffic Management, Civil Aviation Flight University of China, 618307, Guanghan, China LA - en KW - Transformation (genetics) KW - Entrepreneurship KW - Distribution (mathematics) KW - Business KW - Economic geography KW - Complex network KW - Industrial organization KW - Computer science KW - Knowledge management KW - Geography KW - Mathematics KW - World Wide Web VL - 16 IS - 1 SP - 1583 EP - 1622 ER - TY - JOUR TI - Optimisation of Multi-Type Logistics UAV Scheduling under High Demand PY - 2024 PB - University of Zagreb, Faculty of Transport and Traffic Sciences SN - 0353-5320 T2 - PROMET - Traffic&Transportation DO - 10.7307/ptt.v36i1.261 UR - https://doi.org/10.7307/ptt.v36i1.261 DA - 2024-03-01 AU - Jiang, Hong C1 - Civil Aviation University of China AU - Wu, Tong C1 - Civil Aviation University of China AU - Ren, Xinhui C1 - Civil Aviation University of China AU - Gou, Lizhen C1 - Civil Aviation University of China LA - en KW - Computer science KW - Scheduling (production processes) KW - Operations research KW - Operations management KW - Engineering VL - 36 IS - 1 SP - 115 EP - 131 AB - At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems. ER - TY - JOUR TI - A Mission Coordinator Approach for a Fleet of UAVs in Urban Scenarios PY - 2018 PB - Elsevier BV SN - 2352-1457 T2 - Transportation research procedia DO - 10.1016/j.trpro.2018.12.018 UR - https://doi.org/10.1016/j.trpro.2018.12.018 DA - 2018-01-01 AU - Montenegro, Carlos C1 - Politecnico di Torino, Department of Electronics and Telecommunications (DET), Corso Duca degli Abruzzi 24, Turin 10129, Italy AU - Scanavino, Matteo C1 - Politecnico di Torino, Department of Mechanical and Aerospace Engineering(DIMEAS), Corso Duca degli Abruzzi 24, Turin 10129, Italy AU - Bloise, Nicoletta C1 - Politecnico di Torino, Department of Mechanical and Aerospace Engineering(DIMEAS), Corso Duca degli Abruzzi 24, Turin 10129, Italy AU - Capello, Elisa C1 - National Research Council of Italy - Institute of Electronics, Computer and Telecommunication Engineering (CNR-IEIIT), Corso Duca degli Abruzzi 24, Turin 10129, Italy C1 - Politecnico di Torino, Department of Mechanical and Aerospace Engineering(DIMEAS), Corso Duca degli Abruzzi 24, Turin 10129, Italy AU - Guglieri, Giorgio C1 - Politecnico di Torino, Department of Mechanical and Aerospace Engineering(DIMEAS), Corso Duca degli Abruzzi 24, Turin 10129, Italy AU - Rizzo, Alessandro C1 - Politecnico di Torino, Department of Electronics and Telecommunications (DET), Corso Duca degli Abruzzi 24, Turin 10129, Italy LA - en KW - Novelty KW - Key (lock) KW - Computer science KW - Population KW - Tree (set theory) KW - Transport engineering KW - Variable (mathematics) KW - Vehicle routing problem KW - Operations research KW - Engineering KW - Routing (electronic design automation) KW - Computer security KW - Computer network VL - 35 SP - 110 EP - 119 AB - The use of Unmanned Aerial Vehicles (UAVs) is now common, but although they have been for various applications, there are still a lot of challenges that need to be overcome. One key issue is related to standardizing the use of these vehicles in urban environments and guaranteeing a minimum risk level for the population. To rise to these challenges, autonomous strategies that optimize and coordinate vehicles in cooperative missions and avoid human operators should be developed. The novelty of this paper is the development of an autonomous urban mission coordinator, which is responsible for the high-level logistics of a fleet of heterogeneous vehicles. A multi-variable weighted algorithm based on a tree optimization method is also proposed. ER - TY - JOUR TI - Research on collaborative path planning of UAV swarms for urban logistics distribution in dense building environments PY - 2025 PB - Elsevier BV SN - 0957-4174 T2 - Expert Systems with Applications DO - 10.1016/j.eswa.2025.130816 UR - https://doi.org/10.1016/j.eswa.2025.130816 DA - 2025-12-22 AU - Lai, Yuanda C1 - College of Equipment Support and Management, Engineering University of PAP, A1 Wujing Rd, Xi’an 710086, China AU - Wu, Husheng C1 - College of Equipment Support and Management, Engineering University of PAP, A1 Wujing Rd, Xi’an 710086, China AU - Xia, Yuanqing C1 - School of Automation, Beijing Institute of Technology, Beijing 100811, China AU - Fu, Xiaowei C1 - School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China AU - Duan, Dongli C1 - School of Management, Northwestern Polytechnical University, Xi’an 710072, China AU - Wang, Aiai C1 - Foundation Department, Engineering University of PAP, A1 Wujing Rd, Xi’an 710086, China AU - Shi, Meimei C1 - Foundation Department, Engineering University of PAP, A1 Wujing Rd, Xi’an 710086, China LA - en KW - Motion planning KW - Swarm behaviour KW - Collision avoidance KW - Initialization KW - Resilience (materials science) KW - Population KW - Convergence (economics) KW - Path (computing) VL - 305 SP - 130816 EP - 130816 ER - TY - JOUR TI - Research on multi-functional logistics intelligent Unmanned Aerial Vehicle PY - 2022 PB - Elsevier BV SN - 0952-1976 T2 - Engineering Applications of Artificial Intelligence DO - 10.1016/j.engappai.2022.105341 UR - https://doi.org/10.1016/j.engappai.2022.105341 DA - 2022-09-22 AU - Lee, Hai-Wu C1 - School of Science and Engineering, Xiangsihu College of Guangxi University for Nationalities, 530225, No. 55, Youyi Road, Jiangnan District, Nanning City, Guangxi, P. R. China LA - en KW - Computer science KW - Quadcopter KW - Controller (irrigation) KW - Real-time computing KW - Linear-quadratic regulator KW - Process (computing) KW - Artificial intelligence KW - Control (management) KW - Aerospace engineering VL - 116 SP - 105341 EP - 105341 ER - TY - JOUR TI - Location-Route Planning for VTOL Airport and UAV Urban Logistics Network – A Case Study of Tianjin PY - 2025 PB - University of Zagreb, Faculty of Transport and Traffic Sciences SN - 0353-5320 T2 - PROMET - Traffic&Transportation DO - 10.7307/ptt.v37i2.634 UR - https://doi.org/10.7307/ptt.v37i2.634 DA - 2025-03-13 AU - Jiang, Hongwei� C1 - Civil Aviation University of China AU - WANG, Jiaxue C1 - Civil Aviation University of China AU - Ren, Xinhui C1 - Civil Aviation University of China LA - en KW - Transport engineering KW - City logistics KW - Route planning KW - Environmental planning KW - Business KW - Geography KW - Engineering VL - 37 IS - 2 SP - 456 EP - 476 AB - With the potential for fast, contactless and environmentally friendly delivery, unmanned aerial vehicles (UAVs) have gained increasing attention and application due to their cost-effectiveness and convenient and rapid delivery operations. In future cities, a multi-level airport that supports vertical take-off and landing (VTOL) of UAVs and forming a delivery network is necessary to improve delivery efficiency and provide a competitive advantage. This paper proposes a multi-level airport location-routing problem for UAVs that considers UAV flight energy consumption and operational costs. The goal is to minimise the number of locations and minimise delivery path planning while meeting delivery demands within the service range. Based on the traditional distribution centre site-path problem, the UAV distribution network is constructed to solve the problem of airport location and flight path planning, and the two-layer genetic algorithm is used to solve it. Based on this, the validity of the model and algorithm is verified using the urban area of Tianjin as an example. The experimental results show that the constructed model can be used for UAV airport layout planning, which is applicable to large-scale, multi-aircraft-type and multi-level airport layout planning. Data analysis results indicate that when the location layout of the vertical hub airport is on the edge of the VTOL points, both the flight distance and the total cost of the delivery network relatively increase. Increasing the payload capacity will reduce the number of UAV operations, but the total cost shows a decreasing-then-increasing trend. This study can provide a theoretical basis for the selection of airport sites and UAV types in future UAV urban delivery networks. ER - TY - JOUR TI - AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9060392 UR - https://doi.org/10.3390/drones9060392 DA - 2025-05-23 AU - Yang, Zheng C1 - Beijing Institute of Technology, Beijing 100081, China AU - Zhang, Yuting C1 - Beijing Institute of Technology, Beijing 100081, China AU - Zeng, Jie C1 - Beijing Institute of Technology, Beijing 100081, China AU - Yang, Yifan C1 - Beijing Institute of Technology, Beijing 100081, China AU - Jia, Yufei C1 - Beijing Institute of Technology, Beijing 100081, China AU - Song, Hua C1 - China Mobile Research Institute, Beijing 100053, China AU - Lv, Tiejun C1 - School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China AU - Sun, Qian C1 - Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China C1 - University of Chinese Academy of Sciences, Beijing 100049, China AU - An, Jianping C1 - Beijing Institute of Technology, Beijing 100081, China LA - en KW - Computer science KW - Computer security KW - Artificial intelligence VL - 9 IS - 6 SP - 392 EP - 392 AB - As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities. ER - TY - JOUR TI - Drone delivery logistics model for on-demand hyperlocal market PY - 2022 PB - Taylor & Francis SN - 1367-5567 T2 - International Journal of Logistics Research and Applications DO - 10.1080/13675567.2022.2107189 UR - https://doi.org/10.1080/13675567.2022.2107189 DA - 2022-08-05 AU - Pachayappan, Murugaiyan C1 - Operations and Supply Chain Management, Indian Institute of Management Sirmaur C1 - Operations and Supply Chain Management, Indian Institute of Management Sirmaur, Paonta Sahib, Himachal Pradesh, India AU - Sundarakani, Balan C1 - University of Wollongong in Dubai C1 - Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates LA - en KW - Drone KW - Computer science KW - Service (business) KW - Operations research KW - Heuristic KW - Integer programming KW - Business KW - Engineering KW - Marketing KW - Artificial intelligence VL - 26 IS - 12 SP - 1728 EP - 1760 ER - TY - JOUR TI - RETRACTED: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India PY - 2021 PB - Institution of Engineering and Technology SN - 2516-8398 T2 - IET Collaborative Intelligent Manufacturing DO - 10.1049/cim2.12006 UR - https://doi.org/10.1049/cim2.12006 DA - 2021-02-16 AU - Gabani, Parth C1 - Department of Mechanical Engineering K. J. Somaiya College of Engineering Mumbai India C1 - Department of Mechanical Engineering, K. J. Somaiya College of Engineering, Mumbai, India AU - Gala, Umang C1 - Department of Mechanical Engineering K. J. Somaiya College of Engineering Mumbai India C1 - Department of Mechanical Engineering, K. J. Somaiya College of Engineering, Mumbai, India AU - Narwane, Vaibhav C1 - Department of Mechanical Engineering K. J. Somaiya College of Engineering Mumbai India C1 - Department of Mechanical Engineering, K. J. Somaiya College of Engineering, Mumbai, India AU - Raut, Rakesh C1 - Department of Operations and Supply Chain Management National Institute of Industrial Engineering(NITIE) Mumbai India C1 - Department of Operations and Supply Chain Management, National Institute of Industrial Engineering(NITIE), Mumbai, India AU - Govindarajan, Usharani C1 - Sino US Global Logistics Institute, Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China C1 - Sino US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China AU - Narkhede, Balkrishna C1 - Department of Operations and Supply Chain Management National Institute of Industrial Engineering (NITIE) Mumbai India C1 - Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India LA - en KW - Drone KW - Truck KW - Last mile (transportation) KW - Transport engineering KW - Supply chain KW - Business KW - Computer science KW - City logistics KW - Mile KW - Engineering KW - Geography KW - Marketing KW - Automotive engineering VL - 3 IS - 3 SP - 262 EP - 272 AB - Abstract Emerging Unmanned Aerial Vehicle (UAV) technologies have been under spotlight worldwide due to a promising spectrum of applications. Despite interesting use‐cases receiving worldwide media attention, drones are expected to have their most significant impact on supply chain and logistics systems in highly populated urban cities once the regulations for commercial purposes are lifted. This article proposes two models of drone‐delivery technology that can serve as a potential replacement in the Indian truck‐delivery system. First, a drone–truck hybrid delivery model is proposed with trucks carrying multiple drones. Second, a stand‐alone drone‐delivery model is proposed with separate charging infrastructural facilities at specific points. The two proposed models' findings present the advantages of significantly reducing carbon emissions, delivery costs, and time compared to truck‐based delivery. The hybrid drone–truck model eliminates the need for separate charging facilities saving initial infrastructural costs. In contrast, the stand‐alone drone model utilizes the current warehouse location facilities with charging stations on specific points for ease of adoption. This research presents initial viability suggesting that the drone technology can solve the current operational challenges in India's urban cities. Furthermore, vital factors are discussed to enable stakeholders to select a model for drone logistics systems planning and experimentations. ER - TY - JOUR TI - Multistrategy Improved Particle Swarm Optimization Algorithm for Path Planning of UAV in 3-D Low Altitude Urban Environment PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2327-4662 T2 - IEEE Internet of Things Journal DO - 10.1109/jiot.2025.3589644 UR - https://doi.org/10.1109/jiot.2025.3589644 DA - 2025-07-16 AU - Tao, Fazhan C1 - College of Information Engineering, Henan University of Science and Technology, Luoyang, China AU - Chen, Zezheng C1 - College of Information Engineering, Henan University of Science and Technology, Luoyang, China AU - Wang, Zhikai C1 - College of Information Engineering, Henan University of Science and Technology, Luoyang, China AU - Zhu, Longlong C1 - College of Information Engineering, Henan University of Science and Technology, Luoyang, China AU - Wang, Jun C1 - College of Information Engineering, Henan University of Science and Technology, Luoyang, China LA - en KW - Particle swarm optimization KW - Motion planning KW - Computer science KW - Low altitude KW - Algorithm KW - Altitude (triangle) KW - Path (computing) KW - Mathematical optimization KW - Real-time computing KW - Artificial intelligence KW - Mathematics KW - Computer network KW - Robot VL - 12 IS - 19 SP - 40470 EP - 40483 ER - TY - JOUR TI - A bi-level planning approach of logistics unmanned aerial vehicle route network PY - 2023 PB - Elsevier BV SN - 1270-9638 T2 - Aerospace Science and Technology DO - 10.1016/j.ast.2023.108572 UR - https://doi.org/10.1016/j.ast.2023.108572 DA - 2023-08-11 AU - Li, Shan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing 211106, China AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing 211106, China AU - Jia, Yi C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China C1 - National Key Laboratory of Air Traffic Flow Management, Nanjing 211106, China AU - Liu, Hao C1 - College of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China LA - en KW - Simulated annealing KW - Computer science KW - Transport engineering KW - Network planning and design KW - Flow network KW - Simulation KW - Real-time computing KW - Operations research KW - Computer network KW - Engineering KW - Mathematical optimization VL - 141 SP - 108572 EP - 108572 ER - TY - JOUR TI - A multi-objective benchmark for UAV path planning with baseline results PY - 2025 PB - Elsevier BV SN - 2210-6502 T2 - Swarm and Evolutionary Computation DO - 10.1016/j.swevo.2025.101968 UR - https://doi.org/10.1016/j.swevo.2025.101968 DA - 2025-05-18 AU - Darlan, Daison C1 - Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, Daegu, 41566, South Korea AU - Ajani, Oladayo C1 - Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, Daegu, 41566, South Korea AU - Paul, Anand C1 - Biostatistics and Data Science Department, Louisiana State University, Health Sciences Center, New Orleans, 70112, LA, USA AU - Mallipeddi, Rammohan C1 - Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, Daegu, 41566, South Korea LA - en KW - Computer science KW - Baseline (sea) KW - Benchmark (surveying) KW - Motion planning KW - Path (computing) KW - Artificial intelligence KW - Real-time computing KW - Robot KW - Computer network VL - 96 SP - 101968 EP - 101968 ER - TY - JOUR TI - Coalition-based facility location optimization for urban UAV logistics PY - 2026 PB - Elsevier BV SN - 0968-090X T2 - Transportation Research Part C Emerging Technologies DO - 10.1016/j.trc.2026.105624 UR - https://doi.org/10.1016/j.trc.2026.105624 DA - 2026-03-15 AU - Liu, Lianyu C1 - The Research Institute for Risk Governance and Emergency Decision-Making, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China AU - GONG, ZAIWU C1 - The Research Institute for Risk Governance and Emergency Decision-Making, School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China LA - en KW - Facility location problem KW - Trajectory KW - Aviation KW - Mathematical model KW - Intelligent transportation system VL - 186 SP - 105624 EP - 105624 ER - TY - JOUR TI - Research on logistics of intelligent unmanned aerial vehicle integration system PY - 2023 PB - Elsevier BV SN - 2452-414X T2 - Journal of Industrial Information Integration DO - 10.1016/j.jii.2023.100534 UR - https://doi.org/10.1016/j.jii.2023.100534 DA - 2023-10-25 AU - Lee, Hai-Wu C1 - Department of Public Affairs, Fo Guang University. No.160, Linwei Road, Jiaosi, Yilan County, 262307, Taiwan C1 - School of Science and Engineering, Xiangsihu College of Guangxi University for Nationalities, No.55, Youyi Road, Jiangnan District, Nanning City, Guangxi, 530225, China AU - Lee, Chi-Shiuan C1 - Department of Public Affairs, Fo Guang University. No.160, Linwei Road, Jiaosi, Yilan County, 262307, Taiwan LA - en KW - Quadcopter KW - Real-time computing KW - Controller (irrigation) KW - Computer science KW - Process (computing) KW - Engineering KW - Control engineering KW - Aerospace engineering VL - 36 SP - 100534 EP - 100534 ER - TY - JOUR TI - Optimising Drone-Assisted Logistics for Urban Last-Mile Delivery: An Overview of Applications, Methodologies, and Emerging Trends PY - 2024 SN - 2957-9589 T2 - Journal of Urban Development and Management DO - 10.56578/judm030304 UR - https://doi.org/10.56578/judm030304 DA - 2024-09-10 AU - Pratelli, Antonio C1 - Department of Civil and Industrial Engineering , University of Pisa , 56122 Pisa , Italy C1 - POLOG -Logistics Systems University Centre , University of Pisa , 57128 Livorno , Italy AU - Brocchini, Lorenzo C1 - Department of Civil and Industrial Engineering , University of Pisa , 56122 Pisa , Italy C1 - POLOG -Logistics Systems University Centre , University of Pisa , 57128 Livorno , Italy AU - Petri, Massimiliano C1 - Department of Civil and Industrial Engineering , University of Pisa , 56122 Pisa , Italy C1 - POLOG -Logistics Systems University Centre , University of Pisa , 57128 Livorno , Italy LA - en KW - Drone KW - Last mile (transportation) KW - Mile KW - City logistics KW - Aeronautics KW - Engineering KW - Computer science KW - Transport engineering KW - Geography VL - 3 IS - 3 SP - 188 EP - 197 AB - The rapid evolution of drone-assisted logistics for urban last-mile (ULM) delivery has garnered significant interest from both academia and industry. This article presents a comprehensive review of the state-of-the-art research and practical implementations of ULM systems, focusing on the use of unmanned aerial vehicles (UAVs) for the final stage of goods and parcel delivery in urban environments. The applicability of UAV-based logistics across various contexts, including urban and rural areas, is examined, with real-world case studies highlighted to demonstrate practical uses. Key methodologies and models employed in optimising UAV routing and operations are discussed, particularly those that enhance the efficiency and reliability of ULM. The critical advantages and limitations of drone-assisted last-mile logistics are analysed, providing insights into the operational, regulatory, and technological challenges. The discussion is further expanded by addressing emerging trends in UAV technology, as well as innovations in drone deployment strategies and the evolving regulatory landscape. In conclusion, potential theoretical advancements and future applications of ULM systems are outlined, with an emphasis on integrating drones into broader logistics networks and smart city frameworks. The insights offered aim to guide future research and practical developments in this rapidly advancing field. ER - TY - JOUR TI - Aircraft-Cargo Separation Rate-Driven Ground Risk–Cost Optimization for Urban Logistics UAVs PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2025.3603185 UR - https://doi.org/10.1109/access.2025.3603185 DA - 2025-01-01 AU - Yang, Tao C1 - College of Traffic and Transportation, Nanning University, Nanning, China AU - Zheng, Baoli C1 - School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, China AU - Mo, Mingli C1 - School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, China AU - Shang, Jie C1 - College of Traffic and Transportation, Nanning University, Nanning, China AU - Dan, Huang C1 - Guangxi Bozhi Information Technology Company Ltd., Nanning, China C1 - Guangxi Bozhi Information Technology Co., Ltd, Nanning, China LA - en KW - Separation (statistics) KW - Computer science KW - Environmental science KW - Aerospace engineering KW - Aeronautics KW - Engineering VL - 13 SP - 154568 EP - 154580 AB - The surge in demand for urban logistics has positioned unmanned aerial vehicles (UAVs) as a vital solution for improving last mile delivery efficiency in complex urban environments. To overcome the ground safety bottleneck in the large-scale application of logistics UAVs, this paper establishes a full-chain framework of “risk characteristics–quantitative model–risk cost” that proposes an innovative dual-state risk–cost optimization model based on aircraft-cargo separation probability. The study quantifies the separation probability using the Weibull distribution, corrects the risk value assessment deviation under the traditional single fall assumption, and establishes a risk probability model for the separation state by combining the dynamic characteristic differences between the UAV body and cargo, enabling the generation of rasterized risk maps by integrating spatial parameters, including population density and shelter coefficients, to implement risk–cost conversion and support airspace management. Case verification reveals that in areas with different land uses, there are significant differences in the ground risk values and risk costs of logistics UAVs. Additionally, introducing the aircraft-cargo separation rate improves risk assessment accuracy by 57%, and the ground risk in the aircraft-cargo integrated state is 4.31 times higher than in the separated state. This study provides core theoretical support for the safe operation and route decision-making involving logistics UAVs in complex urban scenarios. ER - TY - JOUR TI - UAV–Ground Vehicle Collaborative Delivery in Emergency Response: A Review of Key Technologies and Future Trends PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app15179803 UR - https://doi.org/10.3390/app15179803 DA - 2025-09-06 AU - Wang, Yizhe C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao’an Road, Shanghai 201800, China C1 - The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao'an Road, Shanghai 201800, China AU - Li, Jie C1 - Nanjing Tianyu Technology Co., Ltd., 518 Digital Avenue, Yuhuatai District, Nanjing 210012, China AU - Yang, Xiaoguang C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao’an Road, Shanghai 201800, China C1 - The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao'an Road, Shanghai 201800, China AU - Peng, Qing C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao’an Road, Shanghai 201800, China C1 - The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China C1 - Intelligent Transportation System Research Center, Tongji University, 4801 Cao'an Road, Shanghai 201800, China LA - en KW - Emergency management KW - Standardization KW - Safeguarding KW - Emergency response KW - Decision support system KW - Intelligent transportation system KW - Open research KW - Key (lock) VL - 15 IS - 17 SP - 9803 EP - 9803 AB - UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. ER - TY - JOUR TI - Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 2220-9964 T2 - ISPRS International Journal of Geo-Information DO - 10.3390/ijgi11080419 UR - https://doi.org/10.3390/ijgi11080419 DA - 2022-07-23 AU - He, Hongbo C1 - College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China C1 - Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China AU - Ye, Huping C1 - Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China C1 - The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China AU - Xu, Chenchen C1 - Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China C1 - The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China AU - Liao, Xiaohan C1 - College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China C1 - Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China C1 - The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China LA - en KW - Transport engineering KW - Computer science KW - Engineering VL - 11 IS - 8 SP - 419 EP - 419 AB - Urban logistics is an important research topic in human and economic geography; unmanned aerial vehicles (UAVs) are an emerging technology that has huge potential in the field of logistics with the release of control restrictions on low-altitude airspace. The scientific identification of the spatial pattern and impact factors of UAV logistics networks is greatly significant in regards to UAV logistics planning and scheduling. This study considered the urban logistics network of Hangzhou in 2020 as the research topic and used kernel density estimation, a geodetector, and geographic information system (GIS) spatial analysis technology to systematically analyze the spatial patterns and influencing factors at the city and district scales. The study found that a significant spatial pattern was revealed in the UAV logistics network in Hangzhou, and the logistics nodes showed an obvious “core-edge” structure. The urban population, market scale and logistics infrastructure jointly shaped the structure and function of the UAV logistics network, and logistics nodes had a strong coupling relationship with the urban spatial structure. Through interaction detectors, the technical route of urban UAV logistics network construction was analyzed and summarized, and results can provide a scientific basis and case reference for other cities to build and plan UAV logistics networks. ER - TY - JOUR TI - Urban On-Demand Delivery via Autonomous Aerial Mobility: Formulation and Exact Algorithm PY - 2022 PB - Institute of Electrical and Electronics Engineers SN - 1545-5955 T2 - IEEE Transactions on Automation Science and Engineering DO - 10.1109/tase.2022.3184324 UR - https://doi.org/10.1109/tase.2022.3184324 DA - 2022-06-23 AU - Pei, Zhi C1 - College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China AU - Fang, Tao C1 - College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China AU - Weng, Kebiao C1 - College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China AU - Yi, Wenchao C1 - College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China LA - en KW - Computer science KW - Schedule KW - Drone KW - Heuristic KW - Integer programming KW - Mathematical optimization KW - Greedy algorithm KW - Real-time computing KW - Algorithm KW - Operations research KW - Simulation KW - Engineering KW - Mathematics KW - Artificial intelligence VL - 20 IS - 3 SP - 1675 EP - 1689 ER - TY - JOUR TI - Parametric Modeling and Evaluation of Departure and Arrival Air Routes for Urban Logistics UAVs PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9070454 UR - https://doi.org/10.3390/drones9070454 DA - 2025-06-23 AU - Li, Zhong‐Ming C1 - School of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China AU - Zhao, Yifei C1 - School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Ren, Xinhui C1 - School of Economics and Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Parametric statistics KW - Arrival time KW - Parametric model KW - Computer science KW - Transport engineering KW - Aeronautics KW - Environmental science KW - Geography KW - Engineering KW - Statistics KW - Mathematics VL - 9 IS - 7 SP - 454 EP - 454 AB - With the rapid development of the low-altitude economy, the intensive take-offs and landings of Unmanned Aerial Vehicles (UAVs) performing logistics transport tasks in urban areas have introduced significant safety risks. To reduce the likelihood of collisions, logistics operators—such as Meituan, Antwork, and Fengyi—have established fixed departure and arrival air routes above vertiports and designed fixed flight air routes between vertiports to guide UAVs to fly along predefined paths. In the complex and constrained low-altitude urban environment, the design of safe and efficient air routes has undoubtedly become a key enabler for successful operations. This research, grounded in both current theoretical research and real-world logistics UAV operations, defines the concept of UAV logistics air routes and presents a comprehensive description of their structure. A parametric model for one-way round-trip logistics air routes is proposed, along with an air route evaluation model and optimization method. Based on this framework, the research identifies four basic configurations that are commonly adopted for one-way round-trip operations. These configurations can be further improved into two optimized configurations with more balanced performance across multiple metrics. Simulation results reveal that Configuration 1 is only suitable for small-scale transport; as the number of delivery tasks increases, delays grow linearly. When the task volume exceeds 100 operations per 30 min, Configurations 2, 3, and 4 reduce average delay by 88.9%, 89.2%, and 93.3%, respectively, compared with Configuration 1. The research also finds that flight speed along segments and the cruise segment capacity have the most significant influence on delays. Properly increasing these two parameters can lead to a 28.4% reduction in the average delay. The two optimized configurations, derived through further refinement, show better trade-offs between average delay and flight time than any of the fundamental configurations. This research not only provides practical guidance for the planning and design of UAV logistics air routes but also lays a methodological foundation for future developments in UAV scheduling and air route network design. ER - TY - JOUR TI - Model of Third-Party Risk Index for Unmanned Aerial Vehicle Delivery in Urban Environment PY - 2020 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su12208318 UR - https://doi.org/10.3390/su12208318 DA - 2020-10-09 AU - Ren, Xinhui C1 - College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China AU - Cheng, Caixia C1 - College of Economics and Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Drone KW - Civil aviation KW - Index (typography) KW - Transport engineering KW - Computer science KW - Aeronautics KW - Aviation KW - Engineering KW - Aerospace engineering VL - 12 IS - 20 SP - 8318 EP - 8318 AB - In order to assess the airspace risk of unmanned aerial vehicles (UAVs) operating at low altitudes, the third-party risks of UAV urban operations were defined: the risks caused by drones to people on the ground who are not involved in operations and do not profit from operations, and the sources and objects of the risk were clarified. Taking into account the drone crash, noise, on-board camera, and ground environment factors, a UAV urban logistics risk index evaluation model was constructed. First, the UAV image regression model was used to construct a three-dimensional grid, and then a comprehensive third-party risk index model of UAV urban logistics was built based on the casualty and noise risks. Finally, the Southern District of Civil Aviation University of China was selected as an example scene, and surface data were obtained through a field investigation and instrument measurements. Then, the risk of drone operations in the airspace 30–60 m above this area was evaluated. The results showed that the third-party risk was lower when the UAV flying altitude above a building was greater. However, in other areas such as lakes, woods, roads, open spaces, a lower flight altitude had a lower risk. A comparison of the whole airspace showed that the third-party risk was the lowest when the drone operated at an altitude of 30 m. The results also showed that the third-party risk above the lake and greenery was the lowest when on the same plane, followed by the lower risk above the buildings and open squares, with the highest third-party risk above the canteen passage. ER - TY - JOUR TI - Advancing low-carbon smart cities: Leveraging UAVs-enabled low-altitude economy principles and innovations PY - 2025 PB - Elsevier BV SN - 1364-0321 T2 - Renewable and Sustainable Energy Reviews DO - 10.1016/j.rser.2025.115942 UR - https://doi.org/10.1016/j.rser.2025.115942 DA - 2025-06-10 AU - Tan, Hongjun C1 - Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China C1 - Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, 315200, China AU - Guo, Zhiling C1 - Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China AU - Yan, Jinyue C1 - Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China AU - Zhang, Dongxiao C1 - Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, 315200, China C1 - School of Urban Planning and Design, Peking University, 2199 Lishui Rd, Nanshan District, Shenzhen, China AU - Zhang, Dongxiao C1 - Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, 315200, China C1 - School of Urban Planning and Design, Peking University, 2199 Lishui Rd, Nanshan District, Shenzhen, China AU - Chen, Yuntian C1 - Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, 315200, China C1 - Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang 315200, P. R. China AU - Zhang, Haoran C1 - School of Urban Planning and Design, Peking University, 2199 Lishui Rd, Nanshan District, Shenzhen, China LA - en KW - Low-carbon economy KW - Low altitude KW - Carbon fibers KW - Business KW - Environmental economics KW - Climate change KW - Altitude (triangle) KW - Computer science KW - Economics VL - 222 SP - 115942 EP - 115942 ER - TY - JOUR TI - An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling PY - 2021 PB - Institution of Engineering and Technology SN - 1751-8628 T2 - IET Communications DO - 10.1049/cmu2.12106 UR - https://doi.org/10.1049/cmu2.12106 DA - 2021-02-01 AU - Yuan, Xiaoxiang C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China C1 - School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China AU - Zhu, Jie C1 - School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China AU - Li, Yixuan C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China C1 - School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China AU - Huang, Haiping C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China C1 - School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China AU - Wu, Min C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China C1 - School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China C1 - School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China LA - en KW - Computer science KW - Scheduling (production processes) KW - Vehicle routing problem KW - Job shop scheduling KW - Mathematical optimization KW - Genetic algorithm KW - Population KW - Real-time computing KW - Routing (electronic design automation) KW - Mathematics KW - Computer network KW - Machine learning VL - 15 IS - 10 SP - 1402 EP - 1411 AB - Abstract This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non‐deterministic Polynomial (NP)‐hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic‐based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight‐based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently. ER - TY - JOUR TI - Quantitative and Comparative Analysis of Energy Consumption in Urban Logistics Using Unmanned Aerial Vehicles and Selected Means of Transport PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 1996-1073 T2 - Energies DO - 10.3390/en16186467 UR - https://doi.org/10.3390/en16186467 DA - 2023-09-07 AU - Kwasiborska, Anna C1 - Faculty of Transport, Warsaw University of Technology, Koszykowa Street 75, 00-662 Warsaw, Poland AU - Stelmach, Anna C1 - Faculty of Transport, Warsaw University of Technology, Koszykowa Street 75, 00-662 Warsaw, Poland AU - Jabłońska, Izabela C1 - Łukasiewicz Research Network, Poleczki Street 19, 00-822 Warsaw, Poland LA - en KW - Energy consumption KW - Drone KW - Sustainable transport KW - Transport engineering KW - Consumption (sociology) KW - Environmental economics KW - City logistics KW - Energy (signal processing) KW - Sustainable development KW - Electric vehicle KW - Automotive engineering KW - Computer science KW - Engineering KW - Sustainability KW - Economics VL - 16 IS - 18 SP - 6467 EP - 6467 AB - Cities are moving towards sustainable development, which consists of tasks and challenges to improve the quality of life, and minimize energy consumption. The concept of sustainable mobility includes the choice of means of transport other than the car for all journeys, especially short distances, and for the delivery of goods. Due to the growing populations of cities, lack of free space, and high costs of building infrastructure for traditional means of transport, cities are looking for modern solutions that allow for the cheap, fast, and green transportation of people and goods. Urban air mobility is the answer to these problems, and uses eVTOL (electric vertical take-off and landing) aircraft and unmanned aerial vehicle systems (UAVs). The article’s main purpose is to present an energy efficiency analysis using UAVs and electric scooters in the transport of takeaway food, which is a solution that fits into the zero-emission transport policy. The article presents the following research problem: which type of electric transport (scooters/UAVs) shows a lower demand for electric energy when delivering food from restaurants to individual customers? The analysis method was applied using the D’Andrea, Dorling, Figliozzi, Kirchstein, and Tseng energy models. The completed calculations were used to perform a comparative analysis of energy consumption for three adopted scenarios related to energy consumption by drones. ER - TY - JOUR TI - Hybrid metaheuristic-driven 3D path planning for UAVs in complex urban environments: a multi-objective fusion framework PY - 2025 PB - Taylor & Francis SN - 1010-6049 T2 - Geocarto International DO - 10.1080/10106049.2025.2538099 UR - https://doi.org/10.1080/10106049.2025.2538099 DA - 2025-07-31 AU - Cheng, Qing C1 - School of Air Traffic Management, Civil Aviation Flight University of China C1 - School of Air Traffic Management, Civil Aviation Flight University of China, Chengdu, China AU - Zhang, Zhengyuan C1 - School of Air Traffic Management, Civil Aviation Flight University of China C1 - School of Air Traffic Management, Civil Aviation Flight University of China, Chengdu, China AU - Du, Yunfei C1 - School of Electronic Information Engineering, Beihang University C1 - School of Electronic Information Engineering, Beihang University, Beijing, China AU - Zhao, Xi C1 - School of Air Traffic Management, Civil Aviation Flight University of China C1 - School of Air Traffic Management, Civil Aviation Flight University of China, Chengdu, China LA - en KW - Motion planning KW - Metaheuristic KW - Path (computing) KW - Computer science KW - Fusion KW - Urban planning KW - Geography KW - Artificial intelligence KW - Engineering KW - Civil engineering KW - Robot KW - Computer network VL - 40 IS - 1 AB - Unmanned Aerial Vehicles (UAVs) offer transformative potential for urban logistics, but their deployment is challenged by complex 3D path planning in constrained environments. This study introduces GS-PSO, a novel multi-stage hybrid framework designed to address this challenge by sequentially integrating Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and a Genetic Algorithm (GA). Key innovations include dynamic chaotic inertia weights in PSO for enhanced exploration and specialized GA operators for fine-grained refinement. The efficacy of GS-PSO was validated on the CEC2020 benchmarks, where it found the global optimum in 8/10 functions, showing superior accuracy and stability over standard PSO. In high-fidelity UAV simulations, GS-PSO generated paths up to 22.9% shorter than the AVOA, significantly outperforming competitors. Statistical tests (p < 0.05) confirmed its superior solution quality and robustness. This work provides an effective, reliable solution for complex UAV trajectory planning. ER - TY - JOUR TI - Optimized Collaborative Routing for UAVs and Ground Vehicles in Integrated Logistics Systems PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9080538 UR - https://doi.org/10.3390/drones9080538 DA - 2025-07-30 AU - Nazir, Hafiz C1 - School of Management, Guangzhou University, Guangzhou 510006, China AU - Sun, Yanming C1 - School of Management, Guangzhou University, Guangzhou 510006, China AU - Hu, Yongjun C1 - School of Management, Guangzhou University, Guangzhou 510006, China LA - en KW - Vehicle routing problem KW - Routing (electronic design automation) KW - Ground transportation KW - Transport engineering KW - Computer science KW - Engineering KW - Embedded system VL - 9 IS - 8 SP - 538 EP - 538 AB - This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. ER - TY - JOUR TI - UAV Logistics and Distribution Path Planning in Urban Areas Based on Improved PSO and A* Algorithms PY - 2025 PB - Institute of Electronics Engineers of Korea SN - 2287-5255 T2 - IEIE Transactions on Smart Processing and Computing DO - 10.5573/ieiespc.2025.14.2.257 UR - https://doi.org/10.5573/ieiespc.2025.14.2.257 DA - 2025-04-30 AU - Li, Lei AU - Peng, Huimin AU - Ren, Xingxue AU - Wang, Qianqian LA - en KW - Computer science KW - Path (computing) KW - Motion planning KW - Algorithm KW - Distribution (mathematics) KW - Artificial intelligence KW - Operations research KW - Mathematics KW - Computer network VL - 14 IS - 2 SP - 257 EP - 267 ER - TY - JOUR TI - Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context PY - 2023 PB - Elsevier BV SN - 2213-1388 T2 - Sustainable Energy Technologies and Assessments DO - 10.1016/j.seta.2023.103202 UR - https://doi.org/10.1016/j.seta.2023.103202 DA - 2023-04-07 AU - Chan, Y.Y. C1 - Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region AU - Ng, Kam C1 - Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region AU - Lee, C.K.M. C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region AU - Hsu, Li‐Ta C1 - Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region AU - Keung, K. C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region LA - en KW - Voronoi diagram KW - Context (archaeology) KW - Energy consumption KW - Computer science KW - Dijkstra's algorithm KW - Particle swarm optimization KW - Shortest path problem KW - Path (computing) KW - Mathematical optimization KW - Real-time computing KW - Engineering KW - Algorithm KW - Mathematics KW - Geography VL - 57 SP - 103202 EP - 103202 ER - TY - JOUR TI - BSBL-Based Auxiliary Vehicle Position Analysis in Smart City Using Distributed MEC and UAV-Deployed IoT PY - 2022 PB - Institute of Electrical and Electronics Engineers SN - 2327-4662 T2 - IEEE Internet of Things Journal DO - 10.1109/jiot.2022.3204986 UR - https://doi.org/10.1109/jiot.2022.3204986 DA - 2022-09-08 AU - Wang, Huafei C1 - School of Information and Communication Engineering and the State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, China AU - Wang, Xianpeng C1 - School of Information and Communication Engineering and the State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, China AU - Lan, Xiang C1 - School of Information and Communication Engineering and the State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, China AU - Su, Ting C1 - School of Information and Communication Engineering and the State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University, Haikou, China AU - Wan, Liangtian C1 - Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, China LA - en KW - Computer science KW - Block (permutation group theory) KW - Smart city KW - Position (finance) KW - Real-time computing KW - Artificial neural network KW - Sensor fusion KW - Wireless sensor network KW - Internet of Things KW - Artificial intelligence KW - Computer network KW - Embedded system VL - 10 IS - 2 SP - 975 EP - 986 ER - TY - GEN TI - Research on logistics UAV path planning PY - 2023 DO - 10.1117/12.2684766 UR - https://doi.org/10.1117/12.2684766 DA - 2023-06-26 AU - shen, Yujie C1 - Hubei Univ. of Arts and Science (China) AU - Jin, Liying C1 - AVIC Aerospace Life-Support Industries, Ltd C1 - Hubei Univ. of Arts and Science (China) AU - Hu, Kelian C1 - Hubei Xiangyi New Material Technology Co, Ltd. (China) AU - Hu, Xindong C1 - Hubei Univ. of Arts and Science (China) AU - Xue, Chaohang C1 - Hubei Univ. of Arts and Science (China) LA - en KW - Drone KW - Computer science KW - Path (computing) KW - Motion planning KW - Customer satisfaction KW - Task (project management) KW - Operations research KW - Route planning KW - Mathematical optimization KW - Real-time computing KW - Artificial intelligence KW - Systems engineering KW - Engineering KW - Mathematics KW - Business KW - Computer network KW - Marketing SP - 83 EP - 83 ER - TY - GEN TI - Multi-objective Scheduling of Logistics UAVs Based on Simulated Annealing PY - 2020 PB - Springer Science+Business Media SN - 1865-0929 T2 - Communications in computer and information science DO - 10.1007/978-981-15-2767-8_26 UR - https://doi.org/10.1007/978-981-15-2767-8_26 DA - 2020-01-01 AU - Li, Yixuan C1 - Nanjing University of Posts and Telecommunications, Nanjing, 210003, China AU - Yuan, Xiaoxiang C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, 210003, China C1 - Nanjing University of Posts and Telecommunications, Nanjing, 210003, China AU - Zhu, Jie C1 - Nanjing University of Posts and Telecommunications, Nanjing, 210003, China AU - Huang, Haiping C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, 210003, China C1 - Nanjing University of Posts and Telecommunications, Nanjing, 210003, China AU - Wu, Min C1 - Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, 210003, China C1 - Nanjing University of Posts and Telecommunications, Nanjing, 210003, China LA - en KW - Simulated annealing KW - Computer science KW - Scheduling (production processes) KW - Operations research KW - Mathematical optimization KW - Algorithm KW - Engineering KW - Mathematics SP - 287 EP - 298 ER - TY - JOUR TI - Reinforcement-Learning-Driven Integrated Detection and Mitigation of UAV GPS Spoofing Attacks PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2327-4662 T2 - IEEE Internet of Things Journal DO - 10.1109/jiot.2025.3579307 UR - https://doi.org/10.1109/jiot.2025.3579307 DA - 2025-06-12 AU - Hu, Jueming C1 - Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND, USA AU - Ammar, Mohammad C1 - Khoury College of Computer Science, Northeastern University, Boston, MA, USA C1 - Department of Computer Engineering, Aligarh Muslim University, Aligarh, India AU - Hussain, Bilal C1 - Clean and Resilient Energy System Laboratory, Texas A&M University at Galveston, Galveston, TX, USA C1 - Clean and Resilient Energy System Lab, Texas A and M University, Galveston, TX, USA AU - Kim, Jae-Won C1 - Global Cyber Research Institute, Texas A&M University at College Station, College Station, TX, USA C1 - Global Cyber Research Institute, Texas A and M University, College Station, TX, USA AU - Khan, Irfan C1 - Clean and Resilient Energy System Laboratory, Texas A&M University at Galveston, Galveston, TX, USA C1 - Clean and Resilient Energy System Lab, Texas A and M University, Galveston, TX, USA LA - en KW - Computer science KW - Reinforcement learning KW - Spoofing attack KW - Global Positioning System KW - Artificial intelligence KW - Computer security KW - Real-time computing KW - Telecommunications VL - 12 IS - 18 SP - 36926 EP - 36941 ER - TY - JOUR TI - A Study on the Determination Methods of Monitoring Point for Inundation Damage in Urban Area Using UAV and Hydrological Modeling PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 2073-4441 T2 - Water DO - 10.3390/w14071117 UR - https://doi.org/10.3390/w14071117 DA - 2022-03-31 AU - Song, Youngseok C1 - Department of Civil Engineering and Landscape Architectural, Daegu Technical University, Daegu 42734, Korea AU - Lee, Hyeongjun C1 - Department of Aeronautics and Civil Engineering, Hanseo University, Seosan 31962, Korea AU - Kang, Dong-Ho C1 - Department of Urban and Environmental and Disaster Management, Graduated School of Prevention, Kangwon National University, Chuncheon-si 25913, Korea AU - Kim, Byung C1 - Department of Urban and Environmental and Disaster Management, Graduated School of Prevention, Kangwon National University, Chuncheon-si 25913, Korea AU - Park, Moojong C1 - Department of Aeronautics and Civil Engineering, Hanseo University, Seosan 31962, Korea LA - en KW - Rainwater harvesting KW - Watershed KW - Environmental science KW - Inflow KW - Flood myth KW - Hydrology (agriculture) KW - Remote sensing KW - Flood control KW - Urban area KW - Computer science KW - Meteorology KW - Geography KW - Geology KW - Geotechnical engineering VL - 14 IS - 7 SP - 1117 EP - 1117 AB - Recently, unmanned aerial vehicles (UAVs) have been used in various fields, such as military, logistics, transportation, construction, and agriculture, making it possible to apply the limited activities of humans to various and wide ranges. In addition, UAVs have been utilized to construct topographic data that are more precise than existing satellite images or cadastral maps. In this study, a monitoring point for preventing flood damage in an urban area was selected using a UAV. In addition, the topographic data were constructed using a UAV, and the flow of rainwater was examined using the watershed analysis in an urban area. An orthomosaic, a digital surface model (DSM), and a three-dimensional (3D) model were constructed for the topographic data, and a precision of 0.051 m based on the root mean square error (RMSE) was achieved through the observation of ground control points (GCPs). On the other hand, for the watershed analysis in the urban area, the point in which the flow of rainwater converged was analyzed by adjusting the thresholds. A monitoring point for preventing flood damage was proposed by examining the topographic characteristics of the target area related to the inflow of rainwater. ER - TY - JOUR TI - Improved Utaut2 Model for Uav Urban Logistics Delivery Acceptance PY - 2023 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.4598462 UR - https://doi.org/10.2139/ssrn.4598462 DA - 2023-01-01 AU - Ren, Xinhui C1 - Civil Aviation University of China C1 - Civil Aviation University of China, Tianjin, China AU - Yu, Fang C1 - Civil Aviation University of China C1 - Civil Aviation University of China, Tianjin, China AU - Jiang, Hong C1 - Civil Aviation University of China C1 - Civil Aviation University of China, Tianjin, China LA - en KW - Business KW - Process management KW - Computer science ER - TY - GEN TI - Optimization of Urban Logistics Obstacle Avoidance UAV Path Based on Improved Crowned Porcupine Pig Algorithm PY - 2024 DO - 10.1109/iccasit62299.2024.10828038 UR - https://doi.org/10.1109/iccasit62299.2024.10828038 DA - 2024-10-23 AU - Jiang, Shengyu C1 - Taiyuan University of Science and Technology,Taiyuan,China AU - Wang, Baotong C1 - North China University of Science and Technology,Tangshan,China LA - en KW - Porcupine KW - Computer science KW - Obstacle KW - Obstacle avoidance KW - Path (computing) KW - Collision avoidance KW - Optimization algorithm KW - Real-time computing KW - Algorithm KW - Artificial intelligence KW - Mathematical optimization KW - Geography KW - Computer network KW - Mobile robot KW - Geology KW - Collision KW - Mathematics KW - Computer security KW - Robot KW - Archaeology SP - 919 EP - 924 ER - TY - GEN TI - UAV and Satellite Employment for the Internet of Things Use Case PY - 2020 DO - 10.1109/aero47225.2020.9172701 UR - https://doi.org/10.1109/aero47225.2020.9172701 DA - 2020-03-01 AU - Marchese, Mario C1 - Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy AU - Moheddine, Aya C1 - Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Genoa, Italy AU - Patrone, Fabio C1 - Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy LA - en KW - Computer science KW - Default gateway KW - Software deployment KW - Gateway (web page) KW - Communications protocol KW - Communications satellite KW - Telecommunications KW - Protocol (science) KW - Computer network KW - The Internet KW - Satellite KW - World Wide Web KW - Engineering SP - 1 EP - 8 ER - TY - JOUR TI - PUBLIC SUPPORT AND PROSPECTS OF DRONES OR UNMANNED AERIAL VEHICLES (UAVs) TECHNOLOGIES FOR EFFECTIVE TRANSPORT AND LOGISTICS DELIVERY IN NIGERIA PY - 2021 SN - 2488-9849 T2 - International Journal of Advanced Academic Research DO - 10.46654/ij.24889849.e7126 UR - https://doi.org/10.46654/ij.24889849.e7126 DA - 2021-02-13 AU - Abiodun, Temitope LA - en KW - Drone KW - Business KW - Urbanization KW - Public transport KW - Sustainability KW - Pace KW - Environmental planning KW - Transport engineering KW - Economic growth KW - Engineering KW - Economics SP - 43 EP - 63 AB - The study focuses on the issue that prompt transportation and delivery of logistics from one location to the other in Nigeria is at present costly, difficult, unrealistic, and cumbersome as occasioned by; road congestion in urban cities, increased, insecurity pollution, unnecessary delays, and declined efficiency, among others. The menace often poses threats to city planners to maintain the pace of the ever increasing urbanization process and population growth in all ramifications. The current situation has, however, warranted possibility for drones or unmanned aerial vehicles (UAVs) technology for logistics delivery and transportation in Nigeria and other sub-Saharan African nations, to alleviate the stress posed by the conventional mode of transportation. This paper is poised to: investigate the public support and prospects of using of drones or UAVs for effective transport or logistics delivery, and also find out its applications in other sectors in Nigeria; examine the potential threats or barriers to the application; appraise the technology’s cost-effectiveness, acceptability, local sustainability; and recommend the parameters around adoption, safety or security, reliability of drones technology, and effective monitoring for future implementation in Nigeria. The study generated its data from both primary and secondary sources while the research findings are comprehensively and descriptively analyzed. The study, however, recommends to transport sector, governments, end-users, and drone providers on logistics delivery to swiftly ensure that: safety of drone operations; insurance coverage availability be taken care of; ensure regulatory and procedural frameworks, better assessment and evaluation of drone pilot programmes; strengthening national and local capacity to test, learn, use and maintain drones; and ensure that evidences and prospects that could aid advancing the use of drones for logistics delivery or transport in the country be adequately shared. ER - TY - GEN TI - Fault detection and Performance Monitoring of Propellers in Electric UAV PY - 2022 T2 - 2022 IEEE Aerospace Conference (AERO) DO - 10.1109/aero53065.2022.9843261 UR - https://doi.org/10.1109/aero53065.2022.9843261 DA - 2022-03-05 AU - Palanisamy, Rajendra C1 - Michigan State University,Dept. of Civil and Environmental Engineering,East Lansing,MI,48823 AU - Kulkarni, Chetan C1 - KBR Inc., NASA Ames Research Center,Moffett Field,CA,94035 AU - Corbetta, Matteo C1 - KBR Inc., NASA Ames Research Center,Moffett Field,CA,94035 AU - Banerjee, Portia C1 - Michigan State University,Dept. of Civil and Environmental Engineering,East Lansing,MI,48823 LA - en KW - Propeller KW - Powertrain KW - Takeoff KW - Fault detection and isolation KW - Automotive engineering KW - Fault (geology) KW - Computer science KW - Engineering KW - Aerodynamics KW - Marine engineering KW - Aerospace engineering KW - Torque KW - Actuator KW - Artificial intelligence SP - 1 EP - 6 ER - TY - GEN TI - Urban logistics UAV vertiport location and layout planning method PY - 2025 DO - 10.1117/12.3060784 UR - https://doi.org/10.1117/12.3060784 DA - 2025-04-28 AU - Wang, Hairu AU - Zhang, Qiqian AU - Zhang, Honghai AU - Feng, Dikun LA - en KW - Computer science KW - Transport engineering KW - Engineering SP - 62 EP - 62 ER - TY - JOUR TI - Optimization of Urban Emergency Multimodal Transportation Scheduling With UAV-Ground Traffic Coordination PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 1524-9050 T2 - IEEE Transactions on Intelligent Transportation Systems DO - 10.1109/tits.2025.3626994 UR - https://doi.org/10.1109/tits.2025.3626994 DA - 2025-11-07 AU - Xia, Hanqing C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Zhang, Ming C1 - College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang, Nanjing, China AU - Ma, Zechao C1 - School of Transportation, Southeast University, Nanjing, China AU - Cui, Mengju C1 - Beijing Wanji Technology Company Ltd., Beijing, China AU - Yan, Chao C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China KW - Taxis KW - Scheduling (production processes) KW - Emergency response KW - Process (computing) KW - Intelligent transportation system KW - Relay KW - Trajectory KW - Vehicle dynamics VL - 27 IS - 1 SP - 692 EP - 708 ER - TY - JOUR TI - Assessment of UAV-based low-altitude logistics in urban supply chains: slope-intercepted grey incidence analysis PY - 2025 PB - Emerald Publishing Limited SN - 2043-9377 T2 - Grey Systems Theory and Application DO - 10.1108/gs-04-2025-0047 UR - https://doi.org/10.1108/gs-04-2025-0047 DA - 2025-09-29 AU - Yu, Zui C1 - School of Economics and Management, Zhejiang Business College , , LA - en KW - Drone KW - Software deployment KW - Reliability (semiconductor) KW - Payload (computing) KW - Supply chain KW - Delivery Performance KW - Key (lock) VL - 15 IS - 4 SP - 881 EP - 895 ER - TY - JOUR TI - A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9020118 UR - https://doi.org/10.3390/drones9020118 DA - 2025-02-05 AU - Liu, X.B. C1 - Department of Aeronautics and Astronautics, Fudan University, NO. 220 Handan Road, Yangpu District, Shanghai 200433, China AU - Wang, Fufu C1 - Technology and Engineering Center for Space Utilization, China Academy of Science, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China AU - Liu, Yu C1 - Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, No. 104 Youyi Road, Haidian District, Beijing 100094, China AU - Li, Long C1 - Department of Aeronautics and Astronautics, Fudan University, NO. 220 Handan Road, Yangpu District, Shanghai 200433, China LA - en KW - Kite KW - Computer science KW - Path (computing) KW - Motion planning KW - Algorithm KW - Mathematical optimization KW - Artificial intelligence KW - Mathematics KW - Computer network KW - Geometry VL - 9 IS - 2 SP - 118 EP - 118 AB - In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. ER - TY - GEN TI - Optimizing Beam Selection and Resource Allocation in UAV-Aided Vehicular Networks PY - 2022 DO - 10.1109/eucnc/6gsummit54941.2022.9815631 UR - https://doi.org/10.1109/eucnc/6gsummit54941.2022.9815631 DA - 2022-06-07 AU - Mignardi, Silvia C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy AU - Ferretti, Danila C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy AU - Marini, Riccardo C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy AU - Conserva, Francesca C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy AU - Bartoletti, Stefania C1 - IEIIT, CNR,Bologna,Italy C1 - IEIIT, CNR, Bologna, Italy AU - Verdone, Roberto C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy AU - Buratti, Chiara C1 - University of Bologna,WiLab, CNIT / DEI,Bologna,Italy C1 - WiLab, CNIT / DEI, University of Bologna, Bologna, Italy LA - en KW - Backhaul (telecommunications) KW - Computer science KW - Patrolling KW - Cellular network KW - Base station KW - Computer network KW - Taxis KW - Resource allocation KW - Quality of service KW - Real-time computing KW - Engineering KW - Transport engineering SP - 184 EP - 189 ER - TY - CHAP TI - Optimizing Urban Logistics through Low-Altitude Aerial Routes: A Study on UAV Integration in Supply Chains PY - 2025 PB - Atlantis Press SN - 2352-5428 T2 - Advances in economics, business and management research/Advances in Economics, Business and Management Research DO - 10.2991/978-94-6463-811-0_105 UR - https://doi.org/10.2991/978-94-6463-811-0_105 DA - 2025-01-01 AU - Zhang, Dongyang LA - en KW - Low altitude KW - Supply chain KW - Business KW - Aeronautics KW - Transport engineering KW - Altitude (triangle) KW - Engineering KW - Marketing SP - 973 EP - 982 ER - TY - JOUR TI - Towards Sustainable Urban Logistics: Route Optimization for Collaborative UAV–UGV Delivery Systems Under Road Network and Energy Constraints PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su18021091 UR - https://doi.org/10.3390/su18021091 DA - 2026-01-21 AU - Zou, Cunming C1 - Department of Public Security Management, Liaoning Police College, Dalian 116036, China AU - Yang, Qiaoran C1 - Agricultural Bank of China Limited Jilin Branch, Changchun 130051, China C1 - College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China AU - Li, Junyu C1 - College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China AU - Yue, Wei C1 - College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China AU - Yu, Na C1 - Ernst & Young Global Talent HUB (Dalian) Limited, Dalian 116000, China LA - en KW - Energy consumption KW - Initialization KW - Genetic algorithm KW - Crossover KW - Adaptability KW - Optimization problem KW - Drone KW - Sustainability KW - Intelligent transportation system VL - 18 IS - 2 SP - 1091 EP - 1091 AB - This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km2, the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. ER - TY - GEN TI - Research on Urban Low Altitude Logistics UAV Path Design Based on Intelligent Algorithm PY - 2025 DO - 10.1109/caibda65784.2025.11182626 UR - https://doi.org/10.1109/caibda65784.2025.11182626 DA - 2025-06-20 AU - Zhang, Fan C1 - School of Economics and Trade,ShangHai Urban Construction College,Shanghai,China,201415 AU - Liao, Yuhao C1 - Business School, University of ShangHai for Science and Technology,Shanghai,China,200093 AU - Ding, Ye C1 - School of Economics and Trade,ShangHai Urban Construction College,Shanghai,China,201415 LA - en KW - Motion planning KW - Ant colony optimization algorithms KW - Low altitude KW - Path (computing) KW - Scheduling (production processes) KW - Trajectory KW - Stability (learning theory) KW - Control system SP - 710 EP - 713 ER - TY - JOUR TI - Sharing instant delivery UAVs for crowdsensing: A data-driven performance study PY - 2024 PB - Elsevier BV SN - 0360-8352 T2 - Computers & Industrial Engineering DO - 10.1016/j.cie.2024.110100 UR - https://doi.org/10.1016/j.cie.2024.110100 DA - 2024-03-29 AU - Gao, Junhui C1 - School of Computer Science, Northwestern Polytechnical University, Xi’an, China C1 - School of Computer Science, Northwestern PolytechnicalUniversity, Xi’an, China C1 - School of Computer Science, Northwestern PolytechnicalUniversity, Xi'an, China AU - Pan, Yan C1 - Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan, China AU - Zhang, Xin C1 - Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan, China AU - Han, Qingye C1 - School of Management Science and Real Estate, Chongqing University, Chongqing, China AU - Hu, Yujiao C1 - Future Network Research Center, Purple Mountain Laboratories, Nanjing, 211111, China LA - en KW - Instant KW - Computer science KW - Crowdsensing KW - Real-time computing KW - Anomaly detection KW - Set (abstract data type) KW - Operations research KW - Data mining KW - Data science KW - Engineering VL - 191 SP - 110100 EP - 110100 ER - TY - JOUR TI - UAV Logistics Technology: Challenges and Prospects PY - 2024 PB - International Educative Research Foundation and Publisher (IERFP) SN - 2411-2933 T2 - International Journal for Innovation Education and Research DO - 10.31686/ijier.vol12.iss2.4217 UR - https://doi.org/10.31686/ijier.vol12.iss2.4217 DA - 2024-05-09 AU - Wang, Wen‐Jie C1 - Shanghai University of Engineering Science AU - Zhang, Chao C1 - Shanghai University of Engineering Science AU - Zhang, Yiyang C1 - Shanghai University of Engineering Science AU - Jiang, Yuhan C1 - Shanghai University of Engineering Science LA - en KW - Business KW - Systems engineering KW - Computer science KW - Engineering VL - 12 IS - 2 AB - With the vigorous development of e-commerce, the field of logistics distribution is facing more and more challenges. In order to meet the growing demand for delivery and enhance the shopping experience of consumers, drone logistics technology has gradually attracted attention. In recent years, drone technology has made great progress in the field of logistics distribution. However, its development still faces many challenges. These include the lack of technological maturity, legal and regulatory restrictions, security and other issues. At the same time, drones flying in urban environments need to overcome many obstacles, such as air traffic control and ground obstacles. Despite the challenges, the application prospects of drone logistics technology are still broad. Especially in the field of end-city delivery, drones can break through the limitations of traditional delivery methods, improve delivery efficiency and reduce costs. Through the comprehensive utilization of distribution resources such as drones and vehicles, the optimization and coordination of distribution networks are expected to further promote the development of the logistics industry. The future research direction will focus on the precision delivery of UAV logistics, the optimization of logistics supply chain and the collaborative application of multiple distribution methods. In view of the existing problems, this paper puts forward some improvement suggestions, including strengthening technology research and development, improving relevant laws and regulations, and improving flight safety. At the same time, future research should pay more attention to the collaborative operation of drones and other distribution methods, as well as the application exploration in specific scenarios, so as to provide effective support for the further development of UAV logistics technology. ER - TY - JOUR TI - Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios PY - 2023 SN - 2755-2721 T2 - Applied and Computational Engineering DO - 10.54254/2755-2721/9/20230068 UR - https://doi.org/10.54254/2755-2721/9/20230068 DA - 2023-09-22 AU - Zhang, Jiawei C1 - Northeast University Qinhuangdao Branch LA - en KW - Simulated annealing KW - Motion planning KW - Computer science KW - Drone KW - Operations research KW - Path (computing) KW - Gray (unit) KW - Transport engineering KW - Engineering KW - Artificial intelligence KW - Algorithm VL - 9 IS - 1 SP - 148 EP - 153 AB - Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning problem of unmanned aerial vehicles based on the factors that need to be considered in actual life logistics distribution. Firstly, the actual scenario requirements of logistics UAV applications are analyzed and a path planning algorithm for this scenario is enumerated. Subsequently, mathematical models are established from both path planning and customer satisfaction to assess the performance of logistics drones. Then, a comparative analysis of four existing classical path planning algorithms is conducted, and simulated annealing algorithm is used to optimize logistics delivery scenarios for path planning. Finally, combining the mathematical model and simulated annealing algorithm constructed in the previous article, logistics UAV simulation experiments are conducted in urban distribution scenarios and an ideal path planning model is obtained. The work of this article provides a certain reference basis for logistics companies in the application of logistics UAVs and the selection of path planning methods. ER - TY - JOUR TI - Incentive mechanism and path planning for Unmanned Aerial Vehicle (UAV) hitching over traffic networks PY - 2023 PB - Elsevier BV SN - 0167-739X T2 - Future Generation Computer Systems DO - 10.1016/j.future.2023.03.048 UR - https://doi.org/10.1016/j.future.2023.03.048 DA - 2023-04-05 AU - Lu, Ziyi C1 - School of Artificial Intelligence, Sun Yat-sen University, 519082, Zhuhai, China AU - Yu, Na C1 - School of Artificial Intelligence, Sun Yat-sen University, 519082, Zhuhai, China AU - Wang, Xuehe C1 - School of Artificial Intelligence, Sun Yat-sen University, 519082, Zhuhai, China C1 - The Guangdong Key Laboratory of Big Data Analysis and Processing, 510006, Guangzhou, China LA - en KW - Computer science KW - Incentive KW - Mechanism (biology) KW - Path (computing) KW - Computer network KW - Motion planning KW - Real-time computing KW - Simulation KW - Artificial intelligence KW - Robot VL - 145 SP - 521 EP - 535 ER - TY - JOUR TI - Pre-flight fast hotspot-free and conflict-free trajectory planning for on-demand UAV delivery logistics PY - 2025 PB - Elsevier BV SN - 1366-5545 T2 - Transportation Research Part E Logistics and Transportation Review DO - 10.1016/j.tre.2025.104539 UR - https://doi.org/10.1016/j.tre.2025.104539 DA - 2025-11-21 AU - Chen, Yutong C1 - Cranfield University, Cranfield, MK43 0AL, United Kingdom C1 - Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China AU - Xu, Yan C1 - Beihang University, Beijing, 100191, China C1 - Cranfield University, Cranfield, MK43 0AL, United Kingdom AU - Yang, Lei C1 - Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China AU - Hu, Minghua C1 - Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China LA - en KW - Trajectory KW - Key (lock) KW - Drone KW - Grid KW - Probabilistic logic KW - Plan (archaeology) KW - Motion planning KW - Trajectory optimization VL - 206 SP - 104539 EP - 104539 ER - TY - JOUR TI - Research on Dynamic Particle Swarm Optimization for Multi-Objective Reconnaissance Task Allocation of UAVs PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9080556 UR - https://doi.org/10.3390/drones9080556 DA - 2025-08-07 AU - Wang, Suyu C1 - Institute of Intelligent Mining and Robotics, Beijing 100083, China C1 - School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China AU - Qiao, Peihong C1 - School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China AU - Yue, Quan C1 - School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China AU - Xu, Zuojun C1 - School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China AU - Shang, Qichen C1 - School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China LA - en KW - Particle swarm optimization KW - Task (project management) KW - Computer science KW - Particle (ecology) KW - Mathematical optimization KW - Swarm behaviour KW - Artificial intelligence KW - Engineering KW - Algorithm KW - Biology KW - Systems engineering KW - Mathematics KW - Ecology VL - 9 IS - 8 SP - 556 EP - 556 AB - With the increasingly widespread application of unmanned aerial vehicle (UAV) systems in disaster monitoring, urban management, logistics transportation, and reconnaissance, efficient dynamic task allocation has become a key issue in improving task execution efficiency. To address the challenges posed by dynamic changes in task objectives and resource constraints that traditional task allocation methods struggle with in complex environments, this paper proposes a multi-objective particle swarm optimization algorithm, DCMPSO, for UAV dynamic reconnaissance task allocation. First, the framework of DCMPSO is constructed, dividing the optimization of dynamic problems into three parts: environment change detection, environment change response, and actual optimization, with the designed strategy of range prediction strategy based on centroid translation. Then, simulation experiments are conducted to verify the effectiveness of the algorithm mechanisms through ablation experiments and to demonstrate the superiority of DCMPSO in convergence and distribution compared to DNSGA-II and SGEA through comparative experiments. Finally, a multi-UAV dynamic task allocation model is established and optimized, proving that DCMPSO can correctly solve the UAV dynamic multi-objective allocation problem and effectively find its optimal solution, providing an effective solution for practical applications. ER - TY - JOUR TI - Adaptive airspace allocation model for urban drone logistics using multi-objective optimization under uncertainty PY - 2026 PB - Nature Portfolio SN - 2045-2322 T2 - Scientific Reports DO - 10.1038/s41598-025-32450-8 UR - https://doi.org/10.1038/s41598-025-32450-8 DA - 2026-01-19 AU - Zhu, Yao C1 - Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. yphz5223@outlook.com AU - Sun, Xin C1 - Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China AU - Hou, Tongdi C1 - Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China LA - en KW - Drone KW - Robustness (evolution) KW - Scalability KW - Air traffic control KW - Pareto principle KW - Resource allocation KW - Constraint (computer-aided design) KW - Robust optimization VL - 16 IS - 1 SP - 2562 EP - 2562 AB - The large-scale application of urban unmanned aerial vehicle (UAV) logistics is confronted with challenges such as limited airspace resources, dynamic changes in demand, and multiple uncertainties. Traditional static allocation methods are difficult to adapt to complex urban environments. This study constructs a DRL-RO hybrid framework that integrates deep reinforcement learning and discrete robust optimization. It characterizes the influence mechanisms of demand fluctuations, weather changes, and emergencies through a three-layer uncertainty modeling system, and introduces a policy network enhanced by an attention mechanism to capture spatio-temporal correlations in the spatial domain. The improved MOEA/D-DRL algorithm is adopted to achieve rapid approximation of the Pareto frontier. The verification of the actual scenarios in Shenzhen shows that this framework reduces the computational complexity to the sub-quadratic level while maintaining a high success rate. Through the hierarchical airspace management strategy, it effectively balances the three goals of distribution efficiency, flight safety and operating costs. The Wasserstein sphere constraint ensures robustness and scalability in extreme scenarios. It provides theoretical support and technical solutions for the construction of city-level unmanned aerial vehicle (UAV) traffic management systems. ER - TY - JOUR TI - Environmental sustainability and risk-aware optimization in hybrid truck-drone logistics: A holistic multi-objective framework PY - 2026 PB - Elsevier BV SN - 0969-6997 T2 - Journal of Air Transport Management DO - 10.1016/j.jairtraman.2026.102978 UR - https://doi.org/10.1016/j.jairtraman.2026.102978 DA - 2026-02-10 AU - Mahmoodi, Armin C1 - Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada AU - Sajadi, Seyed C1 - Operations and Supply Chain Simulation, Operations and Information Management Department, Aston Business School, Aston University, Birmingham, B4 7ET, UK AU - Laliberté, Jeremy C1 - Aerospace Engineering, Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, Canada AU - Easa, Said C1 - Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada LA - en KW - Drone KW - Flexibility (engineering) KW - Sustainability KW - Vehicle routing problem KW - Travelling salesman problem KW - Scalability KW - Sensitivity (control systems) KW - Sorting KW - Routing (electronic design automation) VL - 134 SP - 102978 EP - 102978 AB - Compared to conventional truck-only systems, hybrid truck–drone delivery systems offer transformative potential for last-mile logistics by addressing operational inefficiencies, minimizing environmental impact, and enhancing safety through risk-aware optimization. This study develops a stochastic multi-objective optimization framework grounded in the Specific Operations Risk Assessment (SORA) methodology. By extending the vehicle routing problem with pickup and delivery (VRPPD) and the flying sidekick traveling salesman problem (FSTSP), the model incorporates battery optimization, CO 2 emissions reduction, and energy-efficient routing strategies. Delivery cost, time, energy consumption, operational risk, and battery performance are all optimized using a mixed-integer linear programming (MILP) and non-dominated sorting genetic algorithm II (NSGA-II) technique. Sensitivity analysis show that increasing drone fleet size and efficiency results in significant cost, time, and energy savings while improving safety. The model's flexibility in both urban and remote delivery contexts is confirmed by numerical trials. In line with life cycle analysis (LCA), this study offers practical advice for environmentally responsible and risk-aware logistics, assisting decision-makers and industry participants in the development of scalable and sustainable solutions. • Optimizes cost, time, risk, and energy in hybrid truck–drone logistics. • Uses MILP and NSGA-II for multi-objective routing and energy savings. • Incorporates SORA for risk-aware UAV operations in urban logistics. • Sensitivity shows drone efficiency cuts cost, risk, and CO 2 emissions. • Achieves energy-efficient delivery via battery-aware UAV coordination. ER - TY - JOUR TI - Urban Area End Logistics Drones Distribution Route Planning PY - 2023 SN - 2770-6702 T2 - International Journal of Education and Humanities DO - 10.54097/ijeh.v8i1.7255 UR - https://doi.org/10.54097/ijeh.v8i1.7255 DA - 2023-04-10 AU - Tan, Fang C1 - School of Modern Post , Chongqing University of Posts and Telecommunications , Chongqing 400065 , China C1 - School of Modern Post, Chongqing University of Posts and Telecommunications, Chongqing 400065, China LA - en KW - Drone KW - Energy consumption KW - Computer science KW - Grid KW - Fuel efficiency KW - Battery (electricity) KW - Automotive engineering KW - Consumption (sociology) KW - Genetic algorithm KW - Energy (signal processing) KW - Real-time computing KW - Simulation KW - Engineering VL - 8 IS - 1 SP - 155 EP - 160 AB - With the development of logistics technology, the application of drones in the last mile of logistics distribution has also become a hot topic. Due to the complexity and particularity of the flight environment in urban areas, UAVs need to comprehensively consider safety performance and flight energy consumption when conducting terminal logistics distribution. Based on this, this paper constructs a mathematical model with the goal of shortening flight paths, reducing energy consumption, and improving flight safety, and completes environmental modeling using grid method and genetic algorithm to solve the model. At the same time, the battery energy consumption of the UAV is calculated using the energy consumption segmentation mode. The experimental results of a numerical example verify the effectiveness of the model and algorithm, and also prove that considering segmented energy consumption can more accurately calculate the battery energy consumption of unmanned aerial vehicles. ER - TY - JOUR TI - Digital Twin-Driven Trajectory and Resource Optimization for UAV Swarms in Low-Altitude Urban Logistics and Communication Environments PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10050376 UR - https://doi.org/10.3390/drones10050376 DA - 2026-05-14 AU - Tong, Hanyang C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Song, Ziyang C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Zhu, Zhenyan C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Sun, Jinlong C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China LA - en KW - Trajectory KW - Trajectory optimization KW - Particle swarm optimization KW - Optimization problem KW - Cluster analysis KW - Probabilistic logic KW - Throughput KW - Communications system KW - Robust optimization VL - 10 IS - 5 SP - 376 EP - 376 AB - Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments. ER - TY - JOUR TI - Analysis and Empirical Study of Factors Influencing Urban Residents’ Acceptance of Routine Drone Deliveries PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su151813335 UR - https://doi.org/10.3390/su151813335 DA - 2023-09-06 AU - Zhang, Zhao C1 - School of Logistics, Chengdu University of Information Technology, Chengdu 610103, China C1 - Xichang Urban and Rural Grass-Roots Governance Center, Xichang 615000, China AU - Zhang, Zhao C1 - School of Logistics, Chengdu University of Information Technology, Chengdu 610103, China C1 - Xichang Urban and Rural Grass-Roots Governance Center, Xichang 615000, China AU - Xiao, Chunyan C1 - School of Logistics, Chengdu University of Information Technology, Chengdu 610103, China AU - Zhang, Zhiguo C1 - School of Logistics, Chengdu University of Information Technology, Chengdu 610103, China C1 - Xichang Urban and Rural Grass-Roots Governance Center, Xichang 615000, China AU - Zhang, Zhiguo C1 - School of Logistics, Chengdu University of Information Technology, Chengdu 610103, China C1 - Xichang Urban and Rural Grass-Roots Governance Center, Xichang 615000, China LA - en KW - Drone KW - Normalization (sociology) KW - Business KW - Plan (archaeology) KW - Computer science KW - Computer security KW - Operations management KW - Environmental economics KW - Engineering KW - Geography KW - Economics KW - Sociology VL - 15 IS - 18 SP - 13335 EP - 13335 AB - The usage of drone delivery couriers has multiple benefits over conventional methods, and it is expected to play a big role in the development of urban intelligent logistics. Many courier companies are currently attempting to deliver express delivery using drones in the hopes that this new type of tool used for delivery tasks will become the norm as soon as possible. However, most urban residents are currently unwilling to accept the use of drones to deliver express delivery as normal. This study aims to find out the reasons for the low acceptance of the normalization of drone delivery by urban residents and formulate a more reasonable management plan for drone delivery so that the normalization of drone delivery can be realized as soon as possible. A research questionnaire was scientifically formulated which received effective feedback from 231 urban residents in Jinjiang District, Chengdu City. A binary logistic model was used to determine the factors that can significantly influence the acceptance of residents. In addition, the fuzzy interpretive structural model(Fuzzy-ISM) was used to find out the logical relationship between the subfactors inherent to these influencing factors. It was concluded that when the infrastructure is adequate, increasing public awareness and education, enhancing the emergency plan, lowering delivery costs, enhancing delivery efficiency and network coverage, and bolstering the level of safety management can significantly raise resident acceptance of unmanned aerial vehicle(UAV) delivery. Given the positional characteristics of the subfactors in the interpretive structural model(ISM) and matrices impacts croises-multiplication appliance classemen(MICMAC) in this study, we should first make sure that the drone delivery activities can be carried out in a safe and sustainable environment with all the necessary equipment, instead of focusing on increasing the residents’ acceptance right away, in the future work of regularized drone urban delivery has not yet started the construction phase. There should be more effort put into building the links that will enable acceptance to be improved with higher efficiency, which will be helpful to the early realization of the normalization of drone urban delivery if there is already a certain construction foundation in the case where the drone delivery environment is up to standard and hardware conditions are abundant. ER - TY - GEN TI - A Summary of UAV Positioning Technology in GPS Denial Environment PY - 2021 SN - 1867-8211 T2 - Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering DO - 10.1007/978-3-030-69066-3_25 UR - https://doi.org/10.1007/978-3-030-69066-3_25 DA - 2021-01-01 AU - Jun-song, PU C1 - School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, China AU - Shi, Shuo C1 - Network Communication Research Centre, Peng Cheng Laboratory, Shenzhen, China C1 - School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, China AU - Xue-mai, GU C1 - International Innovation Institute of HIT in Huizhou, Huizhou, China C1 - School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, China LA - en KW - Global Positioning System KW - Computer science KW - Hotspot (geology) KW - Computer security KW - Hybrid positioning system KW - Systems engineering KW - Real-time computing KW - Aeronautics KW - Positioning system KW - Engineering KW - Telecommunications SP - 283 EP - 294 ER - TY - JOUR TI - Path optimization for UAV food delivery under extreme weather conditions with wind disturbance consideration PY - 2025 PB - Elsevier BV SN - 0360-8352 T2 - Computers & Industrial Engineering DO - 10.1016/j.cie.2025.111452 UR - https://doi.org/10.1016/j.cie.2025.111452 DA - 2025-08-09 AU - Lu, Xinhui C1 - Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Smart Management of Energy & Environment and Green & Low Carbon Development, Hefei University of Technology, Hefei 230009, China C1 - Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China C1 - School of Management, Hefei University of Technology, Hefei 230009, China AU - Wu, Qian C1 - Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Smart Management of Energy & Environment and Green & Low Carbon Development, Hefei University of Technology, Hefei 230009, China C1 - School of Management, Hefei University of Technology, Hefei 230009, China AU - Zhou, Kaile C1 - Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Smart Management of Energy & Environment and Green & Low Carbon Development, Hefei University of Technology, Hefei 230009, China C1 - Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China C1 - School of Management, Hefei University of Technology, Hefei 230009, China LA - en KW - Disturbance (geology) KW - Extreme weather KW - Food delivery KW - Environmental science KW - Path (computing) KW - Meteorology KW - Computer science KW - Engineering KW - Ecology KW - Geography KW - Biology KW - Climate change KW - Business KW - Advertising VL - 209 SP - 111452 EP - 111452 ER - TY - GEN TI - Optimization of task allocation for multi-UAV community logistics distribution PY - 2024 DO - 10.1117/12.3032666 UR - https://doi.org/10.1117/12.3032666 DA - 2024-08-17 AU - Shiyu, Bu C1 - Shenzhen Technology University (China) AU - Ting, Zhang C1 - Shenzhen Technology University (China) AU - Ruixue, Tang C1 - Shenzhen Technology University (China) AU - Shouqin, Zhou C1 - Shenzhen Technology University (China) AU - Gang, Hu C1 - Shenzhen 4PX Express Co., Ltd. (China) AU - Jianhua, Chen C1 - Shenzhen 4PX Express Co., Ltd. (China) LA - en KW - Crossover KW - Computer science KW - Genetic algorithm KW - Operations research KW - Mathematical optimization KW - Population KW - Tournament selection KW - Context (archaeology) KW - Customer satisfaction KW - Task (project management) KW - Fitness proportionate selection KW - Roulette KW - Fitness function KW - Artificial intelligence KW - Engineering KW - Machine learning KW - Marketing KW - Business KW - Systems engineering KW - Geography KW - Mathematics VL - 39 SP - 110 EP - 110 ER - TY - JOUR TI - Multi-UAV Trajectory Planning Based on a Two-Layer Algorithm Under Four-Dimensional Constraints PY - 2025 T2 - Preprints.org DO - 10.20944/preprints202505.2202.v1 UR - https://doi.org/10.20944/preprints202505.2202.v1 DA - 2025-05-28 AU - Yang, Yong C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support , Chengdu , 610021 , China AU - Fu, Yujie C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support , Chengdu , 610021 , China AU - Xin, Renlin C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support , Chengdu , 610021 , China AU - Feng, Weiqi C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support , Chengdu , 610021 , China AU - Xu, Kaijun C1 - School of Flight Technology , Civil Aviation Flight University of China , Guanghan 618307 , China; C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support , Chengdu , 610021 , China LA - en KW - Trajectory KW - Layer (electronics) KW - Computer science KW - Algorithm KW - Trajectory optimization KW - Mathematical optimization KW - Mathematics KW - Physics KW - Materials science AB - With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower-layer path planning stage, considering the limitations of the Whale Optimization Algorithm (WOA) such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. In the upper-layer task assignment stage, in response to the tendency of the Adaptive Large Neighborhood Search (ALNS) algorithm to fall into local optima under complex constraints, we incorporate K-means clustering for initialization and a simulated annealing mechanism, thereby proposing an improved IALNS to enhance scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. ER - TY - GEN TI - Construction method of urban low-altitude UAV route network based on demand PY - 2023 T2 - Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022) DO - 10.1117/12.2668509 UR - https://doi.org/10.1117/12.2668509 DA - 2023-02-16 AU - Yu, Wenjuan C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics (China) AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics (China) AU - Zhou, Jinlun C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics (China) AU - Zhong, Gang C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics (China) LA - en KW - Computer science KW - Low altitude KW - Node (physics) KW - Greedy algorithm KW - Construct (python library) KW - Computer network KW - Network simulation KW - Real-time computing KW - Altitude (triangle) KW - Engineering KW - Algorithm SP - 21 EP - 21 ER - TY - JOUR TI - Numerical simulation of the transition flight aerodynamics of cross-shaped quad-tiltrotor UAV PY - 2024 PB - Nature Portfolio SN - 2045-2322 T2 - Scientific Reports DO - 10.1038/s41598-024-68927-1 UR - https://doi.org/10.1038/s41598-024-68927-1 DA - 2024-08-02 AU - Du, Siliang C1 - Faculty of Mechanical and Material Engineering, HuaiYin Institute of Technology, Huaian, 223003, China. kjofchina@qq.com C1 - National Key Laboratory of Rotorcraft Aeromechanics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. kjofchina@qq.com C1 - Faculty of Mechanical and Material Engineering, HuaiYin Institute of Technology, Huaian, 223003, China AU - Zha, Yi C1 - Faculty of Mechanical and Material Engineering, HuaiYin Institute of Technology, Huaian, 223003, China LA - en KW - Aerodynamics KW - Aerospace engineering KW - Computer science KW - Aeronautics KW - Simulation KW - Engineering VL - 14 IS - 1 SP - 17878 EP - 17878 AB - In order to enhance the stability of the tilt transition process, a new configuration of Quad-Tiltrotor UAV was presented in this paper. Firstly, numerical simulation was used to calculate and analyze the aerodynamic interaction between the front rotor/fuselage/rear rotor during the transition state mode. The calculation model of the isolated rotor, front-rear rotor, front rotor-fuselage, and front rotor-rear rotor-fuselage combination states are established. Besides, the effects of pitch, roll, and yaw moment on the fuselage at different tilt angles are analyzed. It is concluded that the front rotor is the leading factor in the aerodynamic interference of the whole UAV in the different combination states. The research results can provide a reference for the optimization design of the overall layout, structure, and flight control strategy of the cross-shaped quad-tiltrotor UAV, and can also provide solutions for the logistics application of urban air traffic. ER - TY - JOUR TI - An Improved Multi-Actor Hybrid Attention Critic Algorithm for Cooperative Navigation in Urban Low-Altitude Logistics Environments PY - 2025 SN - 1546-2218 T2 - Computers, materials & continua/Computers, materials & continua (Print) DO - 10.32604/cmc.2025.063703 UR - https://doi.org/10.32604/cmc.2025.063703 DA - 2025-01-01 AU - Li, Chao C1 - School of Computer Science, Hubei University of Technology, Wuhan, 430068, China 2 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China 3 Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer Science, Hubei University of Technology, Wuhan, 430068, China AU - Feng, Quanzhi C1 - School of Computer Science, Hubei University of Technology, Wuhan, 430068, China 2 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China 3 Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer Science, Hubei University of Technology, Wuhan, 430068, China AU - Ding, Caichang C1 - School of Computer Science, Hubei University of Technology, Wuhan, 430068, China 2 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China 3 Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer Science, Hubei University of Technology, Wuhan, 430068, China AU - Ỹe, Zhiwei C1 - School of Computer Science, Hubei University of Technology, Wuhan, 430068, China 2 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China 3 Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer Science, Hubei University of Technology, Wuhan, 430068, China LA - en KW - Computer science KW - Low altitude KW - Altitude (triangle) KW - Environmental science KW - Aerospace engineering KW - Artificial intelligence KW - Engineering KW - Mathematics VL - 84 IS - 2 SP - 3605 EP - 3621 AB - The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordin... | Find, read and cite all the research you need on Tech Science Press ER - TY - JOUR TI - A Resilient Cloud–Edge Digital Twin Framework for Urban UAV Logistics Under 3D Blockages and ADS-B Signal Anomalies PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 1424-8220 T2 - Sensors DO - 10.3390/s26123778 UR - https://doi.org/10.3390/s26123778 DA - 2026-06-13 AU - Tong, Hanyang C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Chen, Yuehua C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Liu, Yilong C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Huang, Feige C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China AU - Sun, Jinlong C1 - School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China LA - en KW - Cloud computing KW - Heuristic KW - Telecommunications network KW - Cluster analysis KW - Partition (number theory) KW - SIGNAL (programming language) KW - Enhanced Data Rates for GSM Evolution KW - Point cloud KW - Jamming VL - 26 IS - 12 SP - 3778 EP - 3778 AB - Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance-Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud-edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier "Teacher-Student" paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity "Teacher," employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV's edge digital twin. This resource-constrained edge tier relies on a localized "Student" network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. ER - TY - JOUR TI - Research on the Impact of Urban Extreme Wind Fields on UAVs’ Flight Stability in Typical Scenarios PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app16010145 UR - https://doi.org/10.3390/app16010145 DA - 2025-12-23 AU - Liu, Jia C1 - China Academy of Civil Aviation Science and Technology, Beijing 100028, China C1 - School of Transportation Science and Engineering, Beihang University, Beijing 100191, China AU - Li, Yu C1 - China Academy of Civil Aviation Science and Technology, Beijing 100028, China AU - Yang, Hao C1 - China Academy of Civil Aviation Science and Technology, Beijing 100028, China AU - Wu, Jianghao C1 - School of Transportation Science and Engineering, Beihang University, Beijing 100191, China AU - Qu, Qiulin C1 - School of Aeronautics Science and Engineering, Beihang University, Beijing 100191, China LA - en KW - Aerodynamics KW - Acceleration KW - Wind speed KW - Wind profile power law KW - Stability (learning theory) KW - Wind gradient KW - Crosswind VL - 16 IS - 1 SP - 145 EP - 145 AB - To assess UAV (Unmanned Aerial Vehicle) flight stability in urban wind fields, this study conducted numerical simulations of urban scene and logistics UAV models and developed a wind field safety level evaluation model for UAV flight paths. First, urban wind field structures were analyzed with simulations of typical building clusters. Second, the UAV’s aerodynamic characteristics under vertical balance were elaborated. Third, sideslip angles and wind speeds were adjusted based on the UAV’s maximum wind resistance to explore aerodynamic performance variations across conditions. Finally, a safety level calculation method was proposed to determine the wind field safety distribution along target paths. The results show that building layouts significantly affect urban wind fields, forming wind acceleration zones beside high-rises and between some buildings. The acceleration effect at 25 m is stronger than at 10 m and 50 m. UAV aerodynamic moments vary greatly with wind sideslip angles, with the dangerous angle being around 150°. Flight stability and wind field structures differ notably by path and height. This evaluation method enables UAVs to avoid high-risk areas, improving urban flight stability. ER - TY - JOUR TI - Management of urban air logistics with unmanned aerial vehicles: The case of medicine supply in Aveiro, Portugal PY - 2021 PB - OmniaScience SN - 2014-4806 T2 - Journal of Airline and Airport Management DO - 10.3926/jairm.182 UR - https://doi.org/10.3926/jairm.182 DA - 2021-06-30 AU - Silva, Mário C1 - CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal Universidade da Beira Interior, Aerospace Sciences Department (DCA-UBI), Rua Marquês D’Ávila e Bolama, 6201- 001, Covilhã, Portugal C1 - CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal Universidade da Beira Interior, Aerospace Sciences Department (DCA-UBI), Rua Marquês D'Ávila e Bolama, 6201- 001, Covilhã, Portugal AU - Silva, Jorge C1 - CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal Universidade da Beira Interior, Aerospace Sciences Department (DCA-UBI), Rua Marquês D’Ávila e Bolama, 6201- 001, Covilhã, Portugal C1 - CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal Universidade da Beira Interior, Aerospace Sciences Department (DCA-UBI), Rua Marquês D'Ávila e Bolama, 6201- 001, Covilhã, Portugal LA - en KW - Payload (computing) KW - City logistics KW - Legislation KW - Transport engineering KW - Computer science KW - Urban environment KW - Supply chain KW - Business KW - Environmental planning KW - Computer security KW - Environmental science KW - Engineering VL - 11 IS - 1 SP - 34 EP - 34 AB - Purpose: This research aims to investigate the relation between urban logistics and all delivery systems used. The unmanned aerial vehicle (UAV) and the unmanned aerial system (UAS) have been under investigation in the world of logistics, having been pointed as the next logistic technology. For that reason, this article proposes the use of UAV in urban logistics.Design/methodology/approach: We set for the methodology study the current state of this system and analyze what lies ahead soon. Based on this information, we intend to implement a scenario of deliveries in an urban environment. This scenario will be in the city of Aveiro and consists of the delivery of medicines into pharmacies located in an urban environment. With this study, we pretend to find the best current and future solution to operate in an urban environment, with the conventional vehicle or the UAV/UAS.Findings: The legal implications for the use of UAV/UAS are one important factor that can set back the use of these technologies due to the lack of legislation. Although this technology has some limitations in endurance and payload, this investigation reached a consensus in the use of UAV for logistics in urban areas, in small payloads (around 15 kg), and low endurance (around 25 min). The UAV/UAS brings excellent advantages that the conventional vehicle cannot overcome: direct routes; traffic congestion; and environmental legislation.Practical implications: This study brings an overview of a possible scenario for urban logistics. Although this is currently not possible, soon, this scenario could be implemented. This would bring a reduction in operation costs and reduce the congestion in the urban cores of the cities.Originality/value: Based on this research, this delivery system could, in the future, help and be the starting point for UAV/UAS logistics, specifically in the delivery of medicines. ER - TY - GEN TI - Optimizing Urban UAV Delivery Networks with Improved K-Means: Insights from Shiyan City PY - 2025 DO - 10.1109/itoec63606.2025.10969068 UR - https://doi.org/10.1109/itoec63606.2025.10969068 DA - 2025-03-14 AU - Song, Zhuoling C1 - Hubei Industrial Polytechnic,School of Tourism and Trade,Shiyan,China AU - Wang, Qin C1 - Hubei Industrial Polytechnic,School of Tourism and Trade,Shiyan,China AU - Chen, Zichao C1 - Hubei Industrial Polytechnic,School of Mechanical and Electrical Engineering,Shiyan,China AU - Fang, Dengan’Qi C1 - Hubei Industrial Polytechnic,School of Tourism and Trade,Shiyan,China LA - en KW - Computer science KW - Environmental science KW - Transport engineering KW - Engineering SP - 752 EP - 756 ER - TY - CHAP TI - Future Transport and Logistics in Smart Cities PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 2624-6511 T2 - Smart Cities DO - 10.1201/9781003272199-10 UR - https://doi.org/10.1201/9781003272199-10 DA - 2022-02-01 AU - Minaei, Negin LA - en KW - Business SP - 113 EP - 142 AB - Flying objects are increasingly introduced to cities ranging from unmanned aerial vehicles (UAV) or drones to autonomous aerial vehicles (AAV), Uber flying cabs, and urban aerial mobility (UAM), as new forms of personal transport. UAVs have been studied from military and law perspectives, with mentions in political, social justice, and some environmental studies. Very few studies have looked at the interaction of UAVs with cities and public spaces. FAA announced drone rules would not be ready until 2022; there is a good opportunity for urban designers, planners, and architects to plan and design urban aerial spaces of cities to accommodate different mobile flying objects in the skies of future cities. This chapter studies drones as the most common flying objects using the future-oriented technology analysis method to review their applications, potentials and problems, and the latest regulations applied by authorities in different countries. It discusses and analyzes some existing or proposed solutions by industry or other researchers to offer planning and design solutions for both Smart Cities and non-Smart Cities including remote sensing, Global Positioning System, Global Navigation Satellite System, and radio wave technologies to create smart platforms to monitor, track, and prevent unauthorized interactions of UAVs and AAVs in urban environments. ER - TY - GEN TI - Multi-UAVs Pickup and Delivery Problem: Minimizing Maximum Energy Consumption PY - 2024 DO - 10.1109/globecom52923.2024.10901694 UR - https://doi.org/10.1109/globecom52923.2024.10901694 DA - 2024-12-08 AU - Deng, Zicong C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 AU - Wu, Fahui C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 AU - Xu, Yu C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 AU - Luo, Yuanhua C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 AU - Yang, Dingcheng C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 AU - Xiao, Lin C1 - Nanchang University,Information Engineering School,Nanchang,China,330031 LA - en KW - Pickup KW - Energy consumption KW - Computer science KW - Energy (signal processing) KW - Mathematical optimization KW - Engineering KW - Physics KW - Electrical engineering KW - Mathematics KW - Artificial intelligence SP - 2027 EP - 2033 ER - TY - JOUR TI - An innovative framework integrating MILP and a parallel optimal algorithm for UAV-Enabled last-Mile delivery PY - 2025 PB - Taylor & Francis SN - 0020-7543 T2 - International Journal of Production Research DO - 10.1080/00207543.2025.2557530 UR - https://doi.org/10.1080/00207543.2025.2557530 DA - 2025-09-23 AU - Amirteimoori, Arash C1 - University of Twente C1 - Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands AU - Kia, Reza C1 - Sultan Qaboos University C1 - Department of Operations Management and Business Statistics, Sultan Qaboos University, Muscat, Oman AU - Mohamed, Moataz C1 - McMaster University C1 - Department of Civil Engineering, McMaster University, Hamilton, Canada AU - Weber, Gerhard‐Wilhelm C1 - Poznan University of Technology C1 - Department of Engineering Management, Poznan University of Technology, Poznan, Poland LA - en KW - Optimal design KW - Production (economics) KW - Minification KW - Key (lock) KW - Algorithm design KW - Efficient algorithm VL - 64 IS - 3 SP - 777 EP - 797 ER - TY - THES TI - Drone deliveries of medical goods in urban healthcare PY - 2019 PB - Chalmers University of Technology T2 - Chalmers Publication Library (Chalmers University of Technology) DA - 2019-01-01 AU - Magnusson, Sofia AU - Hagerfors, Pauline LA - en KW - Health care KW - Drone KW - Business KW - Data science KW - Medical emergency KW - Medicine KW - Computer science KW - Economics KW - Economic growth AB - There is an ongoing trend of centralization and specialization in the healthcare sector that entails requirements for fast, flexible and reliable deliveries of medical goods between healthcare facilities.Logistics systems in healthcare are additionally highly impacted by the importance of high patient safety.Due to the potential time savings compared to road-based transports in urban areas, unmanned aerial vehicles (UAVs), more commonly known as drones, are emerging as a mode of transport.This qualitative single case study intends to combine findings in healthcare and logistics, and from a patient safety perspective assess the potential benefits of a drone-based delivery system of medical goods.The study combines literature on patient safety, centralization and the state of the art of drones, together with a comprehensive current state analysis of the urban delivery system of medical goods in Gothenburg, Sweden.The analysis is based on a total of 17 interviews, six observations and historical data on delivery statistics.The study shows that the delivery system of medical goods in Gothenburg is extensive and complex, partly due to centralization and scattered facilities, and includes both regular and on-demand deliveries chosen based on required delivery lead time.It is also shown that transports are impacted by an unpredictable traffic environment today, resulting in long and unreliable delivery lead times.Thus, there are benefits, primarily related to the possibility to reduce delivery lead time, which can be enabled by a drone-based delivery system.Additional benefits are increased flexibility and reliability of deliveries, reduced risk of errors and potential for security improvements.Through the translation of patient safety aims into logistics performance indicators, we further show that these benefits could improve patient safety in a healthcare organization.Furthermore, characteristics of medical goods are mapped in order to investigate the compatibility and suitability with drone deliveries.We conclude that urgently needed replaceable medical goods with low economic value and low risk of theft, which are lightweight and frequently delivered on-demand in low volumes between facilities, are suitable for drone deliveries.This study mainly focuses on urban drone deliveries of medical goods from a strategic point of view, hence, it is of great importance to explore operational and design aspects of drone delivery systems in future research.The findings from this study can be used as guidance for healthcare organizations' strategic decisions upon the use of drones as a mode of transport for medical goods. ER - TY - GEN TI - A Wind-Aware Path Planning Method for UAVs in Urban Environments PY - 2026 DO - 10.1109/iceaai68945.2026.11442519 UR - https://doi.org/10.1109/iceaai68945.2026.11442519 DA - 2026-01-16 AU - Gao, Juanjuan C1 - Soochow University,Suzhou,China AU - Liu, Xing C1 - University of Science and Technology of China,Hefei,China AU - Xia, Yuchen C1 - Soochow University,Suzhou,China AU - Shang, Shengxuan C1 - Soochow University,Suzhou,China AU - Pang, Tianrui C1 - Soochow University,Suzhou,China AU - Yang, Benyuan C1 - Soochow University,Suzhou,China KW - Motion planning KW - Path (computing) KW - Key (lock) KW - Urban planning KW - Trajectory SP - 1 EP - 4 ER - TY - JOUR TI - Unmanned aerial systems (UAS) in urban search and rescue-methodology, capacity development, and integration PY - 2021 SN - 1543-5865 T2 - Journal of Emergency Management DO - 10.5055/jem.0496 UR - https://doi.org/10.5055/jem.0496 DA - 2021-01-01 AU - Braverman, Ariel C1 - School of Health Sciences, American Public University, Charles Town, West Virginia LA - en KW - Search and rescue KW - Drone KW - Urban search and rescue KW - Situation awareness KW - Process (computing) KW - Computer science KW - Aeronautics KW - Global Positioning System KW - Transport engineering KW - Systems engineering KW - Engineering KW - Artificial intelligence KW - Robot KW - Mobile robot KW - Aerospace engineering KW - Telecommunications VL - 19 IS - 1 SP - 33 EP - 38 ER - TY - JOUR TI - Heuristic Evaluation in A-Star Algorithm for Enhanced Urban Uav Path Optimization PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5079937 UR - https://doi.org/10.2139/ssrn.5079937 DA - 2025-01-01 AU - Balakrishnan, Hari C1 - College of Engineering Trivandrum C1 - College of Engineering Trivandrum, Department of Mechanical Engineering, College of Engineering Trivandrum, India AU - Suryan, Abhilash C1 - College of Engineering Trivandrum C1 - College of Engineering Trivandrum, Department of Mechanical Engineering, College of Engineering Trivandrum, India AU - Alex, Anu C1 - College of Engineering Trivandrum C1 - College of Engineering Trivandrum, Department of Mechanical Engineering, College of Engineering Trivandrum, India AU - Raj, S. C1 - Marian Engineering College C1 - Marian Engineering College, Thiruvananthapuram, Kerala, India, Thiruvananthapuram, India AU - Zhang, Guang LA - en KW - Heuristic KW - Path (computing) KW - Star (game theory) KW - A* search algorithm KW - Computer science KW - Mathematical optimization KW - Algorithm KW - Mathematics KW - Artificial intelligence KW - Computer network ER - TY - JOUR TI - Resilient Last-Mile Logistics in Smart Cities Through Multi-Visit and Time-Dependent Drone–Truck Collaboration PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9110782 UR - https://doi.org/10.3390/drones9110782 DA - 2025-11-10 AU - Xiao, Qinxin C1 - School of Business, Sichuan Normal University, Chengdu 610066, China AU - Gao, Jiaojiao C1 - School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China LA - en KW - Truck KW - Sustainability KW - Drone KW - City logistics KW - Supply chain KW - Vehicle routing problem KW - Routing (electronic design automation) KW - Humanitarian Logistics VL - 9 IS - 11 SP - 782 EP - 782 AB - Urban logistics in smart cities are increasingly challenged by congestion, sustainability pressures, and the growing demand for resilient delivery systems. To address these challenges, this study introduces the Multi-Visit Time-Dependent Truck–Drone Routing Problem with simultaneous Pickup and Delivery (MTTRP-PD), a novel framework that integrates three realistic features: (i) drones serving multiple customers per sortie, (ii) time-dependent truck speeds reflecting dynamic traffic conditions, and (iii) synchronized pickup and delivery between trucks and drones. By incorporating these elements, the proposed model provides a more realistic and comprehensive representation of urban air-ground collaborative logistics in the last mile. An optimization framework and an efficient solution approach are developed and validated through computational experiments. The results demonstrate that enabling multi-visit sortie and simultaneous pickup–delivery operations can significantly reduce logistics costs compared with conventional single-visit or delivery-only strategies. Sensitivity analyses further reveal the critical influence of dynamic traffic conditions on fleet configuration and operational decision making. The findings offer actionable insights for logistics operators and policymakers, illustrating how coordinated UAV–truck collaboration can enhance efficiency, resilience, and environmental sustainability in next-generation urban logistics systems. ER - TY - JOUR TI - Resilient Last-Mile Logistics in Smart Cities Through Multi-Visit and Time-Dependent Drone–Truck Collaboration PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9110782 UR - https://doi.org/10.3390/drones9110782 DA - 2025-11-10 AU - Xiao, Qinxin C1 - School of Business, Sichuan Normal University, Chengdu 610066, China AU - Gao, Jiaojiao C1 - School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China LA - en KW - Truck KW - Sustainability KW - Drone KW - City logistics KW - Supply chain KW - Vehicle routing problem KW - Routing (electronic design automation) KW - Humanitarian Logistics VL - 9 IS - 11 SP - 782 EP - 782 AB - Urban logistics in smart cities are increasingly challenged by congestion, sustainability pressures, and the growing demand for resilient delivery systems. To address these challenges, this study introduces the Multi-Visit Time-Dependent Truck–Drone Routing Problem with simultaneous Pickup and Delivery (MTTRP-PD), a novel framework that integrates three realistic features: (i) drones serving multiple customers per sortie, (ii) time-dependent truck speeds reflecting dynamic traffic conditions, and (iii) synchronized pickup and delivery between trucks and drones. By incorporating these elements, the proposed model provides a more realistic and comprehensive representation of urban air-ground collaborative logistics in the last mile. An optimization framework and an efficient solution approach are developed and validated through computational experiments. The results demonstrate that enabling multi-visit sortie and simultaneous pickup–delivery operations can significantly reduce logistics costs compared with conventional single-visit or delivery-only strategies. Sensitivity analyses further reveal the critical influence of dynamic traffic conditions on fleet configuration and operational decision making. The findings offer actionable insights for logistics operators and policymakers, illustrating how coordinated UAV–truck collaboration can enhance efficiency, resilience, and environmental sustainability in next-generation urban logistics systems. ER - TY - JOUR TI - CODrone: A Comprehensive Oriented Object Detection Benchmark for UAV PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 0196-2892 T2 - IEEE Transactions on Geoscience and Remote Sensing DO - 10.1109/tgrs.2026.3677376 UR - https://doi.org/10.1109/tgrs.2026.3677376 DA - 2026-01-01 AU - Ye, Kai C1 - Xiamen University AU - Tang, Haidi C1 - Xiamen University AU - Liu, Bo C1 - Xiamen University AU - Dai, Pingyang C1 - Xiamen University AU - Cao, Liujuan C1 - Xiamen University KW - Object detection KW - Benchmark (surveying) KW - Object (grammar) KW - Feature extraction KW - Radar detection KW - Radar tracker KW - Noise (video) VL - 64 SP - 1 EP - 15 ER - TY - JOUR TI - High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 0018-9545 T2 - IEEE Transactions on Vehicular Technology DO - 10.1109/tvt.2025.3573595 UR - https://doi.org/10.1109/tvt.2025.3573595 DA - 2025-05-26 AU - Chen, Xinwei C1 - State Key Laboratory of Space Network and Communications, the Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Zhong, Xiaofeng C1 - State Key Laboratory of Space Network and Communications, the Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Zhang, Zijian C1 - State Key Laboratory of Space Network and Communications, the Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Dai, Linglong C1 - State Key Laboratory of Space Network and Communications, the Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Zhou, Shidong C1 - State Key Laboratory of Space Network and Communications, the Department of Electronic Engineering, Tsinghua University, Beijing, China LA - en KW - Computer science KW - Remote sensing KW - Electronic engineering KW - Artificial intelligence KW - Engineering KW - Geography VL - 74 IS - 10 SP - 16488 EP - 16493 ER - TY - JOUR TI - Rotor position aerodynamics research of a hybrid configuration electric UAV PY - 2025 PB - Emerald Publishing Limited SN - 0002-2667 T2 - Aircraft Engineering and Aerospace Technology DO - 10.1108/aeat-04-2024-0114 UR - https://doi.org/10.1108/aeat-04-2024-0114 DA - 2025-06-10 AU - Wang, Xiaolu C1 - School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China AU - Liu, Wei-Wei C1 - School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China AU - Chen, Changning C1 - School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China AU - Liu, Yingyu C1 - School of Aerospace Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China LA - en KW - Aerodynamics KW - Rotor (electric) KW - Position (finance) KW - Aerospace engineering KW - Engineering KW - Computer science KW - Mechanical engineering KW - Aeronautics VL - 97 IS - 6 SP - 739 EP - 749 ER - TY - GEN TI - Design of a Distributed UAV Traffic Management System for Smart Air Service PY - 2023 DO - 10.1109/icite59717.2023.10733868 UR - https://doi.org/10.1109/icite59717.2023.10733868 DA - 2023-10-28 AU - Wu, Meiliwen C1 - Civil Aviation Management Institute of China,CAAC Key laboratory of General Aviation Operation,Beijing,China AU - Lv, Renli C1 - Civil Aviation Management Institute of China,CAAC Key laboratory of General Aviation Operation,Beijing,China AU - Guan, Xiangmin C1 - Civil Aviation Management Institute of China,CAAC Key laboratory of General Aviation Operation,Beijing,China LA - en KW - Computer science KW - Air traffic control KW - Service (business) KW - Computer network KW - Engineering KW - Aerospace engineering KW - Business SP - 160 EP - 164 ER - TY - JOUR TI - Optimization for Urban Low-Altitude Logistics Using an Improved Whale Optimization Algorithm PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app16094385 UR - https://doi.org/10.3390/app16094385 DA - 2026-04-30 AU - Yang, Song C1 - School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China AU - Huang, Yaxuan C1 - School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China AU - Zhou, Hu C1 - School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China LA - en KW - Robustness (evolution) KW - Optimization algorithm KW - Convergence (economics) KW - Baseline (sea) KW - Sensitivity (control systems) KW - Wind speed KW - Wind power VL - 16 IS - 9 SP - 4385 EP - 4385 AB - Urban low-altitude logistics is increasingly constrained by obstacle-rich city morphology and wind-induced flight disturbances, which makes conventional path-planning methods insufficient for simultaneously ensuring efficiency, feasibility, and robustness. To address this issue, this study proposes an improved whale optimization algorithm (IWOA) for wind-field-coupled three-dimensional UAV path planning in urban environments. A voxel-based urban model is established, and the planning objective integrates flight time, energy consumption, wind-field penalty, and path smoothness. On the basis of the original whale optimization algorithm, the proposed method introduces a wind-field-guided local adjustment operator, adaptive convergence control, elite preservation, large-scale mutation, and feasibility repair. The proposed method is evaluated through a structured simulation framework comprising four scenarios: a baseline case, urban density variation, complex wind-field variation, and multi-destination delivery. The results show that IWOA consistently yields the lowest composite cost among the compared algorithms and exhibits better path smoothness, stronger wind adaptation, and earlier convergence stability. In the baseline case, the total cost of IWOA is reduced by 17.3%, 13.1%, and 6.7% relative to A*, GA, and WOA, respectively. Under the high-density urban environment and the complex wind field, IWOA also maintains the best performance, indicating stronger robustness under increased environmental difficulty. Sensitivity analyses further show that wind speed and wind direction have pronounced effects on the total cost, while the energy coefficient mainly affects the energy-related component. These results demonstrate that the proposed framework provides an effective and practically relevant solution for urban low-altitude UAV logistics path planning. ER - TY - JOUR TI - High-Density PCB for On-Edge AI: Energy Harvesting, Thermal Management, and Sensor Fusion for UAVs in Clinical–Urban Missions PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2079-9292 T2 - Electronics DO - 10.3390/electronics15091885 UR - https://doi.org/10.3390/electronics15091885 DA - 2026-04-29 AU - Bibbò, Luigi C1 - Department of Civil, Energetic, Environmental and Material Engineering, Mediterranea University of Reggio Calabria, I-89122 Reggio Calabria, Italy AU - Bilotta, Giuliana C1 - Department of Civil, Energetic, Environmental and Material Engineering, Mediterranea University of Reggio Calabria, I-89122 Reggio Calabria, Italy AU - Angiulli, Giovanni C1 - Department of Information Engineering, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, I-89122 Reggio Calabria, Italy LA - en KW - Payload (computing) KW - Energy consumption KW - Jerk KW - Photovoltaic system KW - Key (lock) KW - Latency (audio) KW - Global Positioning System KW - Porting KW - Energy management VL - 15 IS - 9 SP - 1885 EP - 1885 AB - Unmanned aerial vehicles (UAVs) for urban and clinical–logistics missions operate under severe constraints in onboard energy, computation, and payload integrity. Addressing these challenges requires not only advanced algorithms but also a tight integration between embedded hardware, energy management, perception, and decision-making. This paper presents a unified UAV platform based on a system-level hardware–software co-design. First, a compact six-layer PCB (85 mm × 55 mm) integrates an NVIDIA Jetson Orin for on-edge artificial intelligence and a dedicated microcontroller for real-time flight control, with explicit power-domain separation, thermal management via arrays, and physical isolation of sensitive sensors. Second, a hybrid energy system combines LiPo batteries with perovskite photovoltaic cells and an MPPT stage with experimentally measured efficiency (94.5%), enabling stable operation under variable irradiance conditions. Third, an autonomous navigation strategy based on a Dueling Double Deep Q Network with Prioritized Experience Replay learns energy-efficient trajectories while explicitly incorporating payload thermal deviation (ΔT) and mechanical jerk into the reward function, thereby supporting clinically safe transport. Experimental validation on the physical platform includes onboard power and latency measurements, statistical evaluation across training and deterministic execution, and mission-level key performance indicators. Results show an average reduction of 18.4% in total energy consumption and a 12.1% increase in operational coverage under representative urban scenarios, with end-to-end decision latency below 50 ms. These findings demonstrate that a tightly integrated design of embedded hardware, hybrid energy management, and clinical-aware reinforcement learning enables robust, efficient, and application-ready UAV systems for urban and healthcare missions. ER - TY - JOUR TI - Multi-objective optimization of a truck–drone delivery system for fair and efficient humanitarian logistics under disruption and disinformation PY - 2025 PB - Elsevier BV SN - 0360-8352 T2 - Computers & Industrial Engineering DO - 10.1016/j.cie.2025.111786 UR - https://doi.org/10.1016/j.cie.2025.111786 DA - 2025-12-24 AU - Khameneh, Ramin C1 - Department of Systems Engineering, Stevens Institute of Technology, NJ, USA AU - Ghorbani-Renani, Nafiseh C1 - Department of Systems Engineering, Stevens Institute of Technology, NJ, USA AU - Ramírez-Márquez, José C1 - Department of Systems Engineering, Stevens Institute of Technology, NJ, USA LA - en KW - Humanitarian aid KW - Multi-objective optimization KW - Stackelberg competition KW - Humanitarian Logistics KW - Pareto principle KW - Sorting KW - Drone KW - Resilience (materials science) KW - Genetic algorithm VL - 213 SP - 111786 EP - 111786 ER - TY - JOUR TI - Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability Under Diverse Urban Scenarios PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10040269 UR - https://doi.org/10.3390/drones10040269 DA - 2026-04-08 AU - Liu, Yinying C1 - School of Automation, Chongqing University, Chongqing 400044, China AU - Liu, Jianmeng C1 - School of Automation, Chongqing University, Chongqing 400044, China AU - Shi, X. C1 - School of Automation, Chongqing University, Chongqing 400044, China AU - Tang, Cheng C1 - Mary Frances Early College of Education, University of Georgia, Athens, GA 30602, USA LA - en KW - Scheduling (production processes) KW - Markov decision process KW - Markov chain KW - Job shop scheduling KW - Drone KW - Service (business) KW - Linear programming VL - 10 IS - 4 SP - 269 EP - 269 AB - Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. ER - TY - GEN TI - Smart Self-Diagnosis Method for GPS Attacks and Safety Faults in UAVs PY - 2024 DO - 10.1109/icuas60882.2024.10556910 UR - https://doi.org/10.1109/icuas60882.2024.10556910 DA - 2024-06-04 AU - Ferrão, Isadora C1 - University of São Paulo AU - de Oliveira, André C1 - Federal University of Juiz de Fora AU - Espès, David C1 - Université de Bretagne Occidentale AU - Dezan, Catherine C1 - Université de Bretagne Occidentale AU - Branco, Kalinka C1 - University of São Paulo LA - en KW - Global Positioning System KW - Computer science KW - Computer security KW - Real-time computing KW - Embedded system KW - Telecommunications SP - 1301 EP - 1308 ER - TY - JOUR TI - A study on optimizing the location selection problem for urban UAV distribution while accounting for obstacles PY - 2024 T2 - Research Square DO - 10.21203/rs.3.rs-5301607/v1 UR - https://doi.org/10.21203/rs.3.rs-5301607/v1 DA - 2024-10-25 AU - Menghan, Sun C1 - Harbin Engineering University AU - Yuance, Yu C1 - Harbin Engineering University AU - Luyao, He C1 - Harbin Engineering University AU - Yang, Yong C1 - Harbin Engineering University AU - Zailin, Yang C1 - Harbin Engineering University LA - en KW - Selection (genetic algorithm) KW - Distribution (mathematics) KW - Computer science KW - Geography KW - Operations research KW - Mathematics KW - Artificial intelligence ER - TY - CHAP TI - Urban air mobility PY - 2020 DO - 10.4324/9781351212991-4 UR - https://doi.org/10.4324/9781351212991-4 DA - 2020-07-10 AU - Cureton, Paul LA - en KW - Environmental science KW - Geography SP - 108 EP - 147 ER - TY - JOUR TI - A Dual-Layer Optimization Framework for Multi-UAV Delivery Scheduling in Multi-Altitude Urban Airspace PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10030203 UR - https://doi.org/10.3390/drones10030203 DA - 2026-03-14 AU - Wang, Yong C1 - Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China C1 - Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden C1 - School of Management, Wuhan University of Science and Technology, Wuhan 430081, China AU - Leixin, Jiuye C1 - School of Management, Wuhan University of Science and Technology, Wuhan 430081, China AU - Zhang, Dayuan C1 - School of Management, Wuhan University of Science and Technology, Wuhan 430081, China AU - Ji, Yuxuan C1 - School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China AU - Wang, Xi C1 - Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden AU - Wang, Lihui C1 - Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden LA - en KW - Obstacle KW - Scheduling (production processes) KW - Convergence (economics) KW - Task (project management) KW - Constraint (computer-aided design) KW - Path (computing) KW - Motion planning KW - Layer (electronics) VL - 10 IS - 3 SP - 203 EP - 203 AB - Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality. ER - TY - JOUR TI - A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles PY - 2019 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones3030066 UR - https://doi.org/10.3390/drones3030066 DA - 2019-08-24 AU - Khoufi, Ines C1 - SAMOVAR, CNRS, Télécom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, France C1 - CNRS - Centre National de la Recherche Scientifique (France) C1 - IP Paris - Institut Polytechnique de Paris (Route de Saclay, 91120 Palaiseau Cedex, France - France) C1 - R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité (TELECOM Sudparis 9 rue Charles Fourier 91011 EVRY - France) C1 - TSP - RS2M - Département Réseaux et Services Multimédia Mobiles (Télécom SudParis - 9 rue Charles Fourier - 91011 Évry cedex - France) AU - Laouiti, Anis C1 - SAMOVAR, CNRS, Télécom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, France C1 - CNRS - Centre National de la Recherche Scientifique (France) C1 - IP Paris - Institut Polytechnique de Paris (Route de Saclay, 91120 Palaiseau Cedex, France - France) C1 - R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité (TELECOM Sudparis 9 rue Charles Fourier 91011 EVRY - France) C1 - TSP - RS2M - Département Réseaux et Services Multimédia Mobiles (Télécom SudParis - 9 rue Charles Fourier - 91011 Évry cedex - France) AU - Adjih, Cédric C1 - Inria, Saclay Ile-de-France Research Centre, 91120 Palaiseau, France C1 - INFINE-POST - INFormation NEtworks (1 rue Honoré d'Estienne d'Orves Bâtiment Alan Turing Campus de l'École Polytechnique 91120 Palaiseau - France) LA - en KW - Travelling salesman problem KW - Drone KW - Computer science KW - Context (archaeology) KW - Vehicle routing problem KW - Routing (electronic design automation) KW - Class (philosophy) KW - Range (aeronautics) KW - Operations research KW - Popularity KW - Artificial intelligence KW - Engineering KW - Geography KW - Computer network KW - Aerospace engineering VL - 3 IS - 3 SP - 66 EP - 66 AB - The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, and so forth. They have opened new possibilities such as allowing operation in otherwise difficult or hazardous areas, for instance. For all applications, one foremost concern is the selection of the paths and trajectories of UAVs, and at the same time, UAVs control comes with many challenges, as they have limited energy, limited load capacity and are vulnerable to difficult weather conditions. Generally, efficiently operating a drone can be mathematically formalized as a path optimization problem under some constraints. This shares some commonalities with similar problems that have been extensively studied in the context of urban vehicles and it is only natural that the recent literature has extended the latter to fit aerial vehicle constraints. The knowledge of such problems, their formulation, the resolution methods proposed—through the variants induced specifically by UAVs features—are of interest for practitioners for any UAV application. Hence, in this study, we propose a review of existing literature devoted to such UAV path optimization problems, focusing specifically on the sub-class of problems that consider the mobility on a macroscopic scale. These are related to the two existing general classic ones—the Traveling Salesman Problem and the Vehicle Routing Problem. We analyze the recent literature that adapted the problems to the UAV context, provide an extensive classification and taxonomy of their problems and their formulation and also give a synthetic overview of the resolution techniques, performance metrics and obtained numerical results. ER - TY - JOUR TI - Examining the acceptance of drone delivery services among Chinese consumers: A perspective from urban and rural areas PY - 2025 PB - Public Library of Science SN - 1932-6203 T2 - PLoS ONE DO - 10.1371/journal.pone.0333422 UR - https://doi.org/10.1371/journal.pone.0333422 DA - 2025-09-29 AU - Wu, Jingqiong C1 - School of Traffic Engineering, Kunming University of science and Technology, Kunming, China AU - Chen, Ziwei C1 - School of Traffic Engineering, Kunming University of science and Technology, Kunming, China AU - Zhang, Zhe C1 - School of Traffic Engineering, Kunming University of science and Technology, Kunming, China AU - Cen, Mingrui C1 - School of Traffic Engineering, Kunming University of science and Technology, Kunming, China LA - en KW - Usability KW - Context (archaeology) KW - Multilevel model KW - Regression analysis KW - Perspective (graphical) KW - Rural area KW - Willingness to pay KW - Drone KW - Risk perception VL - 20 IS - 9 SP - e0333422 EP - e0333422 AB - Drones hold considerable promise in the realms of logistics and distribution, as they are expected to enhance the speed and efficiency of courier deliveries while simultaneously reducing associated costs. This study investigates Chinese consumers' willingness to accept drone delivery services in the context of urban-rural disparities, conducting a comparative analysis to identify similarities and differences in the influencing factors across residential settings. The Technology Acceptance Model and the Environmental Literacy Model were extended incrementally using an approach to more comprehensively capture the factors influencing consumers' willingness to adopt drone delivery services. Based on these extended frameworks, nine research hypotheses were proposed and empirically tested through a questionnaire survey. Data collected from 498 respondents was analyzed using a ridge regression model. Furthermore, using an additional independent test set of 102 questionnaires, we confirmed that ridge regression outperformed multiple linear regression in reducing error metrics, thereby demonstrating superior predictive accuracy and robustness. The results reveal significant regional differences in factors influencing consumers' willingness to accept drone delivery services. In urban areas, environmental cognition, perceived usefulness, perceived ease of use and social norm significantly contribute positively to acceptance levels while perceived risk has a significant negative impact. Furthermore, policy support, UAV-environmental cognition, health safety, and service performance exert a relatively minor yet notable positive influence on acceptance levels within urban settings. In contrast, for rural consumers, environmental cognition, perceived usefulness, and perceived ease of use were key positive factors. UAV-environmental cognition and service performance also emerged as major contributors, exerting particularly strong positive effects on acceptance. Perceived risk negatively affected acceptance but to a lesser extent than in urban areas, whereas health safety, policy support, and social norm show no significant impact. Given these pronounced urban-rural disparities, logistics providers and policymakers are advised to implement context-specific, differentiated strategies: urban areas should focus on risk mitigation, regulatory enhancement, and policy incentives, whereas rural areas would benefit from increased awareness campaigns, pilot demonstrations, and infrastructure investment. Such targeted approaches are essential for optimizing the adoption and societal benefits of drone delivery services across diverse consumer groups. ER - TY - JOUR TI - A Comprehensive Review of Building the Resilience of Low-Altitude Logistics: Key Issues, Challenges, and Strategies PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su18010461 UR - https://doi.org/10.3390/su18010461 DA - 2026-01-02 AU - Yang, Jingshuai C1 - School of Automobile, Chang’an University, Xi’an 710064, China AU - Xu, Haofeng C1 - School of Automobile, Chang’an University, Xi’an 710064, China LA - en KW - Orchestration KW - Resilience (materials science) KW - Key (lock) KW - Drone KW - Critical infrastructure KW - Vulnerability (computing) KW - Corporate governance KW - Scalability KW - Urban resilience VL - 18 IS - 1 SP - 461 EP - 461 AB - Low-altitude logistics (LAL), supported by unmanned aerial vehicles (UAVs) and emerging urban air mobility operations within the low-altitude airspace (typically <1000 m), is rapidly reshaping last-mile distribution and time-critical delivery. However, LAL systems remain vulnerable to compound disruptions spanning weather, infrastructure, governance, and cybersecurity. Using a PRISMA-guided protocol, this systematic review synthesizes 1600 peer-reviewed studies published from 2020 to 2025 and combines bibliometric mapping (VOSviewer) with qualitative content analysis to consolidate the knowledge base on low-altitude logistics resilience (LALR). We conceptualize LALR via four coupled pillars, including robustness, adaptability, recoverability, and redundancy. The synthesize evidence across key vulnerability domains consists of platform reliability, communication and infrastructure readiness, regulatory fragmentation, cyber exposure, and weather-driven operational uncertainty. Building on the synthesis, we propose a Technology–Policy–Ecosystem roadmap that links (i) AI-enabled autonomy and risk-aware planning, (ii) adaptive governance tools such as regulatory sandboxes and dynamic airspace/UTM management, and (iii) ecosystem-level interventions, notably public–private partnerships and equity-oriented service design for underserved areas. We further outline a research agenda centered on measurable resilience metrics, activate redundancy design, climate-adaptive UAV operations, and digital-twin-enabled orchestration for scalable and sustainable LAL ecosystems. ER - TY - JOUR TI - Bi-Level Optimization Framework for Urban Low-Altitude UAV Delivery Ensuring Target Level of Safety PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 1524-9050 T2 - IEEE Transactions on Intelligent Transportation Systems DO - 10.1109/tits.2026.3660878 UR - https://doi.org/10.1109/tits.2026.3660878 DA - 2026-02-19 AU - Jiang, Bo C1 - Flight Technology College, Civil Aviation Flight University of China, Guanghan, China AU - Li, Yichao C1 - College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, China AU - Li, Chenglong C1 - Flight Technology College, Civil Aviation Flight University of China, Guanghan, China AU - Zheng, Yuan C1 - School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan, China KW - Trajectory KW - Workload KW - Scheduling (production processes) KW - Key (lock) KW - Task (project management) KW - Computation KW - Path (computing) KW - Constraint (computer-aided design) VL - 27 IS - 7 SP - 8624 EP - 8637 ER - TY - JOUR TI - UAV-based monitoring and urban transport outcomes: evidence from Vietnam’s Low-Altitude Economy policy PY - 2026 PB - Elsevier BV SN - 2213-624X T2 - Case Studies on Transport Policy DO - 10.1016/j.cstp.2026.101892 UR - https://doi.org/10.1016/j.cstp.2026.101892 DA - 2026-06-23 AU - Tran, An C1 - Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam AU - Vu, Thi C1 - VNU University of Economics and Business, Hanoi, Viet Nam AU - Luu, Huu C1 - Academy of Finance, Hanoi, Viet Nam AU - Pham, Thi C1 - VNU Tran Nhan Tong Institute, Hanoi, Viet Nam AU - Bui, Ngoc C1 - Ho Chi Minh City Cadre Academy, Viet Nam AU - Truong, Quoc C1 - Academy of Public Administration and Governance, Hanoi, Viet Nam AU - Le, Khanh C1 - Faculty of Development Economics, VNU University of Economics and Business, Hanoi, Viet Nam AU - Nguyen, Minh C1 - FPT University, Hanoi, Viet Nam LA - en KW - Transport policy KW - Urban policy KW - Urban economics KW - Policy making KW - Public policy VL - 25 SP - 101892 EP - 101892 ER - TY - JOUR TI - Cargo UAVs Pick-Up Systems for Low-Altitude Economy With Communication Quality, Battery Energy, and Time Window Constraints PY - 2025 PB - IEEE Computer Society SN - 1536-1233 T2 - IEEE Transactions on Mobile Computing DO - 10.1109/tmc.2025.3647000 UR - https://doi.org/10.1109/tmc.2025.3647000 DA - 2025-12-23 AU - Chen, Mingjian C1 - College of Computer Science and Electronic Engineering, Hunan University, Changsha, China AU - Yang, Liang C1 - College of Computer Science and Electronic Engineering, Hunan University, Changsha, China AU - Cao, Jiangling C1 - School of Information Engineering, Nanchang University, Nanchang, China AU - Zhu, Guangxu C1 - Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China AU - Yuan, Weijie C1 - School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China AU - Jiang, Hongbo C1 - College of Computer Science and Electronic Engineering, Hunan University, Changsha, China AU - Niyato, Dusit C1 - College of Computing and Data Science, Nanyang Technological University, Singapore KW - Scalability KW - Baseline (sea) KW - Scheduling (production processes) KW - Software deployment KW - Simulated annealing KW - Robustness (evolution) KW - Trajectory KW - Job shop scheduling VL - 25 IS - 6 SP - 8111 EP - 8128 ER - TY - GEN TI - Mixed Electric Vehicles and UAVs for Last-Mile Delivery Under Ecological Objective PY - 2024 DO - 10.1109/icds62089.2024.10756461 UR - https://doi.org/10.1109/icds62089.2024.10756461 DA - 2024-10-23 AU - Hanaa, Bouhia C1 - ESITH,R&D engineer intern, ECC, Computer science engineer,Casablanca,Morocco AU - Benhra, Jamal C1 - OSIL ENSEM, Hassan II University of Casablanca,LARILE Laboratory,Casablanca,Morocco LA - en KW - Mile KW - Last mile (transportation) KW - Computer science KW - Environmental science KW - Ecology KW - Transport engineering KW - Geography KW - Engineering KW - Biology SP - 1 EP - 8 ER - TY - JOUR TI - Multi-UAV Trajectory Planning Based on a Two-Layer Algorithm Under Four-Dimensional Constraints PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9070471 UR - https://doi.org/10.3390/drones9070471 DA - 2025-07-01 AU - Yang, Yong C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 610021, China AU - Fu, Yujie C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 610021, China AU - Xin, Renlin C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 610021, China AU - Feng, Weiqi C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 610021, China AU - Xu, Kaijun C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Chengdu 610021, China LA - en KW - Trajectory KW - Layer (electronics) KW - Algorithm KW - Computer science KW - Trajectory optimization KW - Mathematical optimization KW - Mathematics KW - Physics KW - Materials science VL - 9 IS - 7 SP - 471 EP - 471 AB - With the rapid development of the low-altitude economy and smart logistics, unmanned aerial vehicles (UAVs), as core low-altitude platforms, have been widely applied in urban delivery, emergency rescue, and other fields. Although path planning in complex environments has become a research hotspot, optimization and scheduling of UAVs under time window constraints and task assignments remain insufficiently studied. To address this issue, this paper proposes an improved algorithmic framework based on a two-layer structure to enhance the intelligence and coordination efficiency of multi-UAV path planning. In the lower layer path planning stage, considering the limitations of the whale optimization algorithm (WOA), such as slow convergence, low precision, and susceptibility to local optima, this study integrates a backward learning mechanism, nonlinear convergence factor, random number generation strategy, and genetic algorithm principle to construct an improved IWOA. These enhancements significantly strengthen the global search capability and convergence performance of the algorithm. For upper layer task assignment, the improved ALNS (IALNS) addresses local optima issues in complex constraints. It integrates K-means clustering for initialization and a simulated annealing mechanism, improving scheduling rationality and solution efficiency. Through the coordination between the upper and lower layers, the overall solution flexibility is improved. Experimental results demonstrate that the proposed IALNS-IWOA two-layer method outperforms the conventional IALNS-WOA approach by 7.30% in solution quality and 7.36% in environmental adaptability, effectively improving the overall performance of UAV trajectory planning. ER - TY - GEN TI - Research on Distribution Route Planning Model and Algorithm of Unmanned Aerial Vehicle (UAV) based on Improved Multi-objective Genetic Algorithm PY - 2021 DO - 10.1145/3469213.3470360 UR - https://doi.org/10.1145/3469213.3470360 DA - 2021-05-28 AU - Changjiang, Gao C1 - Nanjing University, China AU - Yinan, Chen C1 - Guangdong University of Technology, China AU - Xiaohai, Tang C1 - Chengdu University of Information Technology, China LA - en KW - Sorting KW - Genetic algorithm KW - Computer science KW - Pareto principle KW - Motion planning KW - Population KW - Multi-objective optimization KW - Path (computing) KW - Mathematical optimization KW - Algorithm KW - Artificial intelligence KW - Machine learning KW - Mathematics SP - 1 EP - 7 ER - TY - GEN TI - Deep Multi Agent Reinforcement Learning Based Decentralized Swarm UAV Control Framework for Persistent Surveillance PY - 2022 PB - Springer Science+Business Media SN - 1876-1100 T2 - Lecture notes in electrical engineering DO - 10.1007/978-981-19-2635-8_70 UR - https://doi.org/10.1007/978-981-19-2635-8_70 DA - 2022-09-29 AU - Kaliappan, Vishnu C1 - Konkuk Aerospace Design-Airworthiness Research Institute, Konkuk University, Seoul, South Korea AU - Nguyễn, Tuấn C1 - Konkuk Aerospace Design-Airworthiness Research Institute, Konkuk University, Seoul, South Korea AU - Jeon, Sangwoo C1 - Department of Computer Science and Engineering, Konkuk University, Seoul, South Korea AU - Lee, Jae C1 - Department of Aerospace Information Engineering, Konkuk University, Seoul, South Korea AU - Min, Dugki C1 - Department of Computer Science and Engineering, Konkuk University, Seoul, South Korea LA - en KW - Reinforcement learning KW - Computer science KW - Swarm behaviour KW - Variety (cybernetics) KW - Control (management) KW - Collision avoidance KW - Plan (archaeology) KW - Swarm intelligence KW - Autonomous agent KW - Artificial intelligence KW - Distributed computing KW - Machine learning KW - Computer security KW - Collision KW - Particle swarm optimization SP - 951 EP - 962 ER - TY - JOUR TI - Unmanned Aerial Vehicle Logistics Distribution Path Planning Based on Improved Grey Wolf Optimization Algorithm PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2073-8994 T2 - Symmetry DO - 10.3390/sym17122178 UR - https://doi.org/10.3390/sym17122178 DA - 2025-12-18 AU - Feng, Wei-Qi C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Guanghan 618307, China AU - Yang, Yong C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Guanghan 618307, China AU - Yang, Linfeng C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Guanghan 618307, China AU - Fu, Yujie C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Guanghan 618307, China AU - Xu, Kaijun C1 - School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China C1 - Sichuan Provincial Engineering Research Center of Domestic Civil Aircraft Flight and Operation Support, Guanghan 618307, China LA - en KW - Motion planning KW - Path (computing) KW - Population KW - Local optimum KW - Particle swarm optimization KW - Convergence (economics) KW - Smoothness KW - Chaotic KW - Fitness function VL - 17 IS - 12 SP - 2178 EP - 2178 AB - Aiming to solve the bottlenecks of the traditional Grey Wolf Optimizer (GWO) in UAV three-dimensional path planning—including uneven initial population distribution, slow convergence speed, and proneness to local optima—this paper proposes an improved algorithm (CPS-GWO) that integrates the Kent chaotic map with Particle Swarm Optimization (PSO) to mitigate these limitations. To enhance the diversity of the initial population, the Kent chaotic map is employed, as ergodicity ensures the symmetric distribution of the initial population, expanding search coverage; meanwhile, a nonlinear adaptive strategy is adopted to dynamically adjust the control parameter a, enabling flexible search behaviour. Furthermore, the grey wolf position update rule is optimized by incorporating the inertia weight and social learning mechanism of PSO, which strengthens the algorithm’s ability to balance exploration and exploitation. Additionally, a multi-objective comprehensive cost function is constructed, encompassing path length, collision penalty, height constraints, and path smoothness, to fully align with the practical demands of UAV path planning. To validate the performance of CPS-GWO, a three-dimensional urban simulation environment is established on the MATLAB platform. Comparative experiments with different population sizes are conducted, with the traditional GWO as the benchmark. The results demonstrate that, compared with the original GWO, (1) the average fitness of CPS-GWO is significantly reduced by 31.30–38.53%; (2) the path length is shortened by 15.62–22.12%; (3) path smoothness is improved by 43.44–51.52%; and (4) the fitness variance is only 9.58–12.16% of that of the traditional GWO, indicating notably enhanced robustness. Consequently, the proposed CPS-GWO effectively balances global exploration and local exploitation capabilities, thereby providing a novel technical solution for efficient path planning in UAV logistics and distribution under complex urban environments, which holds important engineering application value. ER - TY - JOUR TI - PHOENIX: A Hybrid Metaheuristic Framework for Multi-UAV Collaborative Trajectory Planning in Complex Three-Dimensional Environments PY - 2025 PB - Springer Science+Business Media SN - 2198-6053 T2 - Complex & Intelligent Systems DO - 10.1007/s40747-025-02196-x UR - https://doi.org/10.1007/s40747-025-02196-x DA - 2025-12-20 AU - Wang, Ershen C1 - College of Civil Aviation, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China C1 - College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China AU - Xu, Hu C1 - College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China AU - Ji, Guipeng C1 - College of Aerospace Engineering, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China AU - Yu, Tengli C1 - College of Aerospace Engineering, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China AU - Xu, Song C1 - College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China AU - Liu, Fei C1 - CAAC Key laboratory of General Aviation Operation, Civil Aviation Management Institute, Beijing, 100102, Beijing, China AU - Li, Fan C1 - Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University, Beijing, 618307, Sichuan, China LA - en KW - Metaheuristic KW - Particle swarm optimization KW - Robustness (evolution) KW - Ant colony optimization algorithms KW - Population KW - Motion planning KW - Swarm intelligence KW - Computational intelligence KW - Evolutionary algorithm VL - 12 IS - 2 AB - Multi-unmanned aerial vehicle (UAV) collaborative trajectory planning in complex three-dimensional (3D) environments faces critical challenges in real-time coordination, including balancing path optimality, collision avoidance, and computational efficiency under dynamic obstacle fields. While existing metaheuristic algorithms exhibit limitations in convergence speed and adaptability to high-density obstacles ( \( > 0.8 threats/km^{3}\) ), this study introduces PHOENIX—a hybrid metaheuristic framework that synergizes the crested porcupine optimizer (CPO) and ant lion optimizer (ALO) through a dual-layer cooperative mechanism. Three key innovations are proposed: (1) A hierarchical flight protection zone model compliant with RTCA DO-263 standards, dynamically integrating CPO’s collision-avoidance strategies with real-time risk prediction; (2) A chaos-enhanced initialization strategy employing interval-constrained logistic mapping, which increases population diversity by 32.7% (quantified via Shannon entropy, \( p<0.05\) ); (3) An adaptive weight adjustment mechanism with nonlinear decay, achieving 23.5% and 18.7% faster convergence than particle swarm optimization (PSO) and grey wolf optimizer (GWO), respectively. Extensive evaluations on LANDSAT 9-derived 3D terrains demonstrate PHOENIX’s superiority: it reduces average path length by 6.2%, decreases threat exposure by 7.3%, and improves trajectory smoothness by 8.9% compared with state-of-the-art benchmarks. AirSim-based simulations with five UAVs validate 100% collision-free success rates in dynamic urban scenarios, outperforming CPO and ALO in robustness ( \( p<0.01\) ). The framework effectively resolves exploration-exploitation dilemmas while maintaining computational efficiency ( \(<50ms \) per planning cycle), offering a scalable solution for mission-critical applications such as disaster response and smart city logistics. By bridging bio-inspired optimization with complex system dynamics, this work advances intelligent autonomous system research. ER - TY - GEN TI - Wind Tunnel Measurement of the Urban Wind Field for Flight Path Planning of Unmanned Aerial Vehicles PY - 2024 DO - 10.2514/6.2024-2510 UR - https://doi.org/10.2514/6.2024-2510 DA - 2024-01-04 AU - Frey, Juergen C1 - Technische Universitat Dresden AU - Rienecker, Hannes C1 - Technische Universitat Dresden AU - Schubert, Sebastian C1 - Technische Universitat Dresden AU - Hildebrand, Veit C1 - Technische Universitat Dresden AU - Pfifer, Harald C1 - Technische Universitat Dresden LA - en KW - Wind tunnel KW - Meteorology KW - Motion planning KW - Environmental science KW - Wind speed KW - Aerospace engineering KW - Field (mathematics) KW - Remote sensing KW - Wind power KW - Aeronautics KW - Marine engineering KW - Computer science KW - Engineering KW - Geography KW - Electrical engineering ER - TY - JOUR TI - Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction PY - 2024 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app14135712 UR - https://doi.org/10.3390/app14135712 DA - 2024-06-29 AU - Zhang, Jian C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Li, Zong-Xiao C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Luo, Xinyue C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Zhao, Yifei C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Lu, Fei C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Separation (statistics) KW - Track (disk drive) KW - Computer science KW - Position (finance) KW - Machine learning VL - 14 IS - 13 SP - 5712 EP - 5712 AB - In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs. ER - TY - GEN TI - Identification of Critical Success Factor for Sustainable Supply Chain Management Drone Logistics in Indonesia with ISM (Interpretive Structural Modeling) Approach PY - 2021 DO - 10.46254/an11.20211173 UR - https://doi.org/10.46254/an11.20211173 DA - 2021-03-07 AU - Khofiyah, Nida C1 - ) Master Program of Industrial Engineering Department , Faculty of Engineering , Universitas Sebelas Maret , Surakarta , Jl. Ir. Sutami , 36 A , Surakarta , Indonesia C1 - ) Master Program of Industrial Engineering Department, Faculty of Engineering, Universitas Sebelas Maret, Surakarta, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia AU - Sutopo, Wahyudi C1 - ) University Centre of Excellence for Electrical Energy Storage Technology C1 - , AU - Hisjam, Muhammad C1 - ) Research Group Industrial Engineering and Techno-Economic , Department of Industrial Engineering , Faculty of Engineering Universitas Sebelas Maret , Surakarta , Jl. Ir. Sutami , 36 A , Surakarta , Indonesia C1 - ) Research Group Industrial Engineering and Techno-Economic, Department of Industrial Engineering, Faculty of Engineering Universitas Sebelas Maret, Surakarta, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia LA - en KW - Drone KW - Supply chain KW - Identification (biology) KW - Supply chain management KW - Critical success factor KW - Business KW - Process management KW - Factor (programming language) KW - Computer science KW - Systems engineering KW - Knowledge management KW - Risk analysis (engineering) KW - Engineering KW - Marketing AB - Infrastructure Indonesia in building national competitiveness and to support the implementation of the Master Plan for the Acceleration and Expansion of Indonesian Economic Development 2011 -2025.The main thing is logistics in urban areas, because urban logistics has a function as a link in national networks as well as global networks.The main cause in urban areas is road congestion, resulting in delays, in addition to environmental impacts such as carbon dioxide emissions.To overcome this, this study analyzes a new mode of transportation with new technology, namely drones.A drone is an unmanned aerial vehicle or Unmanned Aerial Vehicle (UAV).This drone technology can be a solution for the logistics system to be efficient.A sustainable supply chain is one that is better at identifying current and future environmental and social impacts and finding ways to reduce them.Environmental and social impacts are important in this industrial 4.0 era, especially for new technologies that will enter the Indonesian market.This research focuses on the objective of identifying the critical success factors (CSF) to implement the sustainability of the application of logistic drones as a mode of delivery in urban areas in Indonesia, to determine the contextual relationship between CSFs.The method used in this study is to use a structural hierarchy model (ISM).The results obtained were that there were 8 levels out of a total of 18 CSFs identified.Level 8, the most important is the global marketing factor, where this factor aims to provide a competitive advantage in the global marketing space by implementing a green supply chain.These results can be a recommendation to the government and business people to focus on the most influential factors for the success of a logistics drone business in Indonesia. ER - TY - JOUR TI - Intelligent 5G-Aided UAV Positioning in High-Density Environments Using Neural Networks for NLOS Mitigation PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2226-4310 T2 - Aerospace DO - 10.3390/aerospace12060543 UR - https://doi.org/10.3390/aerospace12060543 DA - 2025-06-15 AU - Mousa, Morad C1 - School of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK AU - Al-Rubaye, Saba C1 - School of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK LA - en KW - Non-line-of-sight propagation KW - Artificial neural network KW - Computer science KW - Artificial intelligence KW - Wireless KW - Telecommunications VL - 12 IS - 6 SP - 543 EP - 543 AB - The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements. ER - TY - JOUR TI - RETRACTED ARTICLE: Unraveling Urban Network Dynamics with Complex Network Modeling: a Case Study of Chengdu, China PY - 2024 PB - Springer Science+Business Media SN - 1868-7865 T2 - Journal of the Knowledge Economy DO - 10.1007/s13132-023-01603-3 UR - https://doi.org/10.1007/s13132-023-01603-3 DA - 2024-01-17 AU - Zou, Xiang C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, Sichuan, China AU - Hu, Peng C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, Sichuan, China AU - Zhang, Jianping C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, Sichuan, China AU - Wu, Qingang C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, Sichuan, China AU - Zhou, Xiaoxia C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Sichuan, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, China C1 - The Second Research Institute of Civil Aviation Administration of China, Chengdu, Sichuan, China LA - en KW - Betweenness centrality KW - Complex network KW - Geography KW - Regional science KW - Node (physics) KW - Economic geography KW - Population KW - Urban planning KW - Clustering coefficient KW - Street network KW - Urbanization KW - Nexus (standard) KW - Computer science KW - Transport engineering KW - Engineering KW - Centrality KW - Civil engineering KW - Economic growth KW - Cluster analysis KW - Artificial intelligence KW - Sociology KW - World Wide Web KW - Mathematics VL - 15 IS - 4 SP - 15652 EP - 15674 ER - TY - GEN TI - Rapid Response Drones: UAV-Based Healthcare Delivery for Critical Situations PY - 2024 DO - 10.1109/icaiccit64383.2024.10912378 UR - https://doi.org/10.1109/icaiccit64383.2024.10912378 DA - 2024-11-28 AU - Hussain, S C1 - Saveetha Engineering College,Department of Electronics and Communication Engineering,Chennai,India AU - Sibirajeshwaran, T C1 - Saveetha Engineering College,Department of Electronics and Communication Engineering,Chennai,India AU - Kishore, Shreya C1 - Saveetha Engineering College,Department of Electronics and Communication Engineering,Chennai,India LA - en KW - Drone KW - Health care KW - Computer science KW - Healthcare delivery KW - Aeronautics KW - Engineering SP - 1229 EP - 1234 ER - TY - JOUR TI - Unmanned Aerial Vehicle Routing Problems: A Literature Review PY - 2020 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app10134504 UR - https://doi.org/10.3390/app10134504 DA - 2020-06-29 AU - Thibbotuwawa, Amila C1 - Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka AU - Bocewicz, Grzegorz C1 - Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland AU - Nielsen, Peter C1 - Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark AU - Banaszak, Zbigniew C1 - Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland LA - en KW - Computer science VL - 10 IS - 13 SP - 4504 EP - 4504 AB - Unmanned aerial vehicle (UAV) routing is transitioning from an emerging topic to a growing research area as the 3D flexible utilization of airspace, promogulated by UAVs, is a potential game changer in solving the urban air mobility challenge by allowing to reshape transportation and logistics in the future. This has revealed a need to classify different types of research and examine the general characteristics of the research area. This research aims to assist in identifying the main topics and emerging research streams and provides a published overview of the current state and contributions to the area of the UAV routing problem (UAVRP) and a general categorization of the vehicle routing problem (VRP) followed by a UAVRP classification with a graphical taxonomy based on the analysis of UAVRP current status. To achieve this, an analysis of the existing research contributions promulgated in this domain is conducted. This analysis is used to identify the current state of UAVRP and the gaps related to the UAVs’ flight dynamics and weather conditions, which significantly influence the fuel consumption of the UAV when modeling the UAVRP. ER - TY - JOUR TI - GIIDS-AR: End-to-end generalized intelligent intrusion detection system with adversarial robustness for heterogeneous UAVs in UAM PY - 2025 PB - Elsevier BV SN - 1389-1286 T2 - Computer Networks DO - 10.1016/j.comnet.2025.111821 UR - https://doi.org/10.1016/j.comnet.2025.111821 DA - 2025-11-08 AU - Kabir, Fahmina C1 - Division of Artificial Intelligence and Software, Ewha Womans University, Seoul, Korea AU - Mowla, Nishat C1 - Department of Industrial Systems, RISE Research Institutes of Sweden, Sundsvall, Sweden AU - Rosenstatter, Thomas C1 - Salzburg University of Applied Sciences, Puch, Salzburg, Austria AU - Doh, Inshil C1 - Department of Cyber Security, Ewha Womans University, Seoul, Korea LA - en KW - Adversarial system KW - Robustness (evolution) KW - Intrusion detection system KW - Wireless KW - Generalization KW - Resilience (materials science) VL - 274 SP - 111821 EP - 111821 AB - This study presents GIIDS-AR, an enhanced version of the Generalized Intelligent Intrusion Detection System (GIIDS), developed to enhance robustness and secure diverse Unmanned Aerial Vehicles (UAVs) in Urban Air Mobility (UAM) while preserving generalization. As UAVs grow vital in logistics, emergency response, and disaster relief, their reliance on wireless communication increases exposure to cyber threats. GIIDS leverages machine learning for cross-platform detection but remains vulnerable to adversarial machine learning (AML) attacks. To assess this, GIIDS was tested under black-box, white-box, and transfer attacks. Accuracy dropped to 72% under black-box and recall to 49.9% under white-box settings. Adversarial training restored original performance improving accuracy to 99.0% and F1 to 99.8%, with AUC reaching 1.00. These evaluations were conducted using cross-dataset splits of live and simulated UAV telemetry, ensuring resilience on previously unseen data. GIIDS-AR retains layered modeling, time-aware feature encoding, and ensemble learning, while incorporating adversarial examples to improve resilience. It demonstrates strong detection performance under diverse attacks and generalizes effectively across heterogeneous UAV platforms. Our findings reveal that generalization techniques inherently contribute to adversarial robustness, positioning GIIDS-AR as one of the first unified UAV IDS frameworks capable of securing UAV networks against evolving cyber threats. ER - TY - JOUR TI - Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark PY - 2025 SN - 2775-2658 T2 - International Journal of Robotics and Control Systems DO - 10.31763/ijrcs.v5i1.1757 UR - https://doi.org/10.31763/ijrcs.v5i1.1757 DA - 2025-02-02 AU - Airlangga, Gregorius AU - Bata, Julius AU - Nugroho, Oskar AU - Sugianto, Lai LA - en KW - Benchmark (surveying) KW - Computer science KW - Motion planning KW - Path (computing) KW - Artificial neural network KW - Algorithm KW - Artificial intelligence KW - Machine learning KW - Computer network KW - Geography KW - Robot KW - Cartography VL - 5 IS - 1 SP - 625 EP - 639 AB - Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning. ER - TY - GEN TI - Research on Model-Based Cooperative Delivery Problem of Trucks and UAVs PY - 2023 DO - 10.1109/dsins60115.2023.10455630 UR - https://doi.org/10.1109/dsins60115.2023.10455630 DA - 2023-11-10 AU - Zhao, Jinchen C1 - Xihua University,Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control,Chengdu,China C1 - Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control, Xihua University, Chengdu, China AU - Lei, Jun C1 - Xihua University,Chengdu,China C1 - Xihua University, Chengdu, China AU - Ye, Mian C1 - Xihua University,Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control,Chengdu,China C1 - Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control, Xihua University, Chengdu, China AU - Zhang, Weiran C1 - Xihua University,Chengdu,China C1 - Xihua University, Chengdu, China AU - Yang, H. C1 - Xihua University,Chengdu,China C1 - Xihua University, Chengdu, China LA - en KW - Truck KW - Computer science KW - Transport engineering KW - Engineering KW - Aerospace engineering SP - 280 EP - 284 ER - TY - JOUR TI - UAV–Rider Collaborative Dispatching Under Stochastic Wind Conditions Considering Nonlinear Energy Dynamics PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10030174 UR - https://doi.org/10.3390/drones10030174 DA - 2026-03-04 AU - Shangguan, Chunxia C1 - College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China AU - Zhang, Churan C1 - College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China AU - Cao, Shouqi C1 - College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China LA - en KW - Wind power KW - Nonlinear system KW - Robustness (evolution) KW - Scheduling (production processes) KW - Stochastic programming KW - Linearization KW - Reliability (semiconductor) KW - Stochastic process KW - Backup VL - 10 IS - 3 SP - 174 EP - 174 AB - To mitigate UAV (unmanned aerial vehicle) range limitation risks and scheduling disruptions caused by complex wind fields in urban instant delivery, this paper proposes a UAV–rider collaborative dispatching framework. By incorporating aerodynamic-based nonlinear energy dynamics, the model accurately characterizes power variations under stochastic wind conditions, significantly enhancing the operational reliability of urban delivery missions. First, an aerodynamic-based nonlinear energy function is constructed, coupling payload, airspeed, and random wind vectors to accurately characterize power variations. Second, a scenario-based two-stage stochastic programming framework is adopted, where the rider’s deterministic path is optimized in the first-stage decision to ensure stability, and the UAV’s scenario-dependent flight plan is resolved in the second stage to adapt to wind uncertainty. An improved branch-and-price (IBP) algorithm is designed to solve this large-scale model, where nonlinear energy is evaluated during label extension in the pricing sub-problem, effectively avoiding linearization errors. The numerical results demonstrate that the proposed framework improves the mission success probability (the likelihood of completing delivery routes without battery exhaustion across all considered wind scenarios) by 25% under strong-wind conditions by effectively avoiding power failure risks. Furthermore, the IBP algorithm outperforms traditional exact solvers by over 40% in solution efficiency for large-scale cases. These findings demonstrate that energy-aware stochastic dispatching significantly improves the reliability and robustness of UAV-assisted last-mile delivery in windy urban environments, thereby providing an effective operational solution for real-world drone delivery logistics. ER - TY - CHAP TI - Drones and UAVs for Rapid Emergency Relief and Last-Mile Delivery PY - 2025 PB - IGI Global SN - 2327-0411 T2 - Advances in computational intelligence and robotics book series DO - 10.4018/979-8-3373-3643-5.ch012 UR - https://doi.org/10.4018/979-8-3373-3643-5.ch012 DA - 2025-08-12 AU - Atluyev, Nazim C1 - Yeditepe University, Ataşehir, Turkey LA - en KW - Drone KW - Mile KW - Last mile (transportation) KW - Aeronautics KW - Medical emergency KW - Geography KW - Engineering KW - Medicine KW - Geodesy KW - Biology SP - 303 EP - 316 ER - TY - JOUR TI - A Review of Last-Mile Delivery Optimization: Strategies, Technologies, Drone Integration, and Future Trends PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9030158 UR - https://doi.org/10.3390/drones9030158 DA - 2025-02-21 AU - Shuaibu, Abdullahi C1 - Department of Computer Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia AU - Mahmoud, Ashraf C1 - Department of Computer Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia C1 - Interdisciplinary Research Center on Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia AU - Sheltami, Tarek C1 - Department of Computer Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia C1 - Interdisciplinary Research Center on Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia LA - en KW - Drone KW - Mile KW - Last mile (transportation) KW - Computer science KW - Aeronautics KW - Engineering KW - Geography KW - Biology KW - Geodesy VL - 9 IS - 3 SP - 158 EP - 158 AB - Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems. ER - TY - JOUR TI - A scientometric review of unmanned aerial vehicles (UAVs) and transportation science PY - 2026 PB - Elsevier BV SN - 2590-1982 T2 - Transportation Research Interdisciplinary Perspectives DO - 10.1016/j.trip.2026.102158 UR - https://doi.org/10.1016/j.trip.2026.102158 DA - 2026-07-27 AU - Grubesic, Tony C1 - Center for Geospatial Sciences, School of Public Policy, University of California at Riverside, 900 University Ave, Riverside, CA 92507, United States LA - en KW - Suite KW - Intersection (aeronautics) KW - Drone KW - Heuristics KW - Transportation infrastructure KW - Intelligent transportation system KW - Aerial survey KW - Field (mathematics) VL - 39 SP - 102158 EP - 102158 AB - The rapid evolution of unmanned aerial vehicles (UAVs) is transforming modern transportation ecosystems. From the development of mathematical models and heuristics to improve UAV-assisted last-mile parcel deliveries to real-time traffic monitoring efforts, UAV technology now permeates nearly every subdomain of transportation science. However, because these innovations are developing both quickly and concurrently, many potential synergies between research fields go unnoticed. This paper leverages a suite of scientometric approaches to provide a comprehensive mapping of the primary subdomains shaping the intersection of unmanned aerial systems and transportation research. The results reveal that while hybrid truck-drone logistics operations remain a focal area for research, urban infrastructure inspection and surveillance applications are also growing. However, these applications are strongly dependent upon advances in localization processes developed in allied fields. The paper concludes with a prospective look at the future of UAVs and transportation research. ER - TY - THES TI - Placement of automated external defibrillators and logistics to facilitate early defibrillation in sudden cardiac arrest PY - 2018 PB - Figshare (United Kingdom) T2 - Figshare DA - 2018-04-12 AU - Fredman, David LA - en KW - Defibrillation KW - Sudden cardiac arrest KW - Automated external defibrillator KW - Ventricular fibrillation KW - Medicine KW - Cardiology KW - Internal medicine KW - Sudden cardiac death KW - Cardiopulmonary resuscitation KW - Resuscitation KW - Emergency medicine AB - Background and aim: Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in many western countries. Much effort is put in to measures to improve survival. Early cardiopulmonary resuscitation (CPR) and the use of an automated external defibrillator (AED) significantly increase the chance of survival. In 2016, 5,312 cases of OHCA were reported to the Swedish Register for Cardiopulmonary Resuscitation (SRCR), but only 577 (11%) survived to 30-days. The bystander CPR rate in Sweden is high (73%), and AEDs are widely spread in all parts of the country; however, the use of public AEDs is low. If the use of AEDs could be increased, more patients could be saved. The aim of this thesis was to investigate, in four separate studies, how logistics and placement of AEDs can help facilitate early defibrillation. Methods and results: Study 1. A prospective study at five emergency dispatch centres in Sweden where dispatchers were given access to the Swedish AED registry and had instructions to refer callers to nearby AEDs in cases of suspected OHCA. Of 3,009 suspected OHCA calls over seven months, only 200 occurred within 100 metres of an AED, and in only two cases did dispatchers referred callers to a nearby AED. AED accessibility (opening hours of the venue) and the fact that the callers often were alone on the scene, were identified as barriers for referral. Study 2. A retrospective analysis of AED installation sites and locations of OHCA in public locations in Stockholm. We used renowned geographic information system (GIS) analyses and a freely available dataset of land use (Urban Atlas). Incidence of OHCA in public locations in “residential areas” was similar to “non-residential” but AED installation was significantly higher in “non-residential areas”. Study 3. An explorative study to investigate the feasibility of using unmanned aerial vehicles (UAV)/Drones to transport AEDs to decrease time to defibrillation. The study included live test flights of a UAV system as well as retrospective GIS analysis of suitable locations for installation of UAVs equipped with AEDs for maximum coverage of OHCA. Study 4. An overview of the Swedish AED registry (SAEDREG) shows a two-fold increase of registered AEDs since 2013 and that the majority (45%) of the n=15,849 AEDs are placed in offices/workplaces. In a select region of Sweden, a survey was directed to the owners of all n=218 AEDs that focused on AED functionality and reasons for not registering the AED in SAEDREG. An additional n=94 AEDs were found through customer registries from AED vendors. AED functionality was high in both groups. Owners of AEDs not registered in SAEDREG was often unaware of the national AED registry or stated difficulties with the registration process as the main cause for not registering AEDs in SAEDREG. Conclusions: Dispatch centres have the potential to refer callers to nearby AEDs at an early stage in OHCA but may need supporting training and software. There is a mismatch between where public OHCA occur and where AEDs are located. Most AEDs are found in offices and workplaces whereas most OHCA occur in residential areas. Drones have the potential to transport AEDs and compensate for prolonged ambulance response time, especially in rural areas. A high quality national AED register is important for increasing general awareness within the community, thus facilitating early defibrillation in OHCA; however, many AEDs are nonregistered or discarded in the validation process. List of scientific papers I. Fredman D, Svensson L, Ban Y, Jonsson M, Hollenberg J, Nordberg P, Ringh M, Rosenqvist M, Lundén M, Claesson A. Expanding the first link in the chain of survival–Experiences from dispatcher referral of callers to AED locations. Resuscitation. 2016 Oct;107:129-34. https://doi.org/10.1016/j.resuscitation.2016.06.022 II. Fredman D, Haas J, Ban Y, Jonsson M, Svensson L, Djarv T, Hollenberg J, Nordberg P, Ringh M, Claesson A. Use of a geographic information system to identify differences in automated external defibrillator installation in urban areas with similar incidence of public out-of-hospital cardiac arrest: a retrospective registry-based study. BMJ Open. 2017 Jun;7:1-9. https://doi.org/10.1136/bmjopen-2016-014801 III. Claesson A, Fredman D, Svensson L, Ringh M, Hollenberg H, Nordberg P, Rosenqvist M, Djarv T, Österberg Ö, Lennartsson J, Ban Y. Unmanned aerial vehicles (drones) in out-of-hospital-cardiac-arrest. Scand Journal of Trauma, Resuscitation and Emergency Medicine. 2016 Oct;24(1):124. https://doi.org/10.1186/s13049-016-0313-5 IV. Fredman D, Ringh M, Svensson L, Hollenberg J, Djärv T, Hasselqvist-Ax I, Wagner H, Forsberg S, Nord A, Jonsson M, Claesson A. Experiences and Outcome from the Introduction of a National Swedish Automated External Defibrillator Registry. [Submitted] ER - TY - JOUR TI - Incentive Mechanism and Path Planning for Uav Hitching Over Traffic Networks PY - 2023 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.4326594 UR - https://doi.org/10.2139/ssrn.4326594 DA - 2023-01-01 AU - Lu, Ziyi C1 - Independent - affiliation not provided to SSRN AU - Yu, Na C1 - Independent - affiliation not provided to SSRN AU - Wang, Xuehe C1 - Independent - affiliation not provided to SSRN LA - en KW - Incentive KW - Mechanism (biology) KW - Path (computing) KW - Computer network KW - Business KW - Computer science KW - Microeconomics KW - Economics ER - TY - JOUR TI - Knowledge-aware path planning for UAV parcel delivery and road monitoring PY - 2026 PB - Elsevier BV SN - 0968-090X T2 - Transportation Research Part C Emerging Technologies DO - 10.1016/j.trc.2026.105780 UR - https://doi.org/10.1016/j.trc.2026.105780 DA - 2026-05-29 AU - Xu, Meng C1 - Urban Transport Systems Laboratory (LUTS), École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, CH-1015, Switzerland AU - Geroliminis, Nikolas C1 - Urban Transport Systems Laboratory (LUTS), École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, CH-1015, Switzerland LA - en KW - Scalability KW - Trajectory KW - Baseline (sea) KW - Routing (electronic design automation) KW - Controller (irrigation) KW - Focus (optics) KW - Motion planning VL - 190 SP - 105780 EP - 105780 ER - TY - JOUR TI - Model of the LOS Probability for the UAV Channel and Its Application for Environment Awareness PY - 2022 PB - Institute of Electronics, Information and Communication Engineers SN - 0916-8516 T2 - IEICE Transactions on Communications DO - 10.1587/transcom.2021ebp3159 UR - https://doi.org/10.1587/transcom.2021ebp3159 DA - 2022-01-31 AU - Li, Chi-Min C1 - Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University AU - Lee, Yu-Hsuan C1 - Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University AU - Liao, Yi-Ting C1 - Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University AU - Wang, Pao-Jen C1 - Department of Electrical Engineering, Ming Chi University of Technology LA - en KW - Computer science KW - Channel (broadcasting) KW - Real-time computing KW - Telecommunications VL - E105.B IS - 8 SP - 975 EP - 980 ER - TY - JOUR TI - An Enhanced Adaptive B-spline Smoothing Approach for UAV Path Planning PY - 2025 SN - 2074-904X T2 - International Journal of Intelligent Systems and Applications DO - 10.5815/ijisa.2025.04.01 UR - https://doi.org/10.5815/ijisa.2025.04.01 DA - 2025-07-28 AU - Nikolaiev, Mykola AU - Novotarskyi, Mykhailo LA - en KW - Computer science KW - Smoothing KW - Motion planning KW - Obstacle avoidance KW - Smoothness KW - Mathematical optimization KW - Path (computing) KW - Spline (mechanical) KW - Curvature KW - Energy consumption KW - Control theory (sociology) KW - Real-time computing KW - Algorithm KW - Simulation KW - Control (management) KW - Artificial intelligence KW - Computer vision KW - Mobile robot KW - Mathematics KW - Robot VL - 17 IS - 4 SP - 1 EP - 13 AB - This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents. ER - TY - JOUR TI - Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2332-7731 T2 - IEEE Transactions on Cognitive Communications and Networking DO - 10.1109/tccn.2025.3601015 UR - https://doi.org/10.1109/tccn.2025.3601015 DA - 2025-08-20 AU - Wang, Yixian C1 - College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China C1 - College of Computer Science and Technology, Jilin University, Changchun, China AU - Sun, Geng C1 - College of Computer Science and Technology, Jilin University, Changchun, China AU - Sun, Zemin C1 - College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China C1 - College of Computer Science and Technology, Jilin University, Changchun, China AU - Wang, Jiacheng C1 - College of Computing and Data Science, Nanyang Technological University, Jurong West, Singapore C1 - College of Computing and Data Science, Nanyang Technological University, Singapore AU - Li, Jiahui C1 - College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China C1 - College of Computer Science and Technology, Jilin University, Changchun, China AU - Zhao, Changyuan C1 - College of Computing and Data Science, Nanyang Technological University, Jurong West, Singapore C1 - College of Computing and Data Science, Nanyang Technological University, Singapore AU - Wu, Jing C1 - College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China C1 - College of Computer Science and Technology, Jilin University, Changchun, China AU - Liang, Shuang C1 - School of Information Science and Technology, Northeast Normal University, Changchun, China AU - Yin, Minghao C1 - School of Information Science and Technology, Northeast Normal University, Changchun, China AU - Wang, Pengfei C1 - School of Computer Science and Technology, Dalian University of Technology, Dalian, China AU - Niyato, Dusit C1 - College of Computing and Data Science, Nanyang Technological University, Jurong West, Singapore C1 - College of Computing and Data Science, Nanyang Technological University, Singapore AU - Sun, Sumei C1 - Institute for Infocomm Research, Agency for Science, Technology, and Research (A*STAR), Singapore, Singapore C1 - Institute for Infocomm Research, Agency for Science, Technology, and Research (A*STAR), Singapore AU - Kim, Dong C1 - Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea C1 - Department of Electrical and Compute Engineering, Sungkyunkwan University, Suwon, South Korea LA - en KW - Realization (probability) KW - Computer science KW - Architecture KW - Computer architecture KW - Telecommunications KW - Core (optical fiber) VL - 11 IS - 5 SP - 2788 EP - 2820 ER - TY - JOUR TI - A collaborative bi-level optimization approach for smart logistics: Depot location and unmanned aerial vehicle-based pickup-delivery services PY - 2025 PB - Elsevier BV SN - 0952-1976 T2 - Engineering Applications of Artificial Intelligence DO - 10.1016/j.engappai.2025.113453 UR - https://doi.org/10.1016/j.engappai.2025.113453 DA - 2025-12-05 AU - Zheng, Yu C1 - College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China AU - Gai, Wendong C1 - College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China AU - Zhang, Jing C1 - College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China AU - Zhong, Maiying C1 - College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China LA - en KW - Benchmark (surveying) KW - Cluster analysis KW - Scheduling (production processes) KW - Fuzzy logic KW - Software deployment KW - Key (lock) KW - Convergence (economics) KW - Routing (electronic design automation) KW - Depot VL - 165 SP - 113453 EP - 113453 ER - TY - JOUR TI - ISSA-Based Evaluation Method of Actual Navigation Performance of Rotorcraft Logistics Unmanned Aerial Vehicles PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2226-4310 T2 - Aerospace DO - 10.3390/aerospace12040357 UR - https://doi.org/10.3390/aerospace12040357 DA - 2025-04-17 AU - Liu, Fei C1 - CAAC Key Laboratory of General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China C1 - General Aviation Department, Civil Aviation Management Institute of China, Beijing 100102, China AU - Zhao, Liang C1 - Hangzhou Antwork Technology Co., Ltd., Hangzhou 311100, China AU - Wang, Maolin C1 - CAAC Key Laboratory of General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China C1 - General Aviation Department, Civil Aviation Management Institute of China, Beijing 100102, China AU - Wu, Meiliwen C1 - CAAC Key Laboratory of General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China C1 - General Aviation Department, Civil Aviation Management Institute of China, Beijing 100102, China LA - en KW - Aeronautics KW - Computer science KW - Aerospace engineering KW - Systems engineering KW - Engineering KW - Transport engineering VL - 12 IS - 4 SP - 357 EP - 357 AB - In response to the demand for the evaluation of the actual navigation performance (ANP) of rotorcraft logistics uncrewed aerial vehicle (UAV) navigation systems in urban scenarios, this paper proposes a method for evaluating the ANP of rotorcraft logistics UAVs based on the Improved Sparrow Search Algorithm (ISSA). Taking ANP as the optimization objective, an optimization model for the ANP of rotorcraft logistics UAVs is constructed. Based on the probability of the UAV’s actual position falling within the error circle, an initial population strategy based on probabilistic decision-making is designed, and an adaptive dynamic step size strategy and dynamic compression search strategy are proposed to improve the traditional Sparrow Search Algorithm (SSA), enhancing the algorithm’s ability of optimization and to escape local extremum. The contribution of this paper mainly includes constructing the ANP optimization model and designing the ISSA method. Experimental results show that the proposed method can effectively estimate ANP, achieve onboard performance monitoring and warning, and ensure the required navigation performance (RNP) and flight safety of UAVs. ER - TY - JOUR TI - Location Selection Methods for Urban Terminal Co-Distribution Centers with Air–Land Collaboration PY - 2024 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app14135814 UR - https://doi.org/10.3390/app14135814 DA - 2024-07-03 AU - Qi, Wei C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 213300, China C1 - College of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China AU - Li, Ang C1 - Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China AU - Zhang, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 213300, China LA - en KW - Transport engineering KW - Site selection KW - Distribution (mathematics) KW - Terminal (telecommunication) KW - Distribution center KW - Computer science KW - Scale (ratio) KW - Cluster analysis KW - Selection (genetic algorithm) KW - Operations research KW - Geography KW - Business KW - Engineering KW - Computer network KW - Mathematics VL - 14 IS - 13 SP - 5814 EP - 5814 AB - Urban terminal logistics and distribution enterprises face the problems of duplicated network layouts, high costs, and inefficient urban environments. Recently, collaborative distribution using unmanned aerial vehicles (UAVs) and ground vehicles has been considered as a means of reducing costs and enhancing efficiency, thus overcoming the issues created by the high-density layout of enterprises’ relatively independent networks. This essentially involves constructing an air–ground collaborative common distribution network. To optimize the economic cost and distribution time of network operations, we established a site selection planning model for air–ground cooperative urban co-distribution centers and designed a solution method based on gray wolf optimization with K-means clustering. Taking the Wangsheren area of Jinan City, China, as an example, 15 UAV co-distribution centers and 13 vehicle co-distribution centers were identified. Although the average distribution cost of the network rose by 35–50% compared to traditional terminal distribution, the time saving was 80–85%, greatly improving the high-value-added service capacity. The end-distribution efficiency and customer satisfaction were also enhanced, which fully verifies the feasibility, validity, and applicability of the proposed model. Our approach can be applied to landing sites and the planning and optimization of large-scale commercial operations using logistics UAVs in urban areas. ER - TY - GEN TI - Cooperative Task Assignment of Multi-UAV in Road-network Reconnaissance Using Customized Genetic Algorithm PY - 2021 DO - 10.1109/imcec51613.2021.9482232 UR - https://doi.org/10.1109/imcec51613.2021.9482232 DA - 2021-06-18 AU - Wang, Qiusheng C1 - School of Aerospace Engineering, Beijing Institute of Technology University, Beijing, China AU - Liu, Li C1 - School of Aerospace Engineering, Beijing Institute of Technology University, Beijing, China AU - Tian, Weiyong C1 - School of Aerospace Engineering, Beijing Institute of Technology University, Beijing, China LA - en KW - Task (project management) KW - Computer science KW - Genetic algorithm KW - Travelling salesman problem KW - Real-time computing KW - Algorithm KW - Engineering KW - Machine learning KW - Systems engineering SP - 803 EP - 809 ER - TY - JOUR TI - Last mile logistics applied to the distribution of COVID-19 vaccines: A prospection of good practices PY - 2021 PB - Centre National de la Recherche Scientifique T2 - HAL (Le Centre pour la Communication Scientifique Directe) DA - 2021-07-01 AU - Bentalha, Badr C1 - Ecole Nationale de Commerce et de Gestion De Fès AU - Hmioui, Aziz C1 - Ecole Nationale de Commerce et de Gestion De Fès AU - Alla, Lhoussaine C1 - École nationale des sciences appliquées de Fès = National School of Applied Sciences of Fez LA - fr KW - Prospection KW - Mile KW - Coronavirus disease 2019 (COVID-19) KW - Distribution (mathematics) KW - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) KW - 2019-20 coronavirus outbreak KW - Geography KW - Virology KW - Medicine KW - Archaeology KW - Outbreak KW - Mathematics KW - Geodesy AB - International audience ER - TY - CHAP TI - Challenges of Civil Application of ‘Drones’ for Commercial Purposes in the Logistics and Transportation Sector PY - 2024 PB - Springer International Publishing SN - 2365-0532 T2 - Management and industrial engineering DO - 10.1007/978-3-031-55571-8_9 UR - https://doi.org/10.1007/978-3-031-55571-8_9 DA - 2024-01-01 AU - Raya, Antonio C1 - Department of Organizational Engineering, Business Administration and Statistics, Technical University of Madrid—Universidad Politécnica de Madrid (UPM), 28040, Madrid, Spain AU - Esteve, Joaquín C1 - Department of Constitutional Law, National University of Distance Education—Universidad Nacional de Educación a Distancia (UNED), 28040, Madrid, Spain LA - en KW - Drone KW - Business KW - Engineering KW - Transport engineering KW - Aeronautics SP - 235 EP - 253 ER - TY - JOUR TI - Neural Network-Based Air–Ground Collaborative Logistics Delivery Path Planning with Dynamic Weather Adaptation PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2227-7390 T2 - Mathematics DO - 10.3390/math13172798 UR - https://doi.org/10.3390/math13172798 DA - 2025-08-31 AU - Feng, Linglin C1 - School of Mathematics, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China AU - Cao, Hongmei C1 - School of Mathematics, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China LA - en KW - Adaptation (eye) KW - Artificial neural network KW - Motion planning KW - Computer science KW - Path (computing) KW - Environmental science KW - Meteorology KW - Operations research KW - Engineering KW - Computer network KW - Artificial intelligence KW - Geography KW - Neuroscience KW - Psychology KW - Robot VL - 13 IS - 17 SP - 2798 EP - 2798 AB - The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. ER - TY - JOUR TI - More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2504.20032 UR - https://doi.org/10.48550/arxiv.2504.20032 DA - 2025-04-28 AU - Ye, Kai AU - Tang, Haidi AU - Liu, Bowen AU - Dai, Pingyang AU - Cao, Liujuan AU - Ji, Rongrong LA - en KW - Robustness (evolution) KW - Benchmark (surveying) KW - Object detection KW - Key (lock) KW - Generalization KW - Bridge (graph theory) KW - Drone AB - Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks.Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments. To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions. It also serves as a new benchmark designed to align with downstream task requirements, ensuring greater applicability and robustness in UAV-based OOD.Based on application requirements, we identify four key limitations in current UAV OOD datasets-low image resolution, limited object categories, single-view imaging, and restricted flight altitudes-and propose corresponding improvements to enhance their applicability and robustness.Furthermore, CODrone contains a broad spectrum of annotated images collected from multiple cities under various lighting conditions, enhancing the realism of the benchmark. To rigorously evaluate CODrone as a new benchmark and gain deeper insights into the novel challenges it presents, we conduct a series of experiments based on 22 classical or SOTA methods.Our evaluation not only assesses the effectiveness of CODrone in real-world scenarios but also highlights key bottlenecks and opportunities to advance OOD in UAV applications.Overall, CODrone fills the data gap in OOD from UAV perspective and provides a benchmark with enhanced generalization capability, better aligning with practical applications and future algorithm development. ER - TY - GEN TI - Heterogeneous Alignment-Based Spatio-Temporal Graph Reinforcement Learning for Dynamic Multi-UAVs Task Assignment PY - 2025 DO - 10.1109/icca65672.2025.11129839 UR - https://doi.org/10.1109/icca65672.2025.11129839 DA - 2025-06-30 AU - Zhu, Haojie C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106 AU - Chen, Mou C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106 AU - Zhou, Tongle C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106 AU - Han, Zengliang C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China,211106 LA - en KW - Reinforcement learning KW - Computer science KW - Task (project management) KW - Graph KW - Artificial intelligence KW - Human–computer interaction KW - Machine learning KW - Theoretical computer science KW - Engineering SP - 769 EP - 774 ER - TY - JOUR TI - LAERACE: Taking the policy fast-track towards low-altitude economy PY - 2025 SN - 2941-198X T2 - Journal of the Air Transport Research Society DO - 10.1016/j.jatrs.2025.100058 UR - https://doi.org/10.1016/j.jatrs.2025.100058 DA - 2025-01-11 AU - Sun, Xiaoqian C1 - School of Electronic and Information Engineering, Beihang University, Beijing, PR China AU - Wang, Shuang C1 - School of Electronic and Information Engineering, Beihang University, Beijing, PR China AU - Zhang, Xuejun C1 - School of Electronic and Information Engineering, Beihang University, Beijing, PR China AU - Wandelt, Sebastian C1 - School of Electronic and Information Engineering, Beihang University, Beijing, PR China LA - en KW - Track (disk drive) KW - Fast track KW - Low altitude KW - Altitude (triangle) KW - Business KW - Computer science KW - Operating system KW - Medicine VL - 4 SP - 100058 EP - 100058 AB - The low-altitude economy (LAE) is an emerging vision for integrating unmanned aerial vehicles (UAV) into logistics, mobility, and communication networks. Driven by rapid advancements in UAV technology and growing demand for faster logistics, urban mobility solutions, and improved connectivity, has paved the way for LAE's emergence as a transformative economic sector . This paper presents a policy-focused review along seven critical dimensions - Latency / resilience, Au- tonomy, Equipment / infrastructure, Regulation, Acceptance, Coordination, and Equity (abbreviated as LAERACE) - to outline a policy fast-track for LAE realization. Key challenges include developing policies to ensure the safe certification of autonomous systems , establishing resilient infrastructures that integrate UAV ports and air traffic management into urban landscapes, and harmonizing global standards for regulation and airspace management. Public acceptance is an essential pillar, requiring participatory policymaking that addresses concerns about privacy, noise, and societal impacts . Additionally, equity-focused policies are necessary to prevent widening socioeconomic disparities, ensuring that the benefits of LAE reach underserved regions and populations. We believe that only by addressing these interconnected dimensions through an integrated policy framework, LAE can progress toward becoming a successful, scalable, and inclusive economic sec‑ tor. ER - TY - GEN TI - Optimizing the TSP-D with Pity Beetle Algorithm to Minimize the Delivery Completion Time for UAV Routing Problems PY - 2025 PB - Springer Nature SN - 1860-949X T2 - Studies in computational intelligence DO - 10.1007/978-3-031-78918-2_24 UR - https://doi.org/10.1007/978-3-031-78918-2_24 DA - 2025-01-01 AU - Kanesen, Yugeswary C1 - Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Zakaria, Hasneeza C1 - Centre of Excellence for Advanced Computing (AdvComp), Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Asi, M. C1 - Centre of Excellence for Advanced Computing (AdvComp), Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Othman, Rozmie C1 - Centre of Excellence for Advanced Computing (AdvComp), Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Ariffin, Wan C1 - Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Ramli, Nuraminah C1 - Centre of Excellence for Advanced Computing (AdvComp), Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia AU - Ghani, Mohd C1 - Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600, Arau, Perlis, Malaysia LA - en KW - Pity KW - Computer science KW - Mathematical optimization KW - Routing (electronic design automation) KW - Routing algorithm KW - Algorithm KW - Mathematics KW - Computer network KW - Psychology KW - Routing protocol SP - 267 EP - 277 ER - TY - JOUR TI - Assessing the Carbon Mitigation Potential of UAV-Based Last-Mile Delivery Using 3D Path Planning: A Case Study of Shanghai PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10050364 UR - https://doi.org/10.3390/drones10050364 DA - 2026-05-11 AU - Wang, Ruiqi C1 - Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China C1 - School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China AU - Liu, Ying C1 - Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China LA - en KW - Software deployment KW - Greenhouse gas KW - Grid KW - Carbon fibers KW - Scenario analysis KW - Efficient energy use KW - Geographic information system KW - Urban area VL - 10 IS - 5 SP - 364 EP - 364 AB - Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO2 annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors. ER - TY - JOUR TI - MPFusionNet: Transformer-Based Multimodal Perception Fusion for Predictive Beamforming in Low-Altitude UAV Communication Networks PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2327-4662 T2 - IEEE Internet of Things Journal DO - 10.1109/jiot.2025.3623880 UR - https://doi.org/10.1109/jiot.2025.3623880 DA - 2025-10-20 AU - Xie, Yanxi C1 - School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China C1 - School of Business Administration, Bejing University of Posts and Telecommunications, Beijing, China AU - Gong, Yi C1 - School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, China AU - Zhao, Yuyang C1 - School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, China AU - Zhou, Meiping C1 - Aviation Industry Development Research Center of China, Beijing, China AU - Wang, Song C1 - School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing, China AU - Zhang, Di C1 - School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China AU - Wang, Yi C1 - School of Electronics and Information and Henan Province Collaborative Innovation Center of Aeronautics and Astronautics Electronic Information Technology, Zhengzhou University of Aeronautics, Zhengzhou, China C1 - School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou, China AU - Wang, Jiaqin C1 - School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, China KW - Robustness (evolution) KW - Beamforming KW - Sensor fusion KW - Radar KW - Deep learning KW - Convolutional neural network KW - Dynamic time warping KW - Fusion VL - 13 IS - 4 SP - 5517 EP - 5530 ER - TY - JOUR TI - Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-Modal UAV Beam Prediction PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 0018-9545 T2 - IEEE Transactions on Vehicular Technology DO - 10.1109/tvt.2026.3678249 UR - https://doi.org/10.1109/tvt.2026.3678249 DA - 2026-03-30 AU - Li, Haojin C1 - University of Science and Technology Beijing AU - Zhang, Anbang C1 - Shandong University AU - Sun, Chen C1 - Sony China Research Laboratory AU - Feng, Chenyuan C1 - University of Exeter AU - Qu, Kaiqian C1 - Southeast University, AU - Quek, Tony C1 - Singapore University of Technology and Design AU - Zhang, Haijun C1 - University of Science and Technology Beijing KW - Weighting KW - Beam (structure) KW - Control theory (sociology) KW - Signal-to-noise ratio (imaging) KW - Radar tracker KW - Noise (video) VL - 75 IS - 8 SP - 18732 EP - 18736 ER - TY - GEN TI - Droneways: Definition of Unmanned Aircraft Vehicles Airways and Traffic Management for Urban and Interurban Environments – A Survey PY - 2023 PB - Springer Science+Business Media SN - 0302-9743 T2 - Lecture notes in computer science DO - 10.1007/978-3-031-37114-1_13 UR - https://doi.org/10.1007/978-3-031-37114-1_13 DA - 2023-01-01 AU - de Andrade, Rafael C1 - Advanced Studies Institute, São José dos Campos SP, Brazil C1 - National Institute for Space Research, São José dos Campos, SP, Brazil AU - Shiguemori, Elcio C1 - Advanced Studies Institute, São José dos Campos SP, Brazil C1 - National Institute for Space Research, São José dos Campos, SP, Brazil AU - Santos, Rafael C1 - National Institute for Space Research, São José dos Campos, SP, Brazil LA - en KW - Interurban KW - Aviation KW - Computer science KW - Aeronautics KW - State (computer science) KW - Operations research KW - Transport engineering KW - Engineering KW - Aerospace engineering SP - 179 EP - 197 ER - TY - GEN TI - Unmanned Aerial Vehicles in Logistics: Efficiency Gains and Communication Performance of Hybrid Combinations of Ground and Aerial Vehicles PY - 2019 DO - 10.1109/vnc48660.2019.9062772 UR - https://doi.org/10.1109/vnc48660.2019.9062772 DA - 2019-12-01 AU - Patchou, Manuel C1 - Communication Networks Institute, TU Dortmund University, Dortmund, Germany C1 - Communication Networks Institute, TU Dortmund University,Dortmund,Germany,44227 AU - Sliwa, Benjamin C1 - Communication Networks Institute, TU Dortmund University, Dortmund, Germany C1 - Communication Networks Institute, TU Dortmund University,Dortmund,Germany,44227 AU - Wietfeld, Christian C1 - Communication Networks Institute, TU Dortmund University, Dortmund, Germany C1 - Technical University of Dortmund, LA - en KW - Drone KW - Base station KW - Truck KW - Computer science KW - Context (archaeology) KW - Wireless KW - Popularity KW - Computer network KW - Last mile (transportation) KW - Transport engineering KW - Distributed computing KW - Telecommunications KW - Engineering KW - Automotive engineering SP - 1 EP - 8 AB - Unmanned Aerial Vehicles (UAVs) have drastically gained popularity in various Intelligent Transportation System (ITS) applications to improve the safety and efficiency of transportation systems. In this context, the combination of ground vehicles, such as delivery trucks, with drones to assist in the last-mile pick-up and delivery of the parcels has been recently proposed. While aerial vehicles promise increased efficiency based on flexible routes and parallelized operation, highly reliable wireless communication is also required for the control and coordination of potentially many drones acting in a self-organized way. In this paper, we analyze the improvements procured by drone usage in parcel delivery compared to traditional delivery and propose a simulation framework to further quantify the efficiency gains of the parcel delivery logistics and to analyze the performance of different wireless communications options. To this end, we consider a heterogeneous vehicle routing problem with various constraints. We consider two approaches regarding the dispatching and recovery of drones and evaluate their benefits as opposed to parcel delivery with a classic truck only. Furthermore, we compare two networking technologies for enabling coordination of the self-organizing teams of drones with a realistically modeled environment: one approach relying on base station oriented Long Term Evolution (LTE) vs. a more decentralized Cellular Vehicle-to-Everything (C-V2X) solution. The results show time savings of nearly 80% can be achieved through drone usage and that the negative impact of urban shadowing on network communications in the base station oriented LTE approach can be compensated by leveraging decentralized C-V2X communications. ER - TY - JOUR TI - Spherical Coordinate System-Based Fusion Path Planning Algorithm for UAVs in Complex Emergency Rescue and Civil Environments PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10040285 UR - https://doi.org/10.3390/drones10040285 DA - 2026-04-14 AU - Pan, Xingyi C1 - Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China AU - He, Xingyu C1 - Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China AU - Ren, Xiaoyue C1 - Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China AU - Qi, Duo C1 - Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China LA - en KW - Local optimum KW - Motion planning KW - Obstacle KW - Particle swarm optimization KW - Population KW - Ant colony optimization algorithms KW - Path (computing) KW - Genetic algorithm KW - Trajectory VL - 10 IS - 4 SP - 285 EP - 285 AB - This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. ER - TY - GEN TI - Autonomous Flight of UAV in Complex Multi-Obstacle Environment Using Data-Driven and Vision-Based Deep Reinforcement Learning and AirSim PY - 2025 DO - 10.2514/6.2025-3686 UR - https://doi.org/10.2514/6.2025-3686 DA - 2025-07-16 AU - Ko, Taehoon C1 - Gwangju Institute of Science and Technology AU - Park, Jinhyuk C1 - Gwangju Institute of Science and Technology AU - Choi, Seongim C1 - Gwangju Institute of Science and Technology AU - Shim, Junki C1 - Hanwha Systems Co Ltd LA - en KW - Reinforcement learning KW - Obstacle KW - Computer science KW - Artificial intelligence KW - Obstacle avoidance KW - Computer vision KW - Mobile robot KW - Robot KW - Geography ER - TY - JOUR TI - Optimization of Last-Mile Logistics Delivery Routes for Ground-Vehicle and Drone Parallel Distribution from Pre-Warehouses Considering Customer Priorities PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su18062679 UR - https://doi.org/10.3390/su18062679 DA - 2026-03-10 AU - Wang, Hui C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Zhang, Zuning C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Liu, Manzhi C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Liu, Lingxuan C1 - Department of Management Science, Lancaster University Management School, Lancaster LA1 4YX, UK AU - Wang, Zhongjin C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Long, Shuyu C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Huang, Li C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Liu, Xiaohan C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Tian, Jie C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China AU - Yan, Sen C1 - School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China LA - en KW - Drone KW - Truck KW - Sustainability KW - Service (business) KW - Fuel efficiency KW - Operational efficiency KW - Distribution (mathematics) VL - 18 IS - 6 SP - 2679 EP - 2679 AB - Pre-warehouse last-mile delivery is currently constrained by service radiuses and intense delivery pressures. Meanwhile, national policies are increasingly promoting a transition toward green logistics. By undertaking deliveries to remote or dispersed locations, UAVs can streamline truck routes and minimize the fuel consumption and emissions typically exacerbated by urban traffic congestion. Accordingly, this paper establishes a Ground-Vehicle and Drone Parallel Distribution Model with Priorities (PW-PDSVRP-P), quantifying customer priorities via delivery delay functions to align efficiency with social service requirements. A master–slave hybrid Large Neighborhood Search algorithm is developed and validated through a Hema Fresh case study in Xuzhou. Results define a clear “economic advantage zone” for drone adoption and reveal an adaptive assignment strategy: drones serve as mass-delivery tools in low-cost scenarios but act as “surgical tools” to prune inefficient truck segments in high-cost environments. These findings confirm that air–ground collaboration fosters a more resilient urban distribution system by balancing operational costs with environmental and social sustainability goals. ER - TY - GEN TI - Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms PY - 2025 PB - Springer Science+Business Media SN - 1865-0929 T2 - Communications in computer and information science DO - 10.1007/978-3-031-80775-6_1 UR - https://doi.org/10.1007/978-3-031-80775-6_1 DA - 2025-01-01 AU - Hanaa, Bouhia C1 - ESITH, Casablanca, Morocco AU - Benhra, Jamal C1 - ENSEM, University Hassan II, Casablanca, Morocco AU - Ed-dehbi, Wissal C1 - ENSEM, University Hassan II, Casablanca, Morocco LA - en KW - Truck KW - Mile KW - Computer science KW - Last mile (transportation) KW - Algorithm KW - Artificial intelligence KW - Automotive engineering KW - Engineering KW - Physics SP - 3 EP - 26 ER - TY - JOUR TI - A cyber-physical social system for autonomous drone trajectory planning in last-mile superchilling delivery PY - 2023 PB - Elsevier BV SN - 0968-090X T2 - Transportation Research Part C Emerging Technologies DO - 10.1016/j.trc.2023.104448 UR - https://doi.org/10.1016/j.trc.2023.104448 DA - 2023-12-07 AU - Liu, Haishi C1 - Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region AU - Tsang, Yung C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region AU - Lee, C.K.M. C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region C1 - Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong Special Administrative Region LA - en KW - Drone KW - Heuristic KW - Computer science KW - Last mile (transportation) KW - Trajectory KW - Operations research KW - Mile KW - Transport engineering KW - Simulation KW - Engineering KW - Artificial intelligence VL - 158 SP - 104448 EP - 104448 ER - TY - JOUR TI - An empirical study of drone medical logistics transportation in a multi-campus model of Chinese public hospitals: Real-world data-driven validation of timeliness and application effects PY - 2026 PB - Public Library of Science SN - 1932-6203 T2 - PLoS ONE DO - 10.1371/journal.pone.0345282 UR - https://doi.org/10.1371/journal.pone.0345282 DA - 2026-03-20 AU - Long, Xingbo C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Huang, Min C1 - Department of Quality Improvement, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Xie, Xiankai C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Wang, Yanxin C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Yu, Xiaojiang C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Zhou, Wei C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China AU - Hu, Bo C1 - Department of Medical and Information Engineering, Deyang People's Hospital, Deyang, Sichuan Province, China LA - en KW - Drone KW - Kilometer KW - Unit (ring theory) KW - Empirical research KW - Nonparametric statistics KW - Public transport KW - Statistical analysis VL - 21 IS - 3 SP - e0345282 EP - e0345282 AB - In the real-world application scenario of China's public hospital multi-site operation model, this study systematically evaluates the feasibility, timeliness, and stability of drone-based medical logistics transportation between hospital campuses, providing high-quality empirical evidence for optimizing cross-campus medical supply transfer processes. This retrospective analysis collected complete drone medical logistics transport data between two campuses of Deyang People's Hospital from April 8 to July 30, 2024. The primary outcome measure was drone transport time per unit distance (min/km), compared with road traffic time per unit distance measured by three mainstream navigation apps (Baidu Maps, Amap, and Tencent Map) at different time points. Intergroup differences were analyzed using the Mann-Whitney U nonparametric test, with effect sizes calculated via Cliff's Delta. Statistical significance was set at P < 0.05. A total of 750 valid drone flight records were included, covering a distance of (5.95 ± 0.03) km with a unit time of (1.64 ± 0.14) min/km. The unit time for drone transportation was significantly lower than that measured by Baidu Maps (2.06 ± 0.12 min/km), Amap (2.01 ± 0.12 min/km), and Tencent Map (2.03 ± 0.09 min/km) at the 0-point road traffic unit time (all P < 0.001). At all nine time points monitored by Tencent Map, the unit time per kilometer exceeded that of the UAV. During the 10:00 peak period, Tencent Map recorded a unit time of (4.12 ± 0.09) min/km, with the UAV achieving a time savings rate of 60.2%. Mann-Whitney U tests revealed significant differences across all time points (P < 0.001), with Cliff's Delta absolute values consistently exceeding 0.75, indicating extremely large effect sizes. Drone-based medical logistics demonstrated significant advantages in timeliness and stability under the multi-campus model of urban hospitals, particularly during peak traffic congestion periods. This study provides crucial empirical support for establishing an efficient, intelligent medical logistics system, holding significant implications for enhancing healthcare service efficiency and improving public health emergency response capabilities. ER - TY - GEN TI - CITRUS: Cost and Ischemia Time Reduction using Urban Air Mobility Solutions for Organ Transport PY - 2025 DO - 10.1109/percomworkshops65533.2025.00060 UR - https://doi.org/10.1109/percomworkshops65533.2025.00060 DA - 2025-03-17 AU - Sengupta, Debjyoti C1 - Missouri University of Science and Technology,Department of Computer Science,Rolla,MO,USA AU - Satpathy, Anurag C1 - Missouri University of Science and Technology,Department of Computer Science,Rolla,MO,USA AU - Khanda, Arindam C1 - Missouri University of Science and Technology,Department of Computer Science,Rolla,MO,USA AU - Das, Sajal C1 - Missouri University of Science and Technology,Department of Computer Science,Rolla,MO,USA LA - en KW - Reduction (mathematics) KW - Cost reduction KW - Environmental science KW - Ischemia KW - Computer science KW - Business KW - Cardiology KW - Mathematics KW - Medicine SP - 170 EP - 175 ER - TY - JOUR TI - Traffic-Predictive Drone Scheduling: Day-Ahead Synchronization of Mobile Depots and Parallel Aerial Sorties in Urban Airspace PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10060461 UR - https://doi.org/10.3390/drones10060461 DA - 2026-06-13 AU - Hasan, Shabahat C1 - Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia AU - Sheltami, Tarek C1 - Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia C1 - Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia AU - Mahmoud, Ashraf C1 - Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia C1 - Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia LA - en KW - Drone KW - Scheduling (production processes) KW - Baseline (sea) KW - Particle swarm optimization KW - Intersection (aeronautics) KW - Linear programming KW - Synchronization (alternating current) KW - Dynamic programming VL - 10 IS - 6 SP - 461 EP - 461 AB - Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. ER - TY - GEN TI - Initial Sizing of an Electric Tiltrotor UAV for Urban Air Logistics PY - 2025 DO - 10.1109/aaac66612.2025.11427480 UR - https://doi.org/10.1109/aaac66612.2025.11427480 DA - 2025-09-26 AU - An, Boxu C1 - China Academy of Aerospace Aerodynamics,Beijing,China AU - Wang, Yongzhi C1 - China Academy of Aerospace Aerodynamics,Beijing,China KW - Sizing KW - Drone KW - Air transport KW - Work (physics) SP - 345 EP - 351 ER - TY - JOUR TI - A Decision System for Routing Problems and Rescheduling Issues Using Unmanned Aerial Vehicles PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 2076-3417 T2 - Applied Sciences DO - 10.3390/app12126140 UR - https://doi.org/10.3390/app12126140 DA - 2022-06-16 AU - Lin, I-Ching C1 - Department of Management, Air Force Institute of Technology, Kaohsiung City 82047, Taiwan C1 - Department of Transportation and Communication, National Cheng Kung University, Tainan City 701, Taiwan AU - Lin, Tsan-Hwan C1 - Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan AU - Chang, Sheng-Hung C1 - Department of Industrial Engineering and Management, Ming Hsin University of Science and Technology, Hsinchu 30401, Taiwan LA - en KW - Customer satisfaction KW - Revenue KW - Maximization KW - Profit (economics) KW - Vehicle routing problem KW - Business KW - Operations research KW - Computer science KW - Process management KW - Marketing KW - Routing (electronic design automation) KW - Engineering KW - Finance KW - Computer network KW - Mathematical optimization VL - 12 IS - 12 SP - 6140 EP - 6140 AB - In recent years, consumers have come to expect faster and better delivery services. Logistics companies, therefore, must implement innovative technologies or services in their logistics processes. It is critical to adopt unmanned aerial vehicles (UAV) in last mile delivery and urban logistics. The service provider applies the characteristics of UAVs to complete more requests, benefiting more revenue. However, it may not be a satisfactory solution, because the customers will be dissatisfied if the actual delivery time does not align with their expectations. This study constructs a revenue maximization model subject to time windows and customer satisfaction. Instead of addressing the traveling salesmen problem, this model takes new customer requests during the delivery process into account. We solved the problem using a genetic algorithm. The results show: (1) the model found an approximate and effective solution in the real-time delivery environment; (2) customer satisfaction is inversely proportional to the total delivery distance; (3) regarding the result of the sensitivity analysis of this study, investment in UAV has no influence on total profit and customer satisfaction. Moreover, the customer is a key factor in the logistics decision-making platform, not the provider’s investment in UAVs. ER - TY - JOUR TI - Evaluation of Factors Affecting UAV Selection in Urban Air Logistics PY - 2025 PB - Inderscience Publishers SN - 1756-6517 T2 - International Journal of Shipping and Transport Logistics DO - 10.1504/ijstl.2025.10070305 UR - https://doi.org/10.1504/ijstl.2025.10070305 DA - 2025-01-01 AU - Yava, Volkan AU - Mercan, Yasin AU - Can, Dilek LA - en KW - Selection (genetic algorithm) KW - Business KW - City logistics KW - Computer science KW - Process management KW - Operations management KW - Transport engineering VL - 1 IS - 1 ER - TY - JOUR TI - Evaluation of factors affecting UAV selection in urban air logistics PY - 2025 PB - Inderscience Publishers SN - 1756-6517 T2 - International Journal of Shipping and Transport Logistics DO - 10.1504/ijstl.2025.148844 UR - https://doi.org/10.1504/ijstl.2025.148844 DA - 2025-01-01 AU - Mercan, Yasin AU - YAVAŞ, Volkan AU - Can, Dilek LA - en KW - Selection (genetic algorithm) KW - Air transport KW - Aviation KW - Air pollution KW - Work (physics) VL - 21 IS - 3 SP - 317 EP - 344 ER - TY - JOUR TI - A Blockchain-Based Multi-Unmanned Aerial Vehicle Task Processing System for Situation Awareness and Real-Time Decision PY - 2023 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su151813790 UR - https://doi.org/10.3390/su151813790 DA - 2023-09-15 AU - Chen, Ziqiang C1 - School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China AU - Xiong, Xuanrui C1 - School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China AU - Wang, Wei C1 - School of Software, Dalian University of Technology, Dalian 116024, China AU - Xiao, Yulong C1 - School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China AU - Alfarraj, Osama C1 - Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia LA - en KW - Computer science KW - Blockchain KW - Swarm behaviour KW - Computer security KW - Scheduling (production processes) KW - Task (project management) KW - Distributed computing KW - Systems engineering KW - Artificial intelligence KW - Engineering VL - 15 IS - 18 SP - 13790 EP - 13790 AB - With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are being extensively applied in various fields, such as intelligent transportation, search and rescue, logistics delivery, and aerial mapping. However, the utilization of UAV swarms in sustainable transportation also presents some challenges, such as inefficient task allocation and data transmission security issues, highlighting the importance of privacy protection in this context. To address these issues, this study applies blockchain technology to multi-UAV tasks and proposes a blockchain-based multi-UAV task processing system for situation awareness and real-time decisions. The primary objective of this system is to enhance the efficiency of UAV swarm task scheduling, bolster data transmission security, and address privacy protection concerns. Utilizing the highly secure features of blockchain technology, the system constructs a distributed task processing network. System tasks are stored in the blockchain through smart contracts, ensuring the immutability and verifiability of task information. Smart contracts have an automatic execution capability, whereby the system can efficiently coordinate tasks and maintain the consistency of task execution information through consensus mechanisms. Additionally, adopting the Pointer Network structure for intelligent path planning based on task allocation results leads to the attainment of the shortest service routes, consequently expanding the service coverage of sustainable transportation systems while reducing energy consumption. This further advances the realization of urban sustainable transportation. Through experimental results, we verify that the proposed system enables real-time task scheduling and collaborative processing for multiple UAVs, significantly enhancing the efficiency, security, and privacy protection level of UAV swarm task execution in the context of sustainable transportation. It makes a positive contribution to building more sustainable urban transportation systems. ER - TY - JOUR TI - RESEARCH ON UAV IN LOGISTICS DISTRIBUTION PY - 2023 PB - National Aviation University SN - 2075-0781 T2 - Science-based technologies DO - 10.18372/2310-5461.59.17954 UR - https://doi.org/10.18372/2310-5461.59.17954 DA - 2023-10-31 AU - Li, Haoyang C1 - National aviation University, Kiev, Ukraine LA - en KW - Last mile (transportation) KW - Distribution center KW - Process (computing) KW - Scope (computer science) KW - Service (business) KW - Computer science KW - Context (archaeology) KW - Distribution (mathematics) KW - Transport engineering KW - Operations research KW - Sorting KW - Engineering KW - Mile KW - Business KW - Marketing VL - 59 IS - 3 SP - 334 EP - 339 AB - This article takes the COVID-19 epidemic as the starting point, and analyzes the difficulty of delivering drones in the "last mile" in the context of a severely aging population. The last mile delivery means that after customers shop through e-commerce, the purchased items are delivered to the distribution point, and then the goods are delivered to the customer from a sorting center through a certain means of transportation to achieve door-to-door service. The "last mile" of distribution is not a real mile, but refers to the distance from the logistics sorting center to the customer, and the process of delivering the goods to the customer through the means of transportation. Because it is a short distance, it is called one-kilometer delivery as the saying goes. This short-distance distribution is the last link of the entire logistics link and the only link that directly contacts customers face-to-face, which is of great significance. With the maturity of artificial intelligence technology, UAV distribution has emerged. This paper expounds the distribution scope, distribution process and technical equipment of the current development of UAV distribution, analyzes the problems of UAV endurance, airspace restrictions, and distribution safety, and puts forward corresponding countermeasures. Intelligent computing can reconstruct the logistics operation process. With the development of artificial intelligence, the logistics process may be decomposed into several parallel processes. Within the scope of the logistics system, using artificial intelligence technology, many activities such as automatic decision-making, automatic detection, and automatic repair can be automatically sorted according to the process. These activities can also not enter the cloud computing center platform, greatly improving the system computing efficiency and responsiveness. Because of its light weight and fast speed, UAVs can avoid urban traffic congestion and can cross special terrains such as mountains and lakes. The use of UAVs can shorten logistics delivery time and reduce logistics costs, so that its application scope of logistics distribution is getting wider and wider. In recent years, due to the strong contagiousness of the new crown epidemic, contactless distribution has attracted widespread attention, which has promoted the promotion and application of cutting-edge logistics distribution tools such as drones, drone vehicles and delivery robots. ER - TY - GEN TI - Research on UAV Low Altitude Logistics Emergency Scheduling Mechanism for Urban Incidents PY - 2025 DO - 10.1109/leficlu65987.2025.11297235 UR - https://doi.org/10.1109/leficlu65987.2025.11297235 DA - 2025-09-26 AU - Yao, Kan C1 - Guangzhou Nanyang Polytechnic College,Guangzhou City,China AU - Liu, Yue C1 - Guangzhou Nanyang Polytechnic College,Guangzhou City,China KW - Scheduling (production processes) KW - Scalability KW - Adaptability KW - Robustness (evolution) KW - Dynamic priority scheduling KW - Reinforcement learning KW - Drone KW - Emergency response SP - 169 EP - 173 ER - TY - JOUR TI - Design and development of a hybrid eVTOL UAV with guided parachute delivery for urban logistics PY - 2026 PB - Elsevier BV SN - 2215-0986 T2 - Engineering Science and Technology an International Journal DO - 10.1016/j.jestch.2026.102389 UR - https://doi.org/10.1016/j.jestch.2026.102389 DA - 2026-05-16 AU - Konyalıoğlu, Turan C1 - Istanbul Technical University, Graduate School, Ayazağa Campus, Katar Cad. No: 2, Sarıyer, Istanbul, 34467, Türkiye AU - Alnıpak, Sinan C1 - Istanbul Technical University, Graduate School, Ayazağa Campus, Katar Cad. No: 2, Sarıyer, Istanbul, 34467, Türkiye AU - Altuğ, Erdinç C1 - Istanbul Technical University, Department of Mechanical Engineering, İnönü Cad. No: 65, Beyoğlu, Istanbul, 34437, Türkiye LA - en KW - Runway KW - Payload (computing) KW - Aerodynamics KW - Multirotor KW - Controller (irrigation) KW - Software deployment KW - Sequential quadratic programming KW - Cruise control KW - Vehicle dynamics KW - Nonlinear system VL - 79 SP - 102389 EP - 102389 AB - In recent years, we have seen various urban parcel delivery solutions designed to overcome the limitations of ground transportation and meet the surge in delivery demand. Many of the proposed systems are in the form of conventional unmanned aerial vehicles (UAVs). Multirotor UAVs typically suffer from limited endurance and range, whereas fixed-wing platforms require runways and lack hovering capability, restricting their applicability in dense urban environments. This paper presents the design and development of a hybrid electric vertical take-off and landing (eVTOL) UAV with a 3.6-m wingspan, combining vertical take-off and landing capability with efficient fixed-wing cruise performance. To eliminate time-consuming landing operations and enhance point-delivery precision, the proposed system integrates a dual parachute-assisted payload deployment mechanism, enabling the delivery of two independent packages without landing. An integrated design framework is adopted, combining aerodynamic modeling, mechanical design, hybrid composite-additive manufacturing, and flight control algorithms. The aerodynamic characteristics of the aircraft are obtained through a unified database generated using the Vortex Lattice Method (VLM), Computational Fluid Dynamics (CFD), and nonlinear simulation environments. Based on this model, trim analysis and stability characteristics are derived and utilized in the controller design. A hierarchical control architecture incorporating Model Predictive Control (MPC), Linear Quadratic Regulator (LQR), and Proportional–Integral–Derivative (PID) controllers is implemented to manage hover, transition, and forward-flight. The proposed system is evaluated through nonlinear simulations conducted in Gazebo and ROS, as well as indoor experimental stability tests. Simulation results demonstrate stable transition behavior, with hover-to-cruise transition completed in 15 s and a maximum altitude deviation of ± 3 m. Trajectory tracking performance across multiple flight modes yielded a root mean square error (RMSE) of 0.5–1.5 m. The guided parachute delivery system is validated through dedicated simulations, achieving a Circular Error Probable (CEP) of about 1 m for each independent package under nominal wind conditions of up to 7 m/s. The aircraft is designed to carry a total payload of 8 kg with a demonstrated cruise efficiency of 15 Wh/km and an operational range of 16 km, while maintaining low manufacturing and operational costs under nominal conditions; performance under adverse wind scenarios is reserved for future experimental validation. The results indicate that the proposed hybrid VTOL UAV offers a viable and scalable solution for multi-package urban delivery applications, combining aerodynamic efficiency, control robustness, and manufacturing flexibility. ER - TY - GEN TI - Grid-based location optimization for UAV vertiports in urban logistics networks PY - 2026 DO - 10.1117/12.3101418 UR - https://doi.org/10.1117/12.3101418 DA - 2026-03-03 AU - Li, Haotian AU - Zhang, Qiqian AU - Zhang, Honghai AU - Wang, Hairu KW - Key (lock) KW - Global Positioning System KW - Trajectory KW - Process (computing) KW - Work (physics) SP - 105 EP - 105 ER - TY - GEN TI - Evaluating the importance of nodes in the urban logistics UAV distribution network based on complex network theory PY - 2023 DO - 10.1117/12.2683964 UR - https://doi.org/10.1117/12.2683964 DA - 2023-06-14 AU - Huang, Longyang C1 - Civil Aviation Flight Univ. of China (China) AU - Li, Siyi C1 - Civil Aviation Flight Univ. of China (China) AU - Zou, Xiang C1 - The Second Research Institute of CAAC (China) AU - Zhao, Bozhi C1 - Civil Aviation Flight Univ. of China (China) AU - Li, Chenglong C1 - Civil Aviation Flight Univ. of China (China) LA - en KW - Ranking (information retrieval) KW - Complex network KW - Node (physics) KW - Construct (python library) KW - Computer science KW - Field (mathematics) KW - TOPSIS KW - Identification (biology) KW - Key (lock) KW - Distribution (mathematics) KW - Drone KW - Transport engineering KW - Operations research KW - Computer network KW - Artificial intelligence KW - Engineering KW - Computer security KW - Mathematics SP - 77 EP - 77 ER - TY - CHAP TI - Urban Logistics UAV System Planning, Operation and Comprehensive Governance: A Review and Prospects PY - 2026 PB - Atlantis Press SN - 2352-5428 T2 - Advances in economics, business and management research/Advances in Economics, Business and Management Research DO - 10.2991/978-94-6239-758-3_40 UR - https://doi.org/10.2991/978-94-6239-758-3_40 DA - 2026-01-01 AU - Lu, Jiang AU - Li, Tao AU - Shi, Mingzhu AU - Lu, Yachen LA - en KW - Work (physics) KW - Key (lock) KW - Global Positioning System KW - Field (mathematics) SP - 418 EP - 427 ER - TY - GEN TI - Logistics UAV operation route planning in urban low-altitude airspace based on rasterization method PY - 2024 PB - Institution of Engineering and Technology SN - 2732-4494 T2 - IET conference proceedings. DO - 10.1049/icp.2024.0660 UR - https://doi.org/10.1049/icp.2024.0660 DA - 2024-02-07 AU - Li, Wei C1 - Civil Aviation Internationalization Talent Development Center, Civil Aviation Management Institute of China,Beijing,People's Republic of China C1 - Civil Aviation Internationalization Talent Development Center, Civil Aviation Management Institute of China , Beijing , People's Republic of China AU - Wang, Zhiji C1 - Civil Aviation Internationalization Talent Development Center, Civil Aviation Management Institute of China,Beijing,People's Republic of China C1 - Civil Aviation Internationalization Talent Development Center, Civil Aviation Management Institute of China , Beijing , People's Republic of China LA - en KW - Computer science KW - Intersection (aeronautics) KW - Fence (mathematics) KW - Route planning KW - Real-time computing KW - Drone KW - Simulation KW - Transport engineering KW - Engineering VL - 2023 IS - 43 SP - 245 EP - 249 ER - TY - GEN TI - Multi-UAV Task Allocation and Path Planning for Urban Low-Altitude Logistics PY - 2026 DO - 10.1109/icaisisas68969.2026.11567702 UR - https://doi.org/10.1109/icaisisas68969.2026.11567702 DA - 2026-05-08 AU - Hao, Yapeng C1 - Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation,Wuxi,China AU - Liu, Chenglin C1 - Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation,Wuxi,China KW - Task (project management) KW - Path (computing) KW - Motion planning KW - Key (lock) KW - Plan (archaeology) SP - 1 EP - 6 ER - TY - JOUR TI - Bi-Level Optimization Framework for Two-Echelon UAV Scheduling in Urban Low-Altitude Logistics PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2026.3725824 UR - https://doi.org/10.1109/access.2026.3725824 DA - 2026-01-01 AU - Yang, Song C1 - Wuhan University of Technology AU - Wang, Haiyan C1 - Wuhan University of Technology AU - Xia, Jianhui C1 - Wuhan University of Technology AU - Zhang, Sai C1 - Wuhan University of Technology AU - Wang, Yifan C1 - Wuhan University of Technology LA - en KW - Scheduling (production processes) KW - Job shop scheduling KW - Processor scheduling KW - Dynamic priority scheduling KW - Algorithm design VL - 14 SP - 139549 EP - 139572 AB - Efficient urban UAV logistics require deep synergy between task allocation and 3D path planning, which traditional methods often decouple. Traditional ground-based wheeled logistics is constrained by road traffic efficiency, leading to frequent delivery delays and continuously rising costs. Additionally, existing studies usually decouple task allocation and path planning, and ignore the dynamic energy consumption differences of UAVs under actual full-load and empty-load states. To address this, this study proposes a novel heterogeneous bi-level optimization framework, Bi-AGWO, to solve the two-echelon multi-UAV cooperative delivery problem (2E-MDVRP) in 3D urban environments. The upper-layer model utilizes ant colony optimization (ACO) to handle discrete combinatorial task dispatching and cross-hub sharing sequences. Meanwhile, the lower-layer model integrates the grey wolf optimizer (GWO) with cubic smoothing splines for continuous 3D obstacle avoidance and precise flight distance evaluation. A novel iterative closed-loop feedback mechanism directly returns high-fidelity lower-layer physical distances to drive the upper-layer heuristic search. Simulation results factoring in UAV empty weight and power consumption demonstrate superior global convergence stability. In dense obstacle scenarios, Bi-AGWO achieves an optimal global energy consumption of 23.42 kWh, significantly outperforming state-of-the-art baselines like Bi-ALNS and Bi-PSO. It autonomously evolves an efficient near-neighbor clustering topology. Furthermore, parameter sensitivity analysis reveals a S-shaped energy decay response to maximum payload capacity and a fierce monotonic escalation with empty weight increases. Ultimately, the framework serves as a robust decision-making tool for urban instant logistics enterprises. ER - TY - JOUR TI - Multi-objective location optimization of urban logistics UAV vertiports considering noise and energy constraints PY - 2026 T2 - DOAJ (DOAJ: Directory of Open Access Journals) DO - 10.3724/sp.j.1249.2026.04451 UR - https://doi.org/10.3724/sp.j.1249.2026.04451 DA - 2026-07-01 AU - Lili, Wang AU - Peiqiao, Song AU - Lin, Tan LA - zh KW - Genetic algorithm KW - Sorting KW - Noise (video) KW - Context (archaeology) KW - Energy consumption KW - Range (aeronautics) KW - Population KW - Service (business) ER - TY - GEN TI - Spatiotemporal Consistency/Anchor in Urban Remote Sensing for UAV-based Logistics using Multi-Temporal Imaging PY - 2025 DO - 10.1109/icraic67376.2025.11376151 UR - https://doi.org/10.1109/icraic67376.2025.11376151 DA - 2025-10-31 AU - Yong, Chengyou C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China AU - Yuan, Cheng C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China AU - Zhan, Hanyu C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China AU - Lai, Jizhou C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China AU - Lyu, Pin C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China AU - Zhang, Qieqie C1 - Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing,China KW - Robustness (evolution) KW - Field (mathematics) KW - Feature (linguistics) KW - Satellite KW - Smart city KW - Feature extraction KW - Data processing KW - Point (geometry) SP - 1 EP - 5 ER - TY - JOUR TI - Positioning-aware accessibility modeling for urban UAV logistics networks using vehicle trajectory data PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7288371 UR - https://doi.org/10.2139/ssrn.7288371 DA - 2026-01-01 AU - Zhou, Zixuan C1 - Peking University, No. 38 Xueyuan Road, Haidian District, Beijing, 100871, China AU - Feng, Jiawei C1 - The University of Hong Kong, Pokfulam Road, Hong Kong, China AU - Qiao, Si AU - Mei, Wenjun C1 - Peking University, No. 38 Xueyuan Road, Haidian District, Beijing, 100871, China AU - Lu, X. C1 - National University of Defense Technology, Changsha Hunan, 410073, China AU - Liu, Kai C1 - Dalian University of Technology, Huiying Rd, DaLian, LiaoNing, 116024, China KW - Trajectory KW - Scale (ratio) KW - Regret KW - Reachability KW - Provisioning KW - Calibration KW - Terminal (telecommunication) KW - Runway KW - Backup KW - Set (abstract data type) ER - TY - JOUR TI - UAV-based urban logistics service network design under demand and weather-induced supply uncertainty PY - 2026 PB - Elsevier BV SN - 1366-5545 T2 - Transportation Research Part E Logistics and Transportation Review DO - 10.1016/j.tre.2026.105091 UR - https://doi.org/10.1016/j.tre.2026.105091 DA - 2026-07-14 AU - Tan, Zhijia C1 - School of Management, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China AU - Wang, Qinpeng C1 - School of Management, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China AU - Yang, Hai C1 - Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong AU - Liu, Wei C1 - Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong C1 - Research Centre for Low-Altitude Economy (RCLAE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong AU - Li, Zhichun C1 - School of Management, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China LA - en KW - Stochastic game KW - Stochastic programming KW - Service (business) KW - Supply and demand KW - Traffic congestion KW - Supply chain KW - Service level KW - City logistics KW - Network planning and design VL - 215 SP - 105091 EP - 105091 ER - TY - JOUR TI - Positioning-aware Accessibility Modeling for Urban UAV Logistics Networks using Vehicle Trajectory Data PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7283480 UR - https://doi.org/10.2139/ssrn.7283480 DA - 2026-01-01 AU - Zhou, Zixuan C1 - College of Engineering , Peking University , Beijing , China C1 - Department of Real Estate and Construction , The University of Hong Kong , Hong Kong , China AU - Feng, Jiawei AU - Qiao, Si C1 - Urban Governance and Design , The Hong Kong University of Science and Technology (Guangzhou) , Guangzhou , China AU - Mei, Wenjun C1 - College of Engineering , Peking University , Beijing , China AU - Lu, X. C1 - College of Systems Engineering , National University of Defense Technology , Changsha , China AU - Liu, Kai C1 - School of Economics and Management , Dalian University of Technology , Dalian , China KW - Scale (ratio) KW - Trajectory KW - Regret KW - Reachability KW - Provisioning KW - Calibration KW - Terminal (telecommunication) KW - Runway KW - Set (abstract data type) KW - Backup ER - TY - JOUR TI - Integrated truck drone delivery services with an optimal charging stations PY - 2024 PB - Elsevier BV SN - 0957-4174 T2 - Expert Systems with Applications DO - 10.1016/j.eswa.2024.124254 UR - https://doi.org/10.1016/j.eswa.2024.124254 DA - 2024-06-01 AU - Mishra, Dev C1 - Indian Institute of Management, Mumbai, Maharashtra, India AU - Tiwari, Manoj C1 - Indian Institute of Management, Mumbai, Maharashtra, India LA - en KW - Drone KW - Truck KW - Computer science KW - Automotive engineering KW - Engineering VL - 254 SP - 124254 EP - 124254 ER - TY - JOUR TI - Multidimensional Coupling Model for Quantitative Ground Risk Assessment of Urban Low-Altitude Logistics UAVs PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2026.3699546 UR - https://doi.org/10.1109/access.2026.3699546 DA - 2026-01-01 AU - Chen, Hongmei C1 - School of Economics and Management, Yanshan University, Qinhuangdao, China AU - Cao, Haoyu C1 - School of Economics and Management, Yanshan University, Qinhuangdao, China AU - Soomro, Jahangeer C1 - School of Economics and Management, Yanshan University, Qinhuangdao, China LA - en KW - Coupling (piping) KW - Multidimensional systems KW - Data modeling KW - Numerical models KW - Trajectory VL - 14 SP - 83869 EP - 83883 AB - This paper proposes a multi-dimensional coupled quantitative model for the ground risk of urban low-altitude logistics drones, integrating public acceptance, to address the difficulty of existing assessment methods in integrating dynamic environmental and socio-psychological factors. The model combines physical, economic, and social dimensions through a grid-based airspace segmentation and multi-source data framework. The physical module quantifies crash patterns and wind resistance impact, the economic module assesses direct and indirect losses, while the social module converts public psychological tolerance into spatial correction factors. Based on the theory of "social amplification of risk," an inter-dimensional coupling mechanism is established, and a dynamic risk map generated using GIS. Case simulations show that wind resistance can expand the high-risk area for cargo falls by approximately 15%, the high-risk area for gliding overlaps with the public's psychologically sensitive area by 83%, and low public acceptance can lead to an increase in the overall risk level by 1–2 levels. Further, the pure physical risk model underreported 35%. This model outperforms traditional methods in terms of the comprehensiveness and spatial accuracy of risk identification, providing quantitative decision support for risk management and route planning in low-altitude logistics systems. ER - TY - GEN TI - Dynamic Scheduling and Conflict ReDynamic Scheduling and Conflict Resolution for Urban Low-Altitude Logistics UAV Swarms Based on Digital Twin Technology: Solution for Urban Low-Altitude Logistics UAV Swarms Based on Digital Twin Technology PY - 2025 DO - 10.1109/ust66533.2025.11340026 UR - https://doi.org/10.1109/ust66533.2025.11340026 DA - 2025-09-19 AU - Zhang, Ning C1 - Guangzhou Civil Aviation College,Guangzhou,China KW - Adaptability KW - Drone KW - Scheduling (production processes) KW - Conflict resolution KW - Motion planning KW - Reinforcement learning KW - Field (mathematics) KW - Conflict management SP - 13 EP - 19 ER - TY - JOUR TI - Range-Feasibility Blindness in Urban UAV Logistics: A Feasibility-Embedded Location–Routing Framework for Infrastructure Planning PY - 2026 T2 - Preprints.org DO - 10.20944/preprints202605.0828.v1 UR - https://doi.org/10.20944/preprints202605.0828.v1 DA - 2026-05-13 AU - Yang, Qunting AU - Liu, Bingqing AU - Xie, Chunsheng AU - Zhang, Wen KW - Benchmark (surveying) KW - Position (finance) KW - Range (aeronautics) KW - Monotonic function KW - Blindness KW - Service (business) KW - Urban planning KW - Neighbourhood (mathematics) KW - Property (philosophy) AB - Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road-network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road-network range constraints simultaneously with depot-opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments across all three cities show consistent cost reductions of 20.6–28.2% over sequential baselines, with gains increasing monotonically with instance scale. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. ER - TY - JOUR TI - Range-Feasibility Blindness in Urban UAV Logistics: A Feasibility-Embedded Location–Routing Framework for Infrastructure Planning PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2226-4310 T2 - Aerospace DO - 10.3390/aerospace13060536 UR - https://doi.org/10.3390/aerospace13060536 DA - 2026-06-08 AU - Yang, Qunting C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Liu, Bingqing C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Xie, Chunsheng C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China AU - Wen, Zhang C1 - College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China LA - en KW - Beijing KW - Benchmark (surveying) KW - Position (finance) KW - Range (aeronautics) KW - Matching (statistics) KW - Service (business) KW - Greedy algorithm KW - Routing (electronic design automation) KW - Urban planning KW - Bottleneck VL - 13 IS - 6 SP - 536 EP - 536 AB - Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road network range constraints simultaneously with depot opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments on Dongli District (Tianjin) show cost reductions of 20.6–28.2% over greedy sequential baselines across three demand scenarios, with gains increasing monotonically with instance scale; cross-city experiments in Beijing and Shanghai confirm consistent improvement averaging 11.4% (Chaoyang, Beijing) and 10.2% (Pudong, Shanghai) over greedy initialisation across diverse urban morphologies. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. ER - TY - GEN TI - Bluetooth Low Energy based Technology for Small UAS Indoor Positioning PY - 2022 T2 - 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace) DO - 10.1109/metroaerospace54187.2022.9856321 UR - https://doi.org/10.1109/metroaerospace54187.2022.9856321 DA - 2022-06-27 AU - Ariante, Gennaro C1 - University of Naples "Parthenope",Department of Science and Technology,Naples,Italy,80133 AU - Ponte, Salvatore C1 - University of Campania "L. Vanvitelli",Department of Engineering,Aversa (CE),Italy,81031 AU - Del Core, Giuseppe C1 - University of Naples "Parthenope",Department of Science and Technology,Naples,Italy,80133 LA - en KW - Computer science KW - Beacon KW - Inertial measurement unit KW - Real-time computing KW - GNSS applications KW - Global Positioning System KW - Bluetooth KW - Drone KW - Embedded system KW - Wireless KW - Telecommunications KW - Artificial intelligence SP - 113 EP - 118 ER - TY - JOUR TI - Driving Innovation: Technology Acceptance of Unmanned and Autonomous Vehicles (Uavs) in Urban Mobility and Logistics – a Systematic Literature Review PY - 2024 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.4987197 UR - https://doi.org/10.2139/ssrn.4987197 DA - 2024-01-01 AU - McTigue, Clare C1 - Edinburgh Napier University C1 - Edinburgh Napier University, Edinburgh, EH10 5LG, United States AU - Akgün, Emine C1 - Heriot-Watt University C1 - Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, EH14 1AS, United Kingdom AU - Mora, Luca C1 - Edinburgh Napier University C1 - Edinburgh Napier University, Edinburgh, United Kingdom LA - en KW - Business KW - Transport engineering KW - Computer science KW - Engineering ER - TY - JOUR TI - A Co-Design Framework for UAV Logistics Infrastructure and Distributed UAV Swarm Scheduling PY - 2026 T2 - Research Square DO - 10.21203/rs.3.rs-9532263/v1 UR - https://doi.org/10.21203/rs.3.rs-9532263/v1 DA - 2026-04-30 AU - Gu, Haoze C1 - Harbin Institute of Technology AU - Qi, Zhixin C1 - Harbin Institute of Technology AU - Zejiao, Dong C1 - Harbin Institute of Technology AU - Gershome, Abaho C1 - University of Rwanda KW - Scheduling (production processes) KW - Network topology KW - Scalability KW - Bottleneck KW - Swarm behaviour KW - Distributed algorithm KW - Topology (electrical circuits) KW - Dynamic priority scheduling ER - TY - JOUR TI - Dynamic Navigation in Unconstrained Environments Using Reinforcement Learning Algorithms PY - 2023 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2023.3326435 UR - https://doi.org/10.1109/access.2023.3326435 DA - 2023-01-01 AU - Chronis, Christos C1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece AU - Anagnostopoulos, Georgios C1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece AU - Politi, Elena C1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece AU - Dimitrakopoulos, George C1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece AU - Varlamis, Iraklis C1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece LA - en KW - Drone KW - Computer science KW - Reinforcement learning KW - Bottleneck KW - Motion planning KW - Artificial intelligence KW - Path (computing) KW - Set (abstract data type) KW - Machine learning KW - Real-time computing KW - Robot KW - Embedded system VL - 11 SP - 117984 EP - 118001 AB - The potential for the use of drones in logistics and transportation is continuously growing, with multiple applications both in urban and rural environments. The safe navigation of drones in such environments is a major challenge that requires sophisticated algorithms and systems, that can quickly and efficiently estimate the situation, find the shortest path to the target, and detect and avoid obstacles. Traditional path planning algorithms are unable to handle the dynamic and uncertain nature of real environments, while traditional machine learning models are insufficient due to the constantly changing conditions that affect the drone location and the location of obstacles. Reinforcement learning (RL) algorithms have been widely used for autonomous navigation problems, however, computational complexity and energy demands of such methods can become a bottleneck to the execution of UAV flights. In this paper, we propose the use of a minimum set of sensors and reinforcement learning (RL) algorithms for the safe and efficient navigation of drones in urban and rural environments. Our approach considers the complex and dynamic nature of such environments by incorporating real-time data from low-cost onboard sensors. After performing a thorough review of the existing solutions in drone path planning and navigation in 3-D environments, we experimentally evaluate our proposed approach in a simulated environment, and in various scenarios. The test results demonstrate the effectiveness of the proposed RL-based approach in the navigation of drones in complex and unconstrained environments. The implemented approach can serve as a basis for the development of advanced and robust navigation systems for drones, which can improve safety and efficiency in transportation applications in the near future. ER - TY - JOUR TI - A Co-Design Framework for UAV Logistics Infrastructure and Distributed UAV Swarm Scheduling PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6669165 UR - https://doi.org/10.2139/ssrn.6669165 DA - 2026-01-01 AU - Gu, Haoze AU - Qi, Zhixin AU - Zejiao, Dong AU - Gershome, Abaho KW - Scheduling (production processes) KW - Network topology KW - Scalability KW - Bottleneck KW - Swarm behaviour KW - Distributed algorithm KW - Topology (electrical circuits) KW - Dynamic priority scheduling ER - TY - GEN TI - Research on UAV Logistics Path Planning and Dynamic Obstacle Avoidance Algorithm in Complex Urban Environment PY - 2025 DO - 10.1109/icusac68314.2025.11414284 UR - https://doi.org/10.1109/icusac68314.2025.11414284 DA - 2025-12-26 AU - Zhao, Xujiayin C1 - Shandong Jiaotong University,School of Traffic and Logistics Engineering,Jinan,China KW - Obstacle avoidance KW - Motion planning KW - Obstacle KW - Urban environment KW - Path (computing) KW - Collision avoidance SP - 91 EP - 97 ER - TY - JOUR TI - Research on Multi-Constraint Path Optimization and Simulation of UAVs in Urban Low-Altitude Logistics Scenarios PY - 2026 SN - 3106-4620 T2 - Studies in Future Technology DO - 10.64216/3106-4620.26.01.008 UR - https://doi.org/10.64216/3106-4620.26.01.008 DA - 2026-01-25 AU - Runze, Zhuo KW - Path (computing) KW - Trajectory KW - Key (lock) KW - Stability (learning theory) KW - Process (computing) VL - 1 IS - 1 ER - TY - GEN TI - Online Integrated Localization and Communication Service Provisioning for UAV-guided Low-altitude Urban Logistics PY - 2025 DO - 10.1109/globecom59602.2025.11432691 UR - https://doi.org/10.1109/globecom59602.2025.11432691 DA - 2025-12-08 AU - Hu, Cheng C1 - Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China,518060 AU - Bi, Suzhi C1 - Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China,518060 AU - Zeng, Yong C1 - Southeast University,National Mobile Communications Research Laboratory,Nanjing,China,210096 AU - Lin, Xiao-Hui C1 - Shenzhen University,College of Electronics and Information Engineering,Shenzhen,China,518060 KW - Service (business) KW - Provisioning KW - Information and Communications Technology KW - Service provider KW - Key (lock) KW - Mobile telephony SP - 2474 EP - 2480 ER - TY - JOUR TI - Unmanned Aerial Vehicle Impacts on Heat-Strengthened Glass PY - 2019 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2019.2930277 UR - https://doi.org/10.1109/access.2019.2930277 DA - 2019-01-01 AU - Lee, Sang C1 - Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea AU - Jung, Jae‐Wook C1 - Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea AU - Choi, Youngjun C1 - Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea AU - Yoon, Yoonjin C1 - Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea AU - Hong, Jung‐Wuk C1 - Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea LA - en KW - Collision KW - Computer science KW - Cladding (metalworking) KW - Marine engineering KW - Finite element method KW - Environmental science KW - Computer simulation KW - Aerospace engineering KW - Simulation KW - Structural engineering KW - Engineering KW - Materials science KW - Computer security VL - 7 SP - 104269 EP - 104278 AB - Increasing demand for small unmanned aerial vehicles (UAVs) in the logistics and rescue fields, which are closely related to urban environments, has been accompanied by an increase in impact risk to people and property. However, safety regulations for small UAVs in urban areas have not yet been well established, and there are very few studies investigating the collisions between UAVs and building structures that could occur in an urban area. In this study, experimental and numerical investigations are conducted to evaluate the damage resulting from a UAV collision with heat-strengthened glass, a widely used building exterior cladding. Commercially available UAVs were impacted into a series of glass panels at various speeds and angles. The collision processes were recorded with a high-speed camera, and the impact forces of the UAVs were measured using dynamic force sensors. A numerical model of the UAV was then developed to simulate the impacts using the finite element method and verified by comparison with the experimental results. The collision tests performed in this study are expected to provide primary data for establishing UAV safety regulations, and the verified numerical model can be applied to evaluate various collision scenarios for the different UAVs. ER - TY - JOUR TI - Hierarchical path planning for low-altitude logistics UAVs in complex urban environments: an enhanced weighted A* approach with geometric smoothing PY - 2026 PB - Frontiers Media SN - 2296-9144 T2 - Frontiers in Robotics and AI DO - 10.3389/frobt.2026.1802470 UR - https://doi.org/10.3389/frobt.2026.1802470 DA - 2026-08-28 AU - Lei, Tan C1 - School of Modern Logistics, Guangzhou Polytechnic University AU - Liu, Shiming C1 - School of Modern Logistics, Guangzhou Polytechnic University AU - Lei, Yongtao C1 - School of Intelligent Manufacturing Engineering, Guangdong Baiyun University LA - en KW - Motion planning KW - Obstacle KW - Heuristic KW - Path (computing) KW - Leverage (statistics) KW - Sensitivity (control systems) KW - Smoothing KW - Baseline (sea) KW - Turning radius VL - 13 SP - 1802470 EP - 1802470 AB - Importance Autonomous logistics UAVs operating in low-altitude urban airspace face a fundamentally distinct set of challenges from those in high-altitude flight—extreme obstacle density, regulatory geo-fencing, and unpredictable urban canyon wind shear demand path planners that are simultaneously fast, kinematically smooth, and safety-aware. Gap Existing grid-based planners (e.g., standard A) generate geometrically suboptimal paths cluttered with sharp 45°/90° turns that waste energy and increase turning workload, while bio-inspired and sampling-based alternatives suffer from stochastic execution times and high hyperparameter sensitivity that preclude deterministic delivery scheduling. Critically, no prior deterministic planner simultaneously minimizes path length, cumulative turning angle, and obstacle proximity risk within a single unified cost function actually enforced during search. Methodology: We propose a hierarchical “Search-and-Smooth” framework comprising: (1) an Enhanced Weighted A algorithm whose edge-cost function directly encodes all three objectives—path length (Jlen), turning cost (Jturn), and spatial risk ( Jrisk ) — and whose inflated heuristic ( w = 1.5 ) provides a provable ε -suboptimality bound while reducing node expansions; and (2) a greedy Line-of-Sight (LOS) post-processor that removes residual grid-discretization artifacts. Results Monte Carlo simulations ( N = 50 randomized urban scenarios, obstacle density ρobs = 20\% ) demonstrate statistically significant improvements over standard A: computation time reduced by 42.8% ( p < 0.001 , Wilcoxon signed-rank W = 1275 ), cumulative turning angle reduced by 69.9% ( p < 0.001 ; W = 1275 ), and path length maintained within 1.8% of the standard A baseline despite the sub-optimal heuristic inflation. Comparisons against Theta* and a representative PSO baseline confirm the competitive advantages of the proposed method for deterministic, low-latency logistics deployment within the tested synthetic simulation conditions. ER - TY - JOUR TI - GraC 2 Allocator: An RL-Based Hypergraph k -Cut and Coloring Approach to UAV-Assisted Last-Mile Urban Logistics PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2332-7731 T2 - IEEE Transactions on Cognitive Communications and Networking DO - 10.1109/tccn.2026.3658754 UR - https://doi.org/10.1109/tccn.2026.3658754 DA - 2026-01-01 AU - Wang, Jingjing C1 - Information Centric Networking and Blockchain Technology (ICNLab), Peking University, Shenzhen, China AU - Yan, Jiachi C1 - Shenzhen Key Lab for Information Centric Networking and Blockchain Technology (ICNLab), Peking University, Shenzhen, China AU - Yan, Ziwei C1 - Shenzhen Key Lab for Information Centric Networking and Blockchain Technology (ICNLab), Peking University, Shenzhen, China AU - Wen, Rou C1 - Shenzhen Key Lab for Information Centric Networking and Blockchain Technology (ICNLab), Peking University, Shenzhen, China AU - Wu, Jingchun C1 - SF Technology Company Ltd., Shenzhen, China AU - Ren, Yakun C1 - Tencent, Shenzhen, China AU - Liu, Tanren C1 - SF Technology Company Ltd., Shenzhen, China AU - Zou, Xianneng C1 - Tencent, Shenzhen, China AU - Lei, Kai C1 - Shenzhen Key Lab for Information Centric Networking and Blockchain Technology (ICNLab), Peking University, Shenzhen, China KW - Scheduling (production processes) KW - Control reconfiguration KW - Hypergraph KW - Backhaul (telecommunications) KW - Graph KW - Pruning KW - Reinforcement learning KW - Motion planning KW - Computation VL - 12 SP - 5718 EP - 5735 ER - TY - GEN TI - A Machine Learning Based Method for Object Detection and Localization Using a Monocular RGB Camera Equipped Drone PY - 2023 DO - 10.1109/iwcmc58020.2023.10182369 UR - https://doi.org/10.1109/iwcmc58020.2023.10182369 DA - 2023-06-19 AU - Javaid, Abdulrahman C1 - Yokogawa Saudi Arabia Company,Research and Development Department,Al Khobar,Saudi Arabia C1 - Department of Electrical Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia C1 - Research and Development Department, Yokogawa Saudi Arabia Company, Al Khobar, Saudi Arabia AU - Syed, Miswar C1 - King Fahd University of Petroleum & Minerals,Department of Electrical Engineering,Dhahran,Saudi Arabia AU - Baroudi, Uthman C1 - King Fahd University of Petroleum & Minerals,Department of Computer Engineering,Dhahran,Saudi Arabia C1 - Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia LA - en KW - Artificial intelligence KW - Computer vision KW - Drone KW - Computer science KW - Object detection KW - RGB color model KW - Monocular KW - Object (grammar) KW - Pattern recognition (psychology) SP - 1 EP - 6 ER - TY - JOUR TI - Robust SDC-UAV collaborative routing for pandemic response: optimizing urban medical logistics under demand uncertainty PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5801892 UR - https://doi.org/10.2139/ssrn.5801892 DA - 2025-01-01 AU - Yang, Hengfei AU - Wang, Dujuan AU - Peng, Wenhao AU - Cheng, T.C.E. KW - Pandemic KW - Routing (electronic design automation) KW - Supply chain KW - Vehicle routing problem KW - Work (physics) KW - Coronavirus disease 2019 (COVID-19) KW - Robust optimization ER - TY - JOUR TI - Optimization of 4D path planning for urban UAV logistics considering nonlinear energy consumption and dynamic wind fields PY - 2026 PB - Elsevier BV SN - 2214-367X T2 - Travel Behaviour and Society DO - 10.1016/j.tbs.2026.101403 UR - https://doi.org/10.1016/j.tbs.2026.101403 DA - 2026-09-15 AU - Yan, Shanshan AU - Fan, Dongming AU - Ren, Yi AU - Gao, Yuntian AU - Yang, Yang LA - en KW - Energy consumption KW - Motion planning KW - Nonlinear system KW - Consumption (sociology) KW - Path (computing) KW - Energy (signal processing) VL - 46 SP - 101403 EP - 101403 ER - TY - GEN TI - A Deep Reinforcement Learning Enabled Path Optimization Approach for UAV Logistics PY - 2025 DO - 10.1109/ccpqt66408.2025.11383259 UR - https://doi.org/10.1109/ccpqt66408.2025.11383259 DA - 2025-10-24 AU - Liu, Daqian C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China AU - Fu, Weiheng C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China AU - Fei, Bowen C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China AU - Xing, Wenjie C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China AU - Du, Shijing C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China AU - Jiang, Yanji C1 - Software College (College of Artificial Intelligence) Liaoning Technical University,Huludao,China KW - Reinforcement learning KW - Flexibility (engineering) KW - Path (computing) KW - Motion planning KW - Task (project management) KW - Energy (signal processing) KW - Key (lock) KW - Energy minimization SP - 1 EP - 6 ER - TY - JOUR TI - SAFETY ANALYSIS OF GOODS TRANSPORTATION BY UNMANNED AERIAL VEHICLES PY - 2020 SN - 1992-3252 T2 - World of Transport and Transportation DO - 10.30932/1992-3252-2019-17-5-286-297 UR - https://doi.org/10.30932/1992-3252-2019-17-5-286-297 DA - 2020-06-07 AU - Shvetsov, Alexey C1 - Far Eastern State Transport University (FESTU) AU - Shvetsov, Alexey C1 - North-East Federal University (NEFU) LA - en KW - Aviation KW - Drone KW - Transport engineering KW - Aviation safety KW - Aeronautics KW - Obstacle KW - Agency (philosophy) KW - Cruise KW - Engineering VL - 17 IS - 5 SP - 286 EP - 297 AB - Modern transport companies around the world actively study the possibility of intra-urban transportation of goods using unmanned aerial vehicles (UAV).The objective of the study is to reveal the existing problems preventing full-scale introducing of UAVs into the operations of logistics centers that accomplish cargo delivery in modern megacities as well as to propose a possible option for their solution.The study conducted applying the methods of comparative analysis and generalization showed that the main obstacle to the use of unmanned aerial vehicles for cargo delivery is currently unsettled problem of safety when UAVs operate in the urban environment.The study has analyzed the worldwide UAV traffic safety management programs. Among the programs reviewed were U-Space (the program is implemented by the European Commission and other participants); NASA Unmanned Aircraft System Traffic Management (the program is implemented by NASA and the US Federal Aviation Administration); European Aviation Safety Agency drone categories (the program is implemented by European Aviation Safety Agency (EASA)); Urban Traffic Management of Unmanned Aircraft System (the program is implemented by Air Traffic Management Research Institute (ATMRI) and other participants). The analysis showed that these programs paid a lot of attention to preventing the UAVs collisions with other air vehicles. But almost no attention was paid to the fact that the flight routes of unmanned aerial vehicles would intersect with the ground highways.The analysis carried out in the article allows us to conclude that one of the solutions to the problem of UAV implementation in the field of cargo delivery is the development of a new concept of UAV traffic safety in the urban conditions, which will take into account the threat of collision of cargo UAV not only with air vehicles but also with ground ones. The concept should determine the requirements for thetechnology for safe traffic of drones over the main surface transport routes including highways, highspeed railways, etc. The development of such a concept will be a turning point for starting the fullscale use of UAVs as a new and effective means of cargo delivery in the city, which, in its turn, willcreate the next-generation transport infrastructure in the cities. ER - TY - GEN TI - A Two Level Structure for Efficient Logistics in the Urban Airspace PY - 2024 DO - 10.1109/codit62066.2024.10708256 UR - https://doi.org/10.1109/codit62066.2024.10708256 DA - 2024-07-01 AU - Lamiscarre, Bruno C1 - Neometsys AU - Olanrewaju, Oludolapo C1 - Durban University of Technology AU - Ramos, Alexandre C1 - Universidade Federal de Itajubá AU - Mora‐Camino, Félix C1 - Durban University of Technology LA - en KW - Computer science KW - Aeronautics KW - Engineering VL - 4 SP - 1107 EP - 1113 ER - TY - JOUR TI - Drone routing problem with truck: Optimization and quantitative analysis PY - 2023 PB - Elsevier BV SN - 0957-4174 T2 - Expert Systems with Applications DO - 10.1016/j.eswa.2023.120260 UR - https://doi.org/10.1016/j.eswa.2023.120260 DA - 2023-04-27 AU - Jeong, Ho C1 - School of Industrial Engineering, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States AU - Lee, Seokcheon C1 - School of Industrial Engineering, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States LA - en KW - Drone KW - Truck KW - Computer science KW - Heuristic KW - Routing (electronic design automation) KW - Vehicle routing problem KW - Operations research KW - Computer network KW - Engineering KW - Artificial intelligence KW - Automotive engineering VL - 227 SP - 120260 EP - 120260 ER - TY - GEN TI - A Cloud-Edge Collaborative Framework for Autonomous UAV-Based Medical Logistics PY - 2025 DO - 10.1109/cloudcom67567.2025.11331555 UR - https://doi.org/10.1109/cloudcom67567.2025.11331555 DA - 2025-11-14 AU - Zhao, Mingze C1 - Shenzhen MSU-BIT University,Department of Biology,Shenzhen,China AU - Liu, Ruida C1 - Shenzhen MSU-BIT University,Department of Mathematics,Shenzhen,China AU - Zhao, Jueming C1 - Shenzhen MSU-BIT University,Department of Engineering,Shenzhen,China AU - Gao, Haixu C1 - Shenzhen MSU-BIT University,Department of Biology,Shenzhen,China AU - Zeng, Runhao C1 - Artificial intelligence Research Institute, Shenzhen MSU-BIT University,Shenzhen,China KW - Robustness (evolution) KW - Obstacle avoidance KW - Payload (computing) KW - Obstacle KW - Field (mathematics) KW - Cloud computing KW - Motion planning KW - Automation SP - 1 EP - 7 ER - TY - GEN TI - A Hybrid Method for Multi-UAV Cooperative Task Allocation in Urban Logistics based on Sparrow Search and Ant Colony Optimization PY - 2026 DO - 10.1109/ccdc69976.2026.11561795 UR - https://doi.org/10.1109/ccdc69976.2026.11561795 DA - 2026-05-15 AU - Li, Jingjie C1 - Equipment of Ministry of Education, Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial,Dalian,China AU - H, Sun C1 - Equipment of Ministry of Education, Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial,Dalian,China AU - Zheng, X C1 - Equipment of Ministry of Education, Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial,Dalian,China AU - Luo, Yanshou C1 - Beijing Institute of Astronautical, Systems Engineering,Beijing,China AU - Zhang, Xutong C1 - School of Control Science and, Engineering, Dalian University of Technology,Dalian,China AU - An, Jun C1 - Beijing Institute of Astronautical, Systems Engineering,Beijing,China KW - Ant colony optimization algorithms KW - Task (project management) KW - Sparrow KW - Ant colony KW - Metaheuristic KW - Key (lock) SP - 1810 EP - 1815 ER - TY - GEN TI - Multi-Algorithm-Based UAV Routing Optimization for Low-Altitude Logistics Scenarios PY - 2026 SN - 0148-7191 T2 - SAE technical papers on CD-ROM/SAE technical paper series DO - 10.4271/2026-99-0590 UR - https://doi.org/10.4271/2026-99-0590 DA - 2026-07-10 AU - Wang, Yi-Xiang C1 - Ningxia Institute of Science and Technology AU - Guo, Jing C1 - Ningxia Institute of Science and Technology AU - Du, Ning C1 - Beijing Guanghui Jintong Education Technology Co, LTD AU - Wei, Mengju C1 - Beijing Guanghui Jintong Education Technology Co, LTD KW - Simulated annealing KW - Greedy algorithm KW - Minification KW - Scheduling (production processes) KW - Convergence (economics) KW - Vehicle routing problem KW - Job shop scheduling KW - Genetic algorithm VL - 1 ER - TY - JOUR TI - Multi-UAVs cooperative task allocation and path planning for low-altitude logistics PY - 2026 PB - SAGE Publishing SN - 1077-5463 T2 - Journal of Vibration and Control DO - 10.1177/10775463261467308 UR - https://doi.org/10.1177/10775463261467308 DA - 2026-07-30 AU - Li, Zongwei C1 - Shaanxi Polytechnic University AU - Zhang, Guang C1 - Shaanxi Polytechnic University AU - Gao, Heyun C1 - Shaanxi Polytechnic University LA - en KW - Robustness (evolution) KW - Motion planning KW - Task (project management) KW - Reinforcement learning KW - Information exchange KW - Resource allocation KW - Path (computing) KW - Bilevel optimization ER - TY - JOUR TI - Research progress on key technologies of hierarchical cooperation of low-altitude logistics UAV PY - 2026 T2 - DOAJ (DOAJ: Directory of Open Access Journals) DO - 10.13374/j.issn2095-9389.2025.08.18.001 UR - https://doi.org/10.13374/j.issn2095-9389.2025.08.18.001 DA - 2026-04-01 AU - DENG, Jian C1 - College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China AU - ZHANG, Honghai C1 - College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China AU - Zhang, Yuetan C1 - College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China AU - Hua, Mingzhuang C1 - College of General Aviation and Flight, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China LA - zh KW - Key (lock) KW - Task (project management) KW - Particle swarm optimization KW - Trajectory KW - Adaptability KW - Bidding KW - Cluster analysis KW - Layer (electronics) AB - The layered collaborative technology of low-altitude logistics of unmanned aerial vehicles (UAVs) serves as the core support for breaking through bottlenecks in full-process logistics distribution. Centering on the three-layer architecture of collaborative task allocation, collaborative trajectory planning, and dynamic trajectory replanning, this paper provides a comprehensive overview of the research progress in key technologies. The collaborative task allocation layer focuses on order clustering and multi-UAV matching, covering algorithms based on optimization models (exact, heuristic, and meta-heuristic optimization), market mechanisms (auction bidding and game-theory-based alliance optimization), and swarm intelligence (swarm intelligence optimization and reinforcement learning). These algorithms are specifically designed to address the challenges of multiconstraint coupling and collaborative task allocation. By adopting techniques such as constraint decoupling, collision-free trajectory design, and priority-aware algorithms, this layer improves the allocation efficiency and dynamic adaptability of the system to ensure that UAVs are assigned to the corresponding logistical tasks in complex and changing environments. The collaborative trajectory planning layer aims to generate three-dimensional airspace paths by combining algorithms for precise optimization (accurate optimization and multiconstraint planning), swarm intelligence (evolutionary optimization and swarm optimization), and intelligent fusion (reinforcement learning-based planning and hierarchical hybrid-driven planning). By integrating these algorithms, this layer achieves a balanced consideration of three critical factors: obstacle avoidance, energy consumption, and operational efficiency. This balance not only enhances the safety of UAV trajectories but also improves the generalization ability of the trajectory planning system to enable it to adapt to different low-altitude logistics scenarios (e.g., urban residential areas, suburban industrial parks, and rural areas with varying geographical features). The dynamic trajectory replanning layer targets abrupt environmental changes such as sudden weather disturbances (e.g., strong winds and sudden rain), temporary airspace restrictions, and unexpected obstacles during UAV flight. Relying on search and traversal algorithms (deterministic graph search and random hierarchical search), physical modeling (artificial potential field-based local planning and heuristic-assisted physical replanning), and intelligence and learning (intelligent optimization, reinforcement learning and deep adaptive planning), this layer realizes rapid adjustments to UAV trajectories. Such adjustments are crucial to ensure the real-time performance of the logistics system (allowing UAVs to respond promptly to unexpected situations without significant distribution delays) and safeguarding system robustness (ensuring stable logistics operations amid environmental uncertainties). A comprehensive analysis shows current technical bottlenecks: strong multiconstraint coupling (interactions between flight time, load capacity, airspace regulations, and energy limits hinder optimal task allocation and trajectory planning); insufficient dynamic adaptability (existing algorithms struggle to adjust quickly to logistics environment changes or sudden task requirement shifts); low large-scale collaboration efficiency (complex coordination among multiple UAVs reduces the overall operational efficiency); and scenario disconnection (many research results based on idealized simulations fail to align with actual complex low-altitude logistics scenarios, causing application difficulties). Looking ahead, future innovations should move toward cross-layer collaborative optimization (integrating the three layers for overall system optimization instead of independent layer optimization); scenario-specific customization and integration (developing targeted technologies/algorithms based on practical scenario characteristics to enhance adaptability and practicality); large-scale swarm intelligence (further exploring swarm intelligence to improve coordination efficiency of large UAV clusters); dynamic robust design (strengthening research to maintain system stability amid complex unexpected environmental changes); and green energy conservation (developing energy-saving UAV technologies and optimizing trajectory planning to reduce energy consumption, in line with green sustainable development). Advancing these innovations will promote the large-scale implementation of low-altitude logistics UAV systems to transform and upgrade the logistics distribution industry. ER - TY - RPRT TI - Urban commercial drone logistics in Singapore: blueprint for implementation PY - 2025 PB - Nanyang Technological University T2 - DR-NTU (Nanyang Technological University) DA - 2025-01-01 AU - Rikat, Mohammad LA - en KW - Blueprint KW - Drone KW - Command and control KW - Control (management) KW - State (computer science) KW - Certainty ER - TY - JOUR TI - A Risk-Aware Three-Layer Co-Optimization Framework for Routing and Scheduling of Multiple Logistics UAVs in Resource-Constrained Urban Low-Altitude Scenarios PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6750140 UR - https://doi.org/10.2139/ssrn.6750140 DA - 2026-01-01 AU - Zheng, Zhengang C1 - Xinjiang University C1 - Xinjiang University, Xinjiang, China AU - Li, Xinkai C1 - Xinjiang University C1 - Xinjiang University, Xinjiang, China AU - Meng, Yue C1 - Xinjiang University C1 - Xinjiang University, Xinjiang, China AU - Zhang, Hongli C1 - Xinjiang University C1 - Xinjiang University, Xinjiang, China AU - Wang, X. C1 - Tsinghua University C1 - Tsinghua University, Beijing, 100084, China KW - Scheduling (production processes) KW - Obstacle KW - Motion planning KW - Dynamic priority scheduling KW - Baseline (sea) KW - Tardiness KW - Time horizon KW - Dynamic programming ER - TY - JOUR TI - Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers PY - 2023 PB - IEEE Communications Society SN - 2644-125X T2 - IEEE Open Journal of the Communications Society DO - 10.1109/ojcoms.2023.3334489 UR - https://doi.org/10.1109/ojcoms.2023.3334489 DA - 2023-11-22 AU - Tariq, Zain C1 - Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar C1 - College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar AU - Baccour, Emna C1 - Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar C1 - College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar AU - Erbad, Aiman C1 - Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar C1 - College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar AU - Hamdi, Mounir C1 - Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar C1 - College of Science and Engineering, Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar LA - en KW - Reinforcement learning KW - Computer science KW - Jamming KW - Wireless KW - Beamforming KW - Context (archaeology) KW - Wireless network KW - Leverage (statistics) KW - Smart city KW - Telecommunications KW - Computer security KW - Artificial intelligence VL - 5 SP - 15 EP - 37 AB - The evolving landscape of beyond 5G and 6G wireless communication systems in smart urban environments faces numerous interference-related challenges posed by legitimate and illicit devices. In this context, Intelligent Reflecting Surfaces (IRS) have emerged as a promising solution to mitigate interference caused by obstacles and unknown jamming devices. Existing techniques mainly focus on mitigating the impact of a single jammer in IRS-assisted communications systems, which affects both stationary and mobile devices. Additionally, these approaches target a single objective, such as minimizing the energy, enhancing the transmission rate, or maximizing the Signal-to-Interference-plus-Noise Ratio (SINR), which restrains the performance of the system. This paper offers a comprehensive anti-jamming solution for securing wireless communications in a smart city urban environment comprising diverse public events such as sporting events, parades, festivals, and exhibitions. The focus is on maintaining essential services like security, law enforcement, logistics, emergency response, crowd management, and public health. We introduce a Reinforcement Learning-based technique for UAV-mounted IRS, optimizing trajectory and phase shift beamforming to counteract the disruptive impact of jammers, ensuring reliable communication in dynamic, security-sensitive settings Our approach also seeks to achieve multi-objective optimization by striking a balance between transmission rate and energy consumption in this highly challenging environment. The formulated optimization is computationally complex due to its combinatorial nature. Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. Experimental results demonstrate the effectiveness of our proposed DDPGbased approach in outperforming other RL algorithms. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%. ER - TY - GEN TI - Internet of Autonomous Vehicles for The Distribution System of Smart Cities PY - 2024 SN - 0148-7191 T2 - SAE technical papers on CD-ROM/SAE technical paper series DO - 10.4271/2024-01-2882 UR - https://doi.org/10.4271/2024-01-2882 DA - 2024-04-08 AU - Ma, Shizi C1 - Coventry University AU - Wang, Sheng C1 - Harbin Institute of Technology AU - Ma, Zhitao C1 - Shenzhen Zhongwe Electronic AU - QI, Zhiguo C1 - BYD Auto Industry LA - en KW - The Internet KW - Computer science KW - Distribution (mathematics) KW - Internet of Things KW - Telecommunications KW - Computer security KW - World Wide Web VL - 1 ER - TY - JOUR TI - OPTIMIZING THE MOVEMENT OF UNMANNED AERIAL VEHICLES (UAVS) IN LOGISTICS ACTIVITIES BASED ON DIGITAL CONTROL PY - 2025 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.21405851 UR - https://doi.org/10.5281/zenodo.21405851 DA - 2025-04-03 AU - Ugli, Sidikov C1 - TSUE, 2nd year master's student in the "Digital Economy" faculty LA - uz KW - Obstacle KW - Global Positioning System KW - Control (management) KW - Movement control KW - Obstacle avoidance KW - Energy (signal processing) KW - Movement (music) KW - Control system AB - Unmanned Aerial Vehicles (UAVs) are revolutionizing logistics by providing efficient, autonomous, and cost-effective delivery solutions. This study explores how digital control systems, AI-driven navigation, and sensor integration enhance flight stability, navigation accuracy, energy efficiency, and obstacle avoidance. Experimental results indicate that PID-based control reduces oscillations by 30%, AI-powered GPS corrections improve accuracy by 20%, and optimized energy management extends flight duration by 15%. Additionally, computer vision-based obstacle detection enables a 95% success rate in urban navigation. These findings highlight the critical role of AI and digital control in optimizing UAV logistics operations, making them safer, more precise, and energy-efficient for large-scale deployment. ER - TY - JOUR TI - OPTIMIZING THE MOVEMENT OF UNMANNED AERIAL VEHICLES (UAVS) IN LOGISTICS ACTIVITIES BASED ON DIGITAL CONTROL PY - 2025 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.21405850 UR - https://doi.org/10.5281/zenodo.21405850 DA - 2025-04-03 AU - Ugli, Sidikov C1 - TSUE, 2nd year master's student in the "Digital Economy" faculty LA - uz KW - Obstacle KW - Global Positioning System KW - Control (management) KW - Movement control KW - Obstacle avoidance KW - Energy (signal processing) KW - Movement (music) KW - Control system AB - Unmanned Aerial Vehicles (UAVs) are revolutionizing logistics by providing efficient, autonomous, and cost-effective delivery solutions. This study explores how digital control systems, AI-driven navigation, and sensor integration enhance flight stability, navigation accuracy, energy efficiency, and obstacle avoidance. Experimental results indicate that PID-based control reduces oscillations by 30%, AI-powered GPS corrections improve accuracy by 20%, and optimized energy management extends flight duration by 15%. Additionally, computer vision-based obstacle detection enables a 95% success rate in urban navigation. These findings highlight the critical role of AI and digital control in optimizing UAV logistics operations, making them safer, more precise, and energy-efficient for large-scale deployment. ER - TY - GEN TI - Optimization of Collaborative Truck-Drone-Subway Delivery for Urban Logistics PY - 2025 DO - 10.46254/an15.20250087 UR - https://doi.org/10.46254/an15.20250087 DA - 2025-02-18 AU - Han, So AU - Song, Byung LA - en KW - Truck KW - Drone KW - Transport engineering KW - City logistics KW - Computer science KW - Aeronautics KW - Engineering KW - Automotive engineering AB - The truck-drone hybrid delivery system has been proposed as an alternative for urban logistics due to its advantages in fast delivery, cost-efficiency, and environmental benefits. However, UAVs face limitations in battery capacity and payload, which make it challenging to meet the increasing demand for logistics services. Additionally, there are constraints in building new logistics facilities in urban areas. Underground logistics has emerged as an alternative solution to address these problems. By utilizing the subway network, the system can avoid urban traffic congestion and manage logistics flows efficiently. Moreover, it minimizes the cost of establishing new facilities by making the most of existing infrastructure. In this study, we propose a new urban delivery system that combines the existing truck-drone hybrid model with the subway network. The established subway network and stations are used to transport goods from suburban areas to urban areas, where they also function as urban logistics hubs. A truck and drones handle the last-mile delivery to customers from the subway stations. Both the truck and multiple drones operate independently; the truck does not act as a mothership for the drones, and they perform deliveries across multiple logistics depots. We developed a mathematical optimization model for a multi-depot, multi-trip vehicle routing problem with time windows. The developed model was validated through numerical experiments, demonstrating its practical applicability to urban delivery services.The truck-drone hybrid delivery system has been proposed as an alternative for urban logistics due to its advantages in fast delivery, cost-efficiency, and environmental benefits. However, UAVs face limitations in battery capacity and payload, which make it challenging to meet the increasing demand for logistics services. Additionally, there are constraints in building new logistics facilities in urban areas. Underground logistics has emerged as an alternative solution to address these problems. By utilizing the subway network, the system can avoid urban traffic congestion and manage logistics flows efficiently. Moreover, it minimizes the cost of establishing new facilities by making the most of existing infrastructure. In this study, we propose a new urban delivery system that combines the existing truck-drone hybrid model with the subway network. The established subway network and stations are used to transport goods from suburban areas to urban areas, where they also function as urban logistics hubs. A truck and drones handle the last-mile delivery to customers from the subway stations. Both the truck and multiple drones operate independently; the truck does not act as a mothership for the drones, and they perform deliveries across multiple logistics depots. We developed a mathematical optimization model for a multi-depot, multi-trip vehicle routing problem with time windows. The developed model was validated through numerical experiments, demonstrating its practical applicability to urban delivery services. ER - TY - JOUR TI - Exploring Window-Level Drone Delivery in Urban Air Logistics PY - 2025 SN - 2791-0210 T2 - Highlights in Science Engineering and Technology DO - 10.54097/1b1x0z19 UR - https://doi.org/10.54097/1b1x0z19 DA - 2025-12-30 AU - Wang, Yiwen C1 - School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China KW - Adaptability KW - Drone KW - Obstacle KW - Scalability KW - Field (mathematics) KW - Key (lock) KW - Trajectory KW - Efficient energy use KW - Smart city VL - 160 SP - 140 EP - 148 AB - Window-level UAVs delivery applies to the emerging field of urban air logistics, enabling autonomous UAVs to deliver packages directly to building windows. However, unprecedented levels of precision, perception, and adaptability in complex, GPS-denied environments are required to achieve reliable window-level operation. This paper provides a systematic review of the key technological advances making this challenging scenario possible. How the fusion of visual, LiDAR, and inertial sensing, which is enhanced by deep learning and semantic SLAM, enables centimeter-level localization among urban canyons. Robust window detection is addressed through hybrid methods that combine geometric efficiency with deep semantic understanding, improving accuracy under architectural repetition and occlusion. Furthermore, intelligent navigation strategies have been developed to support real-time obstacle avoidance and smooth trajectory generation in cluttered airspace. These incorporate reinforcement learning with dynamic window constraints and factor graph optimization. Critical challenges remain in sensor resilience, energy efficiency during vertical operation, generalization across diverse urban forms, and scalable multi-UAV coordination despite progress. This review integrates current advancements and highlights future research needs, thereby providing a foundational reference for the development of reliable, precise, scalable and adaptable window-level UAV delivery systems in smart cities. ER - TY - JOUR TI - Transportation and carbon emissions costs minimization for time-dependent vehicle routing problem with drones PY - 2025 PB - Elsevier BV SN - 0305-0548 T2 - Computers & Operations Research DO - 10.1016/j.cor.2024.106963 UR - https://doi.org/10.1016/j.cor.2024.106963 DA - 2025-01-04 AU - Peng, Yong C1 - School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China AU - Ren, Zhi C1 - China Railway Changjiang Transport Design Group Company Ltd., Chongqing 400000, China AU - Yu, Dennis C1 - The David D. Reh School of Business, Clarkson University, Potsdam, NY 13699, United States AU - Zhang, Yonghui C1 - School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China LA - en KW - Drone KW - Vehicle routing problem KW - Minification KW - Computer science KW - Routing (electronic design automation) KW - Operations research KW - Mathematical optimization KW - Environmental science KW - Transport engineering KW - Mathematics KW - Computer network KW - Engineering VL - 176 SP - 106963 EP - 106963 ER - TY - GEN TI - UAV Path Planning in Low Altitude Urban Electromagnetic Environments PY - 2025 DO - 10.1109/ccns66849.2025.11338032 UR - https://doi.org/10.1109/ccns66849.2025.11338032 DA - 2025-10-31 AU - Tian, Jinfeng C1 - School of Automation and Information Engineering,Sichuan University of Science & Engineering,Yibin,China,644000 AU - Yan, Yuehao C1 - Civil Aviation Flight University of China,Chengdu,China,610000 AU - Zhou, Changyu C1 - School of Automation and Information Engineering,Sichuan University of Science & Engineering,Yibin,China,644000 AU - Han, Qiang C1 - School of Automation and Information Engineering,Sichuan University of Science & Engineering,Yibin,China,644000 KW - Motion planning KW - Heuristic KW - Path (computing) KW - Electromagnetic interference KW - Variable (mathematics) KW - Routing (electronic design automation) KW - Function (biology) SP - 200 EP - 208 ER - TY - GEN TI - UAV Path Planning in Urban Environments Based on RRT Algorithm PY - 2025 DO - 10.1109/spic68204.2025.11470760 UR - https://doi.org/10.1109/spic68204.2025.11470760 DA - 2025-11-28 AU - Sui, Chengxuan C1 - School of Equipment Engineering, Shenyang University of Technology,Liaoning,Shenyang,China AU - Liu, Qingquan C1 - School of Equipment Engineering, Shenyang University of Technology,Liaoning,Shenyang,China AU - WANG, J C1 - School of Equipment Engineering, Shenyang University of Technology,Liaoning,Shenyang,China AU - Zhang, Rui C1 - School of Equipment Engineering, Shenyang University of Technology,Liaoning,Shenyang,China AU - Wang, Youce C1 - School of Equipment Engineering, Shenyang University of Technology,Liaoning,Shenyang,China KW - Motion planning KW - Path (computing) KW - Key (lock) KW - Trajectory SP - 404 EP - 407 ER - TY - JOUR TI - An Improved RRT Algorithm Based on Bézier Curves for Logistics Delivery UAV Path Planning PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10070494 UR - https://doi.org/10.3390/drones10070494 DA - 2026-06-29 AU - Zheng, Fang C1 - Equipment Management and Unmanned Aerial Vehicle College, Air Force Engineering University, Xi’an 710082, China AU - Zhang, Pengtao C1 - Equipment Management and Unmanned Aerial Vehicle College, Air Force Engineering University, Xi’an 710082, China AU - Fan, Xiaolin C1 - Lynxi Technologies Co., Ltd., Beijing 100080, China AU - Liu, Yan C1 - Equipment Management and Unmanned Aerial Vehicle College, Air Force Engineering University, Xi’an 710082, China LA - en KW - Obstacle avoidance KW - Path (computing) KW - Motion planning KW - Smoothness KW - Curvature KW - Random tree KW - Obstacle KW - Tool path VL - 10 IS - 7 SP - 494 EP - 494 AB - This paper investigates the path-planning problem of unmanned aerial vehicles (UAVs) for logistics delivery in urban environments. The impact of real-time obstacle avoidance and path smoothness on the flyability of UAVs remains a challenge in existing research. To address the issue that the path generated by the traditional Rapidly exploring Random Tree (RRT) algorithm exhibits a sudden slope change at connection points, which makes the UAV non-flyable, this paper proposes an improved algorithm that combines the traditional RRT algorithm with Bézier curves. The proposed real-time path generation strategy consists of two stages. The first stage constructs the environment model. The second stage integrates the RRT algorithm with Bézier curves, enabling the generated route to achieve real-time obstacle avoidance while being smooth and free of curvature discontinuities. Simulation experiments compare the improved algorithm with the traditional RRT algorithm and global path optimization methods. The experimental results show that the improved algorithm has the advantage of real-time obstacle avoidance, and the generated route is smooth at connection points with no curvature discontinuities, thereby ensuring good flyability. ER - TY - GEN TI - Collaborative flight plan scheduling for urban low-altitude UAV PY - 2026 DO - 10.1117/12.3095819 UR - https://doi.org/10.1117/12.3095819 DA - 2026-01-08 AU - Liu, Zhaoxuan C1 - Beijing Jiaotong University (China) AU - Wang, Ruyu C1 - Beijing Jiaotong University (China) AU - Ju, Yufei C1 - Beijing Jiaotong University (China) AU - Du, Bohan C1 - Beijing Jiaotong University (China) KW - Beijing KW - Scheduling (production processes) KW - Python (programming language) KW - Flight plan KW - Plan (archaeology) KW - Analytic hierarchy process KW - Process (computing) KW - Air traffic control KW - Digital elevation model SP - 29 EP - 29 ER - TY - GEN TI - UAV-to-Ground Link Optimization Using HHO for Dense Urban Networks PY - 2025 DO - 10.1109/mecon67253.2025.11277219 UR - https://doi.org/10.1109/mecon67253.2025.11277219 DA - 2025-07-21 AU - Ullah, Naeem C1 - Bacha Khan University Charsadda,Department of Computer Science,Pakistan AU - Ali, Farman C1 - Multimedia University,Faculty of AI and Engineering,Cyberjaya,Malaysia,63100 AU - Ullah, Yasir C1 - Multimedia University,Faculty of AI and Engineering,Cyberjaya,Malaysia,63100 AU - Roslee, Mardeni C1 - Multimedia University,Faculty of AI and Engineering,Cyberjaya,Malaysia,63100 AU - Jizat, Noorlindawaty C1 - Multimedia University,Faculty of AI and Engineering,Cyberjaya,Malaysia,63100 AU - Wali, Qamar C1 - Multimedia University,Faculty of AI and Engineering,Cyberjaya,Malaysia,63100 AU - Ahmad, Shabeer C1 - Beijing University of Posts and Telecommunications,School of Electronic Engineering,Beijing,China AU - Ismail, Azmi C1 - Centre of Excellence for Intelligent Network,Telekom Malaysia Research & Development,Cyberjaya,Malaysia,63000 LA - en KW - Key (lock) KW - Latency (audio) KW - Channel (broadcasting) KW - Cellular network KW - Energy (signal processing) KW - Efficient energy use KW - Base station KW - Drone SP - 1 EP - 6 ER - TY - GEN TI - A Real-Time System for Scheduling and Managing UAV Delivery in Urban Areas PY - 2025 DO - 10.1109/robio66223.2025.11378031 UR - https://doi.org/10.1109/robio66223.2025.11378031 DA - 2025-12-03 AU - Liu, Han C1 - School of Computer Science, Sun Yat-sen University,Guangzhou,China AU - Liu, T. C1 - School of Computer Science, Sun Yat-sen University,Guangzhou,China AU - Huang, Kai C1 - School of Computer Science, Sun Yat-sen University,Guangzhou,China KW - Scheduling (production processes) KW - Leverage (statistics) KW - Dynamic priority scheduling KW - Job shop scheduling KW - Fair-share scheduling KW - Two-level scheduling SP - 2131 EP - 2136 ER - TY - JOUR TI - Infrastructure Transitions Precede Congestion: A Regime-Transition Framework for UAV Logistics and Airspace Planning PY - 2026 T2 - Preprints.org DO - 10.20944/preprints202605.2095.v1 UR - https://doi.org/10.20944/preprints202605.2095.v1 DA - 2026-06-01 AU - Liu, Bingqing AU - Yang, Qunting AU - Xie, Chunsheng AU - Song, Yongxuan AU - Zhang, Wen AU - Yang, Qian AU - Jin, Pengyu KW - Time horizon KW - Capacity planning KW - Differential (mechanical device) KW - Lagging KW - Total cost KW - Constraint (computer-aided design) KW - Depot KW - Capacity utilization AB - Urban UAV logistics planning optimizes depot locations for delivery distance while treating airspace capacity as a fixed constraint. We show that this decoupling is not merely a modeling simplification — it produces a structural error that cost monitoring cannot detect in time. When depot configurations begin diverging under airspace capacity pressure, the window for low-cost infrastructure correction closes before any cost signal appears.The central finding is a lead–lag relationship between structural and cost responses: at demand levels where total cost differences remain negligible, depot layouts already diverge substantially under airspace capacity pressure. Cost is a lagging indicator, not a planning trigger.To close this gap, we derive the Ω index, a closed-form demand threshold with three city-level parameters — capacity gap ΔC (from building-footprint data via a FAR volumetric method), depot distance differential Δd (from road-network geometry), and mean OD delivery distance d (from weighted building-centroid sampling) — separating a regime where distance-based planning is analytically sufficient from one where airspace capacity must be internalized into the location decision. Applied to Dongli District, Tianjin, the threshold lies above current UAV density but within a foreseeable planning horizon given observed growth rates in Chinese pilot cities. The depot relocation distance Δreloc provides a leading structural indicator, detectable well before any cost signal emerges. The Ω index gives planners a computable early-warning trigger for airspace-integrated infrastructure review — before congestion, not after. ER - TY - JOUR TI - A Real-Time System for Scheduling and Managing UAV Delivery in Urban Areas PY - 2024 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2412.11590 UR - https://doi.org/10.48550/arxiv.2412.11590 DA - 2024-12-16 AU - Liu, Han AU - Liu, Tian AU - Huang, Kai LA - en KW - Scheduling (production processes) KW - Computer science KW - Real-time computing KW - Operations management KW - Engineering AB - As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks, efficient swarm scheduling and management are crucial. In this paper, we propose a real-time scheduling and management system based on the ``Airport-Unloading Station" model, aiming to bridge the gap between high-level scheduling algorithms and low-level execution systems. This system, acting as middleware, accurately translates the requirements from the scheduling layer into specific execution instructions, ensuring that the scheduling algorithms perform effectively in real-world environments. Additionally, we implement three collaborative scheduling schemes involving autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and ground staff to further optimize overall delivery efficiency. Through extensive experiments, this study demonstrates the rationality and feasibility of the proposed management system, providing practical solution for the commercial application of UAVs delivery in urban. Code: https://github.com/chengji253/UAVDeliverySystem ER - TY - GEN TI - Safety Analysis of Low-Altitude UAV Logistics Systems Driven by STPA-AHP from an STS Perspective PY - 2025 DO - 10.1109/iceiom65271.2025.11239850 UR - https://doi.org/10.1109/iceiom65271.2025.11239850 DA - 2025-08-01 AU - Qiu, Shumao C1 - China Aero-Polytechnology Establishment,Beijing,China AU - Pang, Shujie C1 - China Aero-Polytechnology Establishment,Beijing,China AU - Lin, Cong C1 - China Aero-Polytechnology Establishment,Beijing,China AU - Peng, Wensheng C1 - China Aero-Polytechnology Establishment,Beijing,China KW - Process (computing) KW - Perspective (graphical) KW - Hierarchy KW - Control (management) KW - System safety KW - Analytic hierarchy process KW - Complex system SP - 491 EP - 498 ER - TY - JOUR TI - TeCoR-UAV: A Two-Stage Topology Extraction and Cooperative Routing Algorithm for Low-Altitude Logistics PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2079-9292 T2 - Electronics DO - 10.3390/electronics15132939 UR - https://doi.org/10.3390/electronics15132939 DA - 2026-07-05 AU - , 丁步扬 C1 - College of Computer and Information Science, Southwest University, Chongqing 400715, China AU - Ni, Weijun C1 - College of Computer and Information Science, Southwest University, Chongqing 400715, China AU - Luo, Yi C1 - Software Department, Beijing Institute of Control Engineering, Beijing 100190, China AU - Liu, Zhiming C1 - College of Computer and Information Science, Southwest University, Chongqing 400715, China AU - Li, Nianyu C1 - ZGC Laboratory, Beijing 100190, China AU - Li, Jialong C1 - Waseda Institute for Advanced Study, Waseda University, Tokyo 169-0051, Japan AU - Zhang, M C1 - College of Computer and Information Science, Southwest University, Chongqing 400715, China LA - en KW - Executable KW - Solver KW - Key (lock) KW - Benchmark (surveying) KW - Adaptability KW - Payload (computing) KW - Routing (electronic design automation) KW - Task (project management) KW - Genetic algorithm KW - Quality of service VL - 15 IS - 13 SP - 2939 EP - 2939 AB - Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. ER - TY - GEN TI - Research on UAV Logistics Operation Mode Based on Low Altitude Air Corridor Hierarchical Management PY - 2025 DO - 10.1109/leficlu65987.2025.11297260 UR - https://doi.org/10.1109/leficlu65987.2025.11297260 DA - 2025-09-26 AU - Chen, Biaoxin C1 - Guangdong University of Science and Technology,Dongguan Social Science Master Chen Biaoxin Digital Economy Studio,Dongguan City,China AU - Li, Jiancai C1 - Guangdong University of Science and Technology,Dongguan Social Science Master Chen Biaoxin Digital Economy Studio,Dongguan City,China KW - Drone KW - Scheduling (production processes) KW - Scalability KW - Channel (broadcasting) KW - Robustness (evolution) KW - Operational efficiency KW - Task (project management) SP - 174 EP - 178 ER - TY - JOUR TI - Evaluating the impact of air corridors on the environment and public interests PY - 2025 PB - Elsevier BV SN - 1361-9209 T2 - Transportation Research Part D Transport and Environment DO - 10.1016/j.trd.2025.104732 UR - https://doi.org/10.1016/j.trd.2025.104732 DA - 2025-04-09 AU - Wang, Sheng C1 - Harbin Institute of Technology (Weihai), Weihai 264209, China AU - Liu, Haishi C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China AU - Rinaldi, Marco C1 - Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino 10129 Torino, Italy AU - Tsang, Yung C1 - Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China LA - en KW - Environmental science KW - Environmental planning KW - Transport engineering KW - Engineering KW - Civil engineering VL - 143 SP - 104732 EP - 104732 ER - TY - JOUR TI - Improving Unmanned Aerial Vehicle Security as a Factor in Sustainable Development of Smart City Infrastructure: Automatic Dependent Surveillance–Broadcast (ADS-B) Data Protection PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su17041553 UR - https://doi.org/10.3390/su17041553 DA - 2025-02-13 AU - Semenov, Serhii C1 - Faculty of Cyber Defense, SET University, 01033 Kyiv, Ukraine C1 - Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland AU - Krupska-Klimczak, Magdalena C1 - Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland AU - Mazurek, Patryk C1 - Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland AU - Zhang, Minjian C1 - Zhejiang Nova Intelligent Technology Co., Ltd., Huzhou 318025, China AU - Chernikh, Olena C1 - Department of “Computer Engineering and Programming”, National Technical University “Kharkiv Polytechnic Institute”, 61000 Kharkiv, Ukraine C1 - Department of "Computer Engineering and Programming", National Technical University "Kharkiv Polytechnic Institute", 61000 Kharkiv, Ukraine LA - en KW - Computer security KW - Sustainable development KW - Business KW - Computer science KW - Telecommunications VL - 17 IS - 4 SP - 1553 EP - 1553 AB - The rapid integration of Unmanned Aerial Vehicles (UAVs) into smart city infrastructures necessitates advanced security measures to ensure their safe and sustainable operation. However, existing Automatic Dependent Surveillance–Broadcast (ADS-B) systems are highly vulnerable to spoofing, data falsification, and cyber threats, which compromises air traffic management and poses significant challenges to UAV security. This paper presents an innovative approach to improving UAV security by introducing a novel steganographic method for ADS-B data protection. The proposed method leverages Fourier transformation to embed UAV identifiers into ADS-B signals, ensuring a high level of concealment and robustness against signal distortions. A key feature of the approach is the dynamic parameter management system, which adapts to varying transmission conditions to minimize distortions and enhance resilience. Experimental validation demonstrates that the method achieves a tenfold reduction in Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) compared to existing techniques such as mp3stego while also improving the Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) compared to s-tools. The proposed solution ensures compliance with existing ADS-B standards, maintaining seamless integration with air traffic management systems while enhancing cybersecurity measures. By safeguarding UAV communications, the method contributes to the sustainable development of smart cities and supports critical applications such as logistics, environmental monitoring, and emergency response operations. These findings confirm the practical feasibility of the proposed approach and its potential to strengthen UAV security and ADS-B data protection, ultimately contributing to the resilience and sustainability of urban airspace infrastructure. ER - TY - JOUR TI - Route Efficiency and Carbon Emissions of Urban UAV Delivery: Evidence from Shenzhen PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6512717 UR - https://doi.org/10.2139/ssrn.6512717 DA - 2026-01-01 AU - Wang, Jing-Peng AU - Zhou, Yong AU - Tian, Qiong AU - Liu, Peng AU - Cui, Yang KW - Baseline (sea) KW - Routing (electronic design automation) KW - Carbon fibers KW - Greenhouse gas KW - Telemetry KW - Cumulative effects KW - Carbon footprint ER - TY - GEN TI - Nonlinear Efficient Path Planning for UAV in Complex Three-Dimensional Urban Environment PY - 2025 DO - 10.1109/aaac66612.2025.11427442 UR - https://doi.org/10.1109/aaac66612.2025.11427442 DA - 2025-09-26 AU - Dai, Xin C1 - Beijing Institute of Technology,School of Aerospace Engineering,Beijing,China AU - Wang, Qiao C1 - Xi'an Modern Control Technology Research Institute,Xi'an,China AU - Liu, Xuhan C1 - Xi'an Modern Control Technology Research Institute,Xi'an,China AU - Wang, Hui C1 - Beijing Institute of Technology,School of Aerospace Engineering,Beijing,China KW - Urban environment KW - Motion planning KW - Nonlinear system KW - Path (computing) KW - Urban planning SP - 338 EP - 344 ER - TY - GEN TI - Enhancing Urban Logistics Through Integrated Public Transit Vehicles and Unmanned Aerial Vehicles PY - 2024 PB - Springer Nature SN - 2190-3018 T2 - Smart innovation, systems and technologies DO - 10.1007/978-981-97-6748-9_1 UR - https://doi.org/10.1007/978-981-97-6748-9_1 DA - 2024-01-01 AU - Cui, Shaohua C1 - Department of Architecture and Civil Engineering, Chalmers University of Technology, 41296, Gothenburg, Sweden AU - Anric, Jelena C1 - Department of Architecture and Civil Engineering, Chalmers University of Technology, 41296, Gothenburg, Sweden C1 - Volvo Group Trucks Technology, Gothenburg, Sweden AU - Xue, Yongjie C1 - School of Transportation Science and Engineering, Beihang University, Beijing, 100191, PR China AU - Jia, Ruo C1 - Department of Architecture and Civil Engineering, Chalmers University of Technology, 41296, Gothenburg, Sweden LA - en KW - Transit (satellite) KW - Public transport KW - Transport engineering KW - Aeronautics KW - Engineering SP - 1 EP - 11 ER - TY - GEN TI - Cfd-Based Wind Turbulence Assessment Model for Urban Uav Path Planning PY - 2026 DO - 10.1109/icuas69441.2026.11598637 UR - https://doi.org/10.1109/icuas69441.2026.11598637 DA - 2026-06-15 AU - Aldao, Enrique C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 AU - Veiga-Piñeiro, Gonzalo C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 AU - Martínez-Alonso, Gálata C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 AU - Veiga-López, Fernando C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 AU - Martín, Elena C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 AU - González-Jorge, Higinio C1 - University of Vigo,AEROLAB / IFCAE (Research Institute of Physics, Computation and Aerospace Science),Ourense,Spain,32004 LA - en KW - Motion planning KW - Path (computing) KW - Turbulence KW - Noise (video) KW - Trajectory SP - 1224 EP - 1230 ER - TY - JOUR TI - Space Weather and Low‐Altitude Drone Economy PY - 2025 PB - American Geophysical Union SN - 1542-7390 T2 - Space Weather DO - 10.1029/2025sw004803 UR - https://doi.org/10.1029/2025sw004803 DA - 2025-12-29 AU - Xue, Dabin C1 - State Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing China AU - Zhang, Qing‐He C1 - State Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing China AU - Feng, Xueshang C1 - State Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing China AU - Wang, Chi C1 - State Key Laboratory of Solar Activity and Space Weather National Space Science Center Chinese Academy of Sciences Beijing China LA - en KW - Drone KW - Space weather KW - Situation awareness KW - Compromise KW - Space (punctuation) KW - SAFER KW - Space environment VL - 24 IS - 1 AB - Abstract Unmanned Aerial Vehicles (UAVs) are becoming integral to the emerging low‐altitude economy, operating primarily below 3,000 m for applications such as logistics, inspection, precision agriculture, and urban air mobility. The safe and reliable operation of UAVs depends critically on communication, navigation, and surveillance (CNS) systems, which provide command and control, precise positioning, and situational awareness. Although UAV hardware is largely protected from direct radiation hazards by the Earth's atmosphere and magnetosphere, space weather can still indirectly disrupt operations by affecting CNS performance. Solar flares, geomagnetic storms, and ionospheric irregularities can degrade communication links, induce navigation errors, and compromise surveillance reliability, posing risks to flight safety and operational continuity. This commentary highlights these vulnerabilities, reviews mechanisms through which space weather impacts UAV systems, and emphasizes the importance of integrating space weather awareness into UAV traffic management and system design to support a resilient low‐altitude economy. ER - TY - GEN TI - Biomimetic Optimization in UAV Logistics: Combining Physarum polycephalum with Ant Colony Algorithms for Next-Generation Distribution Systems PY - 2025 PB - Springer Nature SN - 2195-4356 T2 - Lecture notes in mechanical engineering DO - 10.1007/978-981-96-7703-0_31 UR - https://doi.org/10.1007/978-981-96-7703-0_31 DA - 2025-10-31 AU - Kamil, Nurul C1 - Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia AU - Razali, Mohd C1 - Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia AU - Jamaludin, Ahmad C1 - Faculty of Manufacturing and Mechatronic Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia C1 - Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia LA - en KW - Physarum polycephalum KW - Ant colony optimization algorithms KW - Convergence (economics) KW - Scalability KW - Obstacle KW - Optimization problem KW - Ant colony KW - Closing (real estate) SP - 309 EP - 316 ER - TY - GEN TI - Recent Development of a Conceptual Design Methodology for Unmanned Aerial Vehicles PY - 2021 T2 - AIAA AVIATION 2021 FORUM DO - 10.2514/6.2021-3219 UR - https://doi.org/10.2514/6.2021-3219 DA - 2021-07-28 AU - Babetto, Laura C1 - RWTH Aachen University AU - Stumpf, Eike C1 - RWTH Aachen University LA - en KW - Scope (computer science) KW - Conceptual design KW - Systems engineering KW - Computer science KW - Drone KW - Design methods KW - Engineering ER - TY - JOUR TI - 6G Edge-Networks and Multi-UAV Knowledge Fusion for Urban Autonomous Vehicles PY - 2024 DO - 10.36227/techrxiv.171837878.82978982/v1 UR - https://doi.org/10.36227/techrxiv.171837878.82978982/v1 DA - 2024-06-14 AU - Nawaz, Muhammad C1 - University of Glasgow, James Wastt School of Engineering AU - Zhang, Wanquan C1 - University of Glasgow, James Wastt School of Engineering AU - Zhang, Lei C1 - University of Glasgow, James Wastt School of Engineering AU - Flynn, David C1 - University of Glasgow, James Wastt School of Engineering AU - Swash, Rafiq C1 - aiDrivers Ltd, Autonomous System AU - Abbasi, Qammer C1 - University of Glasgow, James Wastt School of Engineering AU - Imran, Muhammad C1 - University of Glasgow, James Wastt School of Engineering AU - Popoola, Olaoluwa C1 - University of Glasgow, James Wastt School of Engineering LA - en KW - Computer science KW - Distributed computing KW - Efficient energy use KW - Energy consumption KW - Latency (audio) KW - Overhead (engineering) KW - Scalability KW - Architecture KW - Reinforcement learning KW - Artificial intelligence KW - Engineering AB - The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge. ER - TY - JOUR TI - Review on Logistics Air Route Planning Methods for Unmanned Aerial Vehicles in Urban Environments PY - 2025 T2 - DOAJ (DOAJ: Directory of Open Access Journals) DO - 10.16356/j.1005-2615.2025.06.001 UR - https://doi.org/10.16356/j.1005-2615.2025.06.001 DA - 2025-12-01 AU - Yifei, ZHAO C1 - School of Air Traffic Management， Civil Aviation University of China， Tianjin 300300， China AU - Zhongming, LI C1 - School of Safety Science and Engineering， Civil Aviation University of China， Tianjin 300300， China AU - Xinhui, Ren C1 - School of Economics and Management， Civil Aviation University of China， Tianjin 300300， China LA - zh KW - Route planning KW - Key (lock) KW - Component (thermodynamics) KW - Agile software development KW - Air transport KW - Multirotor KW - Air traffic control AB - With the rapid development of urban logistics unmanned aerial vehicle （UAV） transportation， multirotor UAVs， due to their vertical take-off and landing capability and agile maneuverability， have formed air route networks with wide coverage and diverse operational modes in many cities. However， urban low-altitude airspace is constrained by high-rise buildings， complex terrain， and dynamic wind fields. It also faces practical challenges such as communication interruptions and low public acceptance. These factors make scientific air route planning a key component for ensuring operational safety and efficiency. First， this study reviews the air route design schemes of major domestic operators. It extracts the basic characteristics of the spatial layout and the sequential connection of functional segments within logistics air routes， and defines a parameterized structural model for logistics air routes. Second， through a literature review， this study summarizes the key factors influencing air route planning， including ground impact risk， weather conditions， public acceptance， and infrastructure. It also examines the main planning methods for en-route air routes and departure/arrival air routes， together with their applicability， advantages， and disadvantages. Third， the study develops an integrated logistics air route planning framework. This framework includes airspace， air routes， and air route networks， covering both en-route air routes and departure/arrival air routes. The findings of this study provide methodological guidance for future theoretical research and practical operations. ER - TY - GEN TI - Design of a Collaborative Drone Logistics System for Organ Delivery in Urban Emergency Scenarios PY - 2026 PB - Springer Science+Business Media SN - 0302-9743 T2 - Lecture notes in computer science DO - 10.1007/978-3-032-30083-6_26 UR - https://doi.org/10.1007/978-3-032-30083-6_26 DA - 2026-01-01 AU - Liu, Shihan C1 - Hefei University of Technology, Hefei, 230601, China AU - Chi, Ziyuan C1 - Harbin Institute of Technology, Harbin, 150080, China LA - en KW - Drone KW - Key (lock) KW - Emergency response KW - Systems design KW - Humanitarian Logistics SP - 430 EP - 442 ER - TY - JOUR TI - Co-planning of Flight Corridors and Communication Infrastructure for Urban Drone Logistics Networks PY - 2026 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2607.23989 UR - https://doi.org/10.48550/arxiv.2607.23989 DA - 2026-07-27 AU - He, Yingjie AU - Wang, Yikang AU - Gao, Zhenyu KW - Software deployment KW - Drone KW - Base station KW - Baseline (sea) KW - Channel (broadcasting) KW - Plan (archaeology) KW - Wireless KW - Flight plan KW - Key (lock) AB - Reliable wireless connectivity is essential for urban air mobility (UAM) networks in dense urban environments. It is therefore imperative to carefully plan the supporting communication infrastructure for UAM flight corridors. Most existing works optimize communication infrastructure and UAV flight paths independently, often leading to unnecessary base station (BS) deployment or excessive flight detours. This paper studies the joint optimization of BS deployment and UAV flight corridors in complex urban environments, aiming to minimize both infrastructure investment and flight distance while satisfying communication quality constraints. We propose CR-CMAB, a channel reciprocity-guided combinatorial multi-armed bandit framework. The framework constructs high-fidelity radio maps using 3D ray tracing, selects BS combinations via coverage-aware CMAB search, and dynamically expands the search space by identifying promising BS locations through channel reciprocity. Experimental results from a detailed case study demonstrate that CR-CMAB outperforms baseline methods with moderate computational time, yielding more strategically positioned BSs and shorter flight corridors. This study offers a practical planning perspective for cost-effective and communication-reliable UAM deployment in future smart cities. ER - TY - JOUR TI - Co-planning of Flight Corridors and Communication Infrastructure for Urban Drone Logistics Networks PY - 2026 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DA - 2026-07-27 AU - He, Yingjie AU - Wang, Yikang AU - Gao, Zhenyu KW - Software deployment KW - Drone KW - Base station KW - Baseline (sea) KW - Channel (broadcasting) KW - Plan (archaeology) KW - Wireless KW - Flight plan KW - Key (lock) AB - Reliable wireless connectivity is essential for urban air mobility (UAM) networks in dense urban environments. It is therefore imperative to carefully plan the supporting communication infrastructure for UAM flight corridors. Most existing works optimize communication infrastructure and UAV flight paths independently, often leading to unnecessary base station (BS) deployment or excessive flight detours. This paper studies the joint optimization of BS deployment and UAV flight corridors in complex urban environments, aiming to minimize both infrastructure investment and flight distance while satisfying communication quality constraints. We propose CR-CMAB, a channel reciprocity-guided combinatorial multi-armed bandit framework. The framework constructs high-fidelity radio maps using 3D ray tracing, selects BS combinations via coverage-aware CMAB search, and dynamically expands the search space by identifying promising BS locations through channel reciprocity. Experimental results from a detailed case study demonstrate that CR-CMAB outperforms baseline methods with moderate computational time, yielding more strategically positioned BSs and shorter flight corridors. This study offers a practical planning perspective for cost-effective and communication-reliable UAM deployment in future smart cities. ER - TY - GEN TI - Ocularone: Exploring Drones-based Assistive Technologies for the Visually Impaired PY - 2023 DO - 10.1145/3544549.3585863 UR - https://doi.org/10.1145/3544549.3585863 DA - 2023-04-19 AU - Raj, Suman C1 - Department of Computational and Data Sciences, Indian Institute of Science, India AU - Padhi, Swapnil C1 - Department of Computational and Data Sciences, Indian Institute of Science, India AU - Simmhan, Yogesh C1 - Department of Computational and Data Sciences, Indian Instititue of Science, India LA - en KW - Drone KW - Computer science KW - Enhanced Data Rates for GSM Evolution KW - Human–computer interaction KW - Edge computing KW - Gesture KW - Analytics KW - Artificial intelligence KW - Data science SP - 1 EP - 9 AB - Fleets of Unmanned Aerial Vehicles (UAVs), also called drones, are becoming commonplace to support diverse applications in logistics and urban safety. These rely on advances in computer vision and Artificial Intelligence (AI) for autonomous navigation and control, facilitated by onboard sensors and accelerated edge computing devices attached to their base stations. This article examines how drones can be used for social good to enhance the lifestyle of Visually-Impaired People (VIP) through navigational assistance and situation awareness, enabled through visual analytics over drone video streams. We propose Ocularone, a platform for coordinating and managing a heterogeneous UAV fleet that offers drones as buddies to guide users. It uses onboard sensors and edge accelerators, and two-way communication using gestures and audio prompts, to enhance the safety and autonomy of the VIP. We validate our early prototype using Tello nano-drones and Jetson Nano edge accelerators, and present preliminary results for several safety scenarios. ER - TY - CHAP TI - Decentralized control and distributed intelligence for high-density UAV logistics networks supporting smart-city transportation and delivery services PY - 2026 DO - 10.1201/9781003731580-6 UR - https://doi.org/10.1201/9781003731580-6 DA - 2026-08-17 AU - Gorak, Tanusha AU - R, Anisha AU - Reddy, C. AU - Sadaf, Rimsha AU - Paranthaman, Jothi LA - en KW - Drone KW - Variety (cybernetics) KW - Enhanced Data Rates for GSM Evolution KW - Cloud computing KW - Decentralised system KW - Edge computing KW - Work (physics) KW - Controller (irrigation) SP - 110 EP - 133 ER - TY - JOUR TI - Strategic monitoring of improperly disposed urban waste using UAV imagery and object detection PY - 2025 PB - Elsevier BV SN - 2667-3789 T2 - Resources Conservation & Recycling Advances DO - 10.1016/j.rcradv.2025.200306 UR - https://doi.org/10.1016/j.rcradv.2025.200306 DA - 2025-12-19 AU - Izquierdo-Horna, Luis C1 - Department of Civil Engineering, Universidad Tecnológica del Perú, Lima AU - Zevallos, José C1 - Department of Civil Engineering, Universidad Tecnológica del Perú, Lima AU - Angulo, Mariana C1 - Department of Civil Engineering, Universidad Tecnológica del Perú, Lima LA - en KW - Metropolitan area KW - Object detection KW - Plastic bag KW - Impervious surface KW - Municipal solid waste KW - Sanitation KW - Aerial imagery KW - Aerial survey KW - Urban waste VL - 29 SP - 200306 EP - 200306 AB - • UAV imagery and YOLOv7 detected improperly discarded plastic bags in Lima. • The model reached 34% precision and 33% recall under constrained data conditions. • Detection accuracy varied by bag color due to contrast, light, and occlusion. • The approach proved feasible for integrating UAV monitoring into MSW operations. Urban areas in the Global South continue to face persistent municipal solid waste (MSW) management challenges driven by rapid urbanization, informal disposal practices, and limited monitoring capacity. This study assesses the operational feasibility of integrating unmanned aerial vehicle (UAV) imagery with YOLOv7 object detection to identify improperly discarded plastic bags in public spaces across Metropolitan Lima, Peru. A total of 370 high-resolution aerial images from three districts were processed to detect black, yellow, and white bags, representative of local disposal behaviors. The model achieved a recall of 33%, precision of 34%, and a mean average precision (mAP@0.5) of 21.3% at an operational point corresponding to a confidence threshold of 0.24, reflecting the challenges posed by data scarcity and high visual heterogeneity in the urban environment. Although performance was constrained by class imbalance and low inter-class contrast, detections remained adaptable to diverse urban conditions through threshold tuning. UAV-based detection provides spatially explicit data to prioritize cleanup operations, verify citizen reports, and optimize waste collection routes. This proof of concept demonstrates that UAV-assisted monitoring can complement municipal records and support incremental improvements in sanitation logistics within resource-limited urban contexts. ER - TY - JOUR TI - LCRS-Guidance: LLM-Conditioned Residual and Safety Reinforcement Learning for Urban UAV Navigation PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7088306 UR - https://doi.org/10.2139/ssrn.7088306 DA - 2026-01-01 AU - Huang, Yuzhou C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China AU - Long, Yaosong C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China AU - Cao, Y C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China AU - Cheng, Zhongtao C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China KW - Reinforcement learning KW - Residual KW - Trajectory KW - Bounded function KW - Smoothness KW - Constraint (computer-aided design) KW - Convergence (economics) KW - Markov decision process KW - Pallet ER - TY - DATA TI - Dataset for The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS) PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20491777 UR - https://doi.org/10.5281/zenodo.20491777 DA - 2026-06-01 AU - Kłodawski, Michał C1 - Warsaw University of Technology LA - en KW - Drone KW - Energy consumption KW - Wind power KW - China KW - Consumption (sociology) KW - Time horizon AB - The purpose of this dataset is to enable the replication and interpretation of the simulation research results presented in the articleMichał Kłodawski, The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS). The dataset supports the analysis of last-mile parcel delivery systems with the use of unmanned aerial vehicles (UAVs), with particular attention to demand distribution, parcel-locker locations, wind conditions, UAV speed changes, energy consumption and CO2 emissions. The dataset includes:- spatial data describing network nodes and links,- residential building locations,- parcel-locker locations,- parcel demand assigned to buildings and parcel lockers,- model parameters used in the simulation study,- wind data used to analyse the influence of wind conditions on UAV operations,- simulation results for UAV speed under no-wind and wind conditions,- aggregated comparison of energy consumption and CO2 emissions for different delivery modes. The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months). ER - TY - DATA TI - Dataset for The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS) PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20491776 UR - https://doi.org/10.5281/zenodo.20491776 DA - 2026-06-01 AU - Kłodawski, Michał C1 - Warsaw University of Technology LA - en KW - Drone KW - Energy consumption KW - Wind power KW - China KW - Consumption (sociology) KW - Time horizon AB - The purpose of this dataset is to enable the replication and interpretation of the simulation research results presented in the articleMichał Kłodawski, The use of UAVs in Last-Mile Logistics: opportunities, constraints and potential benefits of Autonomous Drone Delivery Systems (ADDS). The dataset supports the analysis of last-mile parcel delivery systems with the use of unmanned aerial vehicles (UAVs), with particular attention to demand distribution, parcel-locker locations, wind conditions, UAV speed changes, energy consumption and CO2 emissions. The dataset includes:- spatial data describing network nodes and links,- residential building locations,- parcel-locker locations,- parcel demand assigned to buildings and parcel lockers,- model parameters used in the simulation study,- wind data used to analyse the influence of wind conditions on UAV operations,- simulation results for UAV speed under no-wind and wind conditions,- aggregated comparison of energy consumption and CO2 emissions for different delivery modes. The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months). ER - TY - JOUR TI - Integrated drone delivery network design under policy-induced restrictions PY - 2026 PB - Elsevier BV SN - 2213-624X T2 - Case Studies on Transport Policy DO - 10.1016/j.cstp.2026.101795 UR - https://doi.org/10.1016/j.cstp.2026.101795 DA - 2026-03-23 AU - Wang, Shuang C1 - School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China AU - Wandelt, Sebastian C1 - School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China AU - Sun, Xiaoqian C1 - School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China AU - Stumpf, Eike C1 - Institute of Aerospace Systems, RWTH Aachen University, 52062 Aachen, Germany LA - en KW - Drone KW - Network planning and design KW - Flow network KW - Key (lock) VL - 24 SP - 101795 EP - 101795 ER - TY - JOUR TI - Emergency medical facility site selection in drone-based relief operations using an enhanced T-spherical fuzzy frank combined compromise solution method PY - 2025 PB - Elsevier BV SN - 0952-1976 T2 - Engineering Applications of Artificial Intelligence DO - 10.1016/j.engappai.2025.112140 UR - https://doi.org/10.1016/j.engappai.2025.112140 DA - 2025-09-12 AU - Mahajan, Rajdip C1 - Department of Mathematics, National Institute of Technology Agartala, Tripura, India AU - Biswas, Saptadeep C1 - Department of Mathematics, National Institute of Technology Agartala, Tripura, India C1 - Department of Operations and Supply Chain, GITAM School of Business, GITAM (Deemed to be University), Visakhapatnam, India AU - Šimić, Vladimir C1 - Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea C1 - Department of Industrial Engineering and Management, College of Engineering, Yuan Ze University, Taoyuan City, 320315, Taiwan C1 - University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia AU - Pamucar, Dragan C1 - Department of Mechanics and Mathematics, Western Caspian University, Baku, Azerbaijan C1 - Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia C1 - Faculty of Engineering, Dogus University, 34775 Umraniye, Istanbul, Turkiye AU - Baidya, Abhijit C1 - Department of Mathematics, National Institute of Technology Agartala, Tripura, India AU - Bera, Uttam C1 - Department of Mathematics, National Institute of Technology Agartala, Tripura, India LA - en KW - Weighting KW - Compromise KW - Fuzzy logic KW - Emergency management KW - Site selection KW - Sensitivity (control systems) KW - Selection (genetic algorithm) KW - Disaster area KW - Field (mathematics) VL - 161 SP - 112140 EP - 112140 ER - TY - GEN TI - A Review of Advanced High-Speed Rotorcraft PY - 2021 PB - IOP Publishing SN - 1757-8981 T2 - IOP Conference Series Materials Science and Engineering DO - 10.1088/1757-899x/1102/1/012006 UR - https://doi.org/10.1088/1757-899x/1102/1/012006 DA - 2021-03-01 AU - Yan, Xufei C1 - Zhejiang Lab, Hangzhou, Zhejiang, 310000, China AU - Lou, Bin C1 - Zhejiang Lab, Hangzhou, Zhejiang, 310000, China AU - Xie, Anhuan C1 - Zhejiang Lab, Hangzhou, Zhejiang, 310000, China AU - Chen, Lingkai C1 - Zhejiang Lab, Hangzhou, Zhejiang, 310000, China AU - Zhang, Dan C1 - Zhejiang Lab, Hangzhou, Zhejiang, 310000, China LA - en KW - Stall (fluid mechanics) KW - Aerospace engineering KW - Aeronautics KW - Rotor (electric) KW - Engineering KW - Automotive engineering KW - Aviation KW - Mechanical engineering VL - 1102 IS - 1 SP - 012006 EP - 012006 AB - Abstract Urban air transportation (private aircraft, air taxi, etc.) and all kinds of special flight missions (aerial sightseeing, logistics transportation, disaster relief, anti-terrorism and anti-riot etc.) put forward high requirements for the flight performance of future urban aircraft. It not only demands high-efficiency hovering performance, but also demands the capability of high-speed cruising as well as high safety, clean energy and other characteristics. The maximum flight speed of conventional rotorcraft (such as helicopter, multi-rotor UAV, etc.) is limited by the fluid compressibility of advancing rotor blade and the airflow separation of retreating rotor blade, which makes further improvement of the flight speed more difficult. In order to overcome the fluid compressibility of rotor advancing side and stall of rotor retreating side encountered under high-speed, researchers have proposed many new concepts of advanced rotorcraft. This article sorts and analyses the current advanced VTOL (Vertical Take-Off and Landing) rotorcrafts with new configurations, and attempts to point out the feasible research direction in the future. ER - TY - GEN TI - Integrated scheduling and path planning of heterogeneous urban UAV fleets under energy and airspace constraints PY - 2025 DO - 10.1117/12.3089447 UR - https://doi.org/10.1117/12.3089447 DA - 2025-11-07 AU - Zhan, Qian C1 - Tsinghua University (China) AU - Yin, Hao C1 - Tsinghua University (China) AU - Wang, Yichen C1 - Tsinghua University (China) AU - Xiong, Chenyi C1 - Tsinghua University (China) AU - Gao, Benhe C1 - Tsinghua University (China) KW - Scheduling (production processes) KW - Route planning KW - Baseline (sea) KW - Computation KW - Job shop scheduling KW - Task (project management) KW - Metaheuristic KW - Fleet management SP - 91 EP - 91 ER - TY - JOUR TI - Urban UAV Delivery Routing Optimization Using Multi-Agent Graph Neural Networks PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5437192 UR - https://doi.org/10.2139/ssrn.5437192 DA - 2025-01-01 AU - Li, Shuhan C1 - Universiti Malaya C1 - Universiti Malaya, Universiti Malaya (UM). 50603, Kuala Lumpur, Malay, Kuala LUmper, 50603, Malaysia AU - Zhou, Xingyu LA - en KW - Computer science KW - Vehicle routing problem KW - Routing (electronic design automation) KW - Artificial neural network KW - Graph KW - Computer network KW - Artificial intelligence KW - Distributed computing KW - Theoretical computer science ER - TY - JOUR TI - 6G-empowered low-altitude economy for energy saving and emission reduction: a case study of UAV logistics in Eastern Guizhou PY - 2026 PB - Springer Science+Business Media SN - 1868-8071 T2 - International Journal of Machine Learning and Cybernetics DO - 10.1007/s13042-026-03274-3 UR - https://doi.org/10.1007/s13042-026-03274-3 DA - 2026-08-26 AU - Dai, Ming C1 - Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic University, Shenzhen, 518055, China AU - Chen, Huajun C1 - School of Data Science, Tongren University, Tongren, 554300, China AU - Yuan, Lina C1 - School of Data Science, Tongren University, Tongren, 554300, China LA - en KW - Energy (signal processing) KW - Computational intelligence KW - Energy system KW - Energy consumption KW - National economy VL - 17 IS - 9 ER - TY - JOUR TI - Improving Health Access Through the Distribution of COVID-19 Vaccines Using Drones in Ghana PY - 2023 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.4401693 UR - https://doi.org/10.2139/ssrn.4401693 DA - 2023-01-01 AU - Bu, Daniel C1 - Zipline - Monitoring and Evaluation Team AU - Hernandez, Mallory C1 - Zipline - GIS Data Team AU - Haruna, Florence C1 - Zipline - Monitoring and Evaluation Team AU - Abasi, Priscilla C1 - Zipline - GIS Data Team AU - Kremer, Pedro C1 - Zipline - Monitoring and Evaluation Team LA - en KW - Coronavirus disease 2019 (COVID-19) KW - Drone KW - 2019-20 coronavirus outbreak KW - Virology KW - Distribution (mathematics) KW - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) KW - Pandemic KW - Computer science KW - Business KW - Medicine KW - Biology KW - Mathematics KW - Infectious disease (medical specialty) KW - Genetics KW - Outbreak ER - TY - GEN TI - Autonomous Drone Delivery Systems for Urban Logistics: A Comprehensive Review of Technologies, Challenges, and Future Directions PY - 2025 DO - 10.46254/in05.20250381 UR - https://doi.org/10.46254/in05.20250381 DA - 2025-11-06 AU - Quazi, Ruhina C1 - Anjuman college of engineering and technology AU - Singh, Rudrapratap C1 - Anjuman College of Engineering and Technology AU - Umate, Piyush C1 - Anjuman College Of Engineering And Technology AU - Dubey, Prajjwal C1 - Anjuman College Of Engineering And Technology AU - Gop, Ritik C1 - Anjuman College Of Engineering And Technology KW - Drone KW - Software deployment KW - Global Positioning System KW - Key (lock) KW - Control (management) KW - Order (exchange) AB - The accelerated growth of online shopping and the need for quick last-mile delivery have accelerated research interest in autonomous Unmanned Aerial Vehicle (UAV) delivery systems. This detailed review systematically examines the state-of-the-art in drone-based delivery systems, integrating recent breakthroughs in system architectures, route optimization algorithms, energy management strategies, and IoT-enabled operational frameworks. Based on the critical evaluation of scholarly articles from 2016 to 2025 across IEEE, ACM, and logistics research journals, this review finds three main system setups: single depot facilities, networked multiple depots, and support station-based systems. The paper evaluates key technological elements such as GPS autonomous flight navigation systems, web order management systems based on Node.js and MongoDB, Crossflight flight control systems, and Raspberry Pi 4-based companion computing. This review offers researchers and practitioners a unified understanding of existing capabilities, implementation issues, and areas of future research required for large-scale deployment of autonomous drone delivery systems within urban and rural logistics networks. ER - TY - JOUR TI - Time-Dependent Path Optimization for Vehicles and UAVs Under Urban Dynamic Traffic and Restricted Zones PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10060443 UR - https://doi.org/10.3390/drones10060443 DA - 2026-06-05 AU - Ji, Yuxuan C1 - School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China AU - Liu, L C1 - School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China AU - Wang, Yong C1 - Department of Production Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden C1 - School of Management, Wuhan University of Science and Technology, Wuhan 430080, China AU - Wang, Xi C1 - Department of Production Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden AU - Wang, L C1 - Department of Production Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden LA - en KW - Solver KW - Robustness (evolution) KW - Heuristics KW - Baseline (sea) KW - Genetic algorithm KW - Traffic congestion KW - Variable neighborhood search KW - Robust optimization KW - Metaheuristic KW - Routing (electronic design automation) VL - 10 IS - 6 SP - 443 EP - 443 AB - Current urban logistics models often struggle to reconcile diurnal traffic dynamics with rigid spatial–temporal regulations. This decoupling causes “cascading infeasibility,” where traffic delays trigger structural regulatory violations and UAV energy depletion. This study formulates a time-dependent vehicle–UAV joint routing problem that strictly couples time-varying speeds with vehicle-restricted zones and no-fly zones. The mixed-integer program minimizes a composite cost by integrating speed curves, geometric detour models, and coupled energy functions. To solve large-scale instances, we propose a hybrid metaheuristic solver (IHGA-VNS-SL) combining genetic algorithms, variable neighborhood search, simulated annealing, and self-learning. Tested on calibrated Wuhan instances, IHGA-VNS-SL quantitatively outperforms baseline heuristics (GA and ALNS). It achieves a tight 2.31% optimality gap against exact solvers (CPLEX) and up to a 20% cost reduction over ALNS, alongside near-zero tardiness. Results demonstrate that this strict coupling effectively mitigates synchronization failures, confirming the framework’s robustness for megacity distribution. ER - TY - JOUR TI - Secure and Intelligent Low-Altitude Infrastructures: Synergistic Integration of IoT Networks, AI Decision-Making and Blockchain Trust Mechanisms PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 1424-8220 T2 - Sensors DO - 10.3390/s25216751 UR - https://doi.org/10.3390/s25216751 DA - 2025-11-04 AU - Ye, Yuwen C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China AU - Min, Xirun C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China AU - Liu, Xiangwen C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China AU - Chen, Xiangyi C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK AU - Chen, Xiangyi C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK C1 - School of Computing, Newcastle University, Newcastle NE1 7RU, UK AU - Cao, Kefan C1 - School of Computing, Newcastle University, Newcastle NE1 7RU, UK C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK AU - Howlader, S. C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK AU - Chen, Xiao C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK AU - Chen, Xiao C1 - School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China C1 - School of Computing Science and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK LA - en KW - Drone KW - Blockchain KW - Payload (computing) KW - Resilience (materials science) KW - Internet of Things KW - Identifier KW - Authentication (law) KW - Ontology KW - Analytics KW - Cloud computing VL - 25 IS - 21 SP - 6751 EP - 6751 AB - The low-altitude economy (LAE), encompassing urban air mobility, drone logistics and sub 3000 m aerial surveillance, demands secure, intelligent infrastructures to manage increasingly complex, multi-stakeholder operations. This survey evaluates the integration of Internet of Things (IoT) networks, artificial intelligence (AI) decision-making and blockchain trust mechanisms as foundational enablers for next-generation LAE ecosystems. IoT sensor arrays deployed at ground stations, unmanned aerial vehicles (UAVs) and vertiports form a real-time data fabric that records variables from air traffic density to environmental parameters. These continuous data streams empower AI models ranging from predictive analytics and computer vision (CV) to multi-agent reinforcement learning (MARL) and large language model (LLM) reasoning to optimize flight paths, identify anomalies and coordinate swarm behaviors autonomously. In parallel, blockchain architectures furnish immutable audit trails for regulatory compliance, support secure device authentication via decentralized identifiers (DIDs) and automate contractual exchanges for services such as airspace leasing or payload delivery. By examining current research and practical deployments, this review demonstrates how the synergistic application of IoT, AI and blockchain can bolster operational efficiency, resilience and trustworthiness across the LAE landscape. ER - TY - JOUR TI - Cooperative Task Allocation of UAVs–UGVs for Urban Transportation via Hierarchical Spatio-Temporal Collaborative GTNN PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2332-7782 T2 - IEEE Transactions on Transportation Electrification DO - 10.1109/tte.2026.3665346 UR - https://doi.org/10.1109/tte.2026.3665346 DA - 2026-02-23 AU - Ma, Ziyuan C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Gong, Huajun C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Xiong, Jun C1 - School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China KW - Adaptability KW - Energy consumption KW - Reinforcement learning KW - Task (project management) KW - Artificial neural network KW - Efficient energy use KW - Public transport VL - 12 IS - 3 SP - 5092 EP - 5111 ER - TY - CHAP TI - Localization and grasping of small load carriers with autonomous unmanned aerial vehicles PY - 2020 DO - 10.1007/978-3-662-61755-7_22 UR - https://doi.org/10.1007/978-3-662-61755-7_22 DA - 2020-01-01 AU - Lieret, Markus C1 - Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen-Nürnberg, Germany AU - Lallinger, M. C1 - Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen-Nürnberg, Germany AU - Tauscher, Markus C1 - Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen-Nürnberg, Germany AU - Franke, Jörg C1 - Institute for Factory Automation and Production Systems, FAU Erlangen-Nuremberg, Erlangen, Germany LA - en KW - Payload (computing) KW - Drone KW - Usability KW - Scope (computer science) KW - Computer science KW - Sensor fusion KW - Real-time computing KW - Engineering KW - Simulation KW - Artificial intelligence KW - Human–computer interaction KW - Computer security SP - 241 EP - 250 AB - The application of unmanned aerial vehicles (UAV) in the area of inspection, survey or urban logistics has become a rapidly developing research domain. While the feasibility of material transports with UAVs has already been shown in the scope of different projects, the payload is thereby usually transferred manually into the UAV’s load handling device. A decisive factor for the economic usability of UAVs for aerial transportation, however, is a fully automated system including the autonomous recognition and pick-up of the cargo. We therefore present a solution for the automated detection, localization and grasping of small load carriers with UAVs. The system includes a specialized load handing device, a camera-based real-time tracking solution for small load carriers and a fusion of the global and relative position measurements to achieve the in-flight positioning accuracy required for the autonomous cargo pick-up. ER - TY - JOUR TI - An Optimized UAV Flight Path Planning Method Based on Urban Low-Altitude Navigation Knowledge Graph PY - 2025 PB - Copernicus Publications SN - 1682-1750 T2 - ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences DO - 10.5194/isprs-archives-xlviii-4-w14-2025-97-2025 UR - https://doi.org/10.5194/isprs-archives-xlviii-4-w14-2025-97-2025 DA - 2025-11-26 AU - Kong, Ying C1 - School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China AU - Guo, Xian C1 - School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China LA - en KW - Motion planning KW - Beijing KW - Flight planning KW - Path (computing) KW - Graph KW - Constraint (computer-aided design) VL - XLVIII-4/W14-2025 SP - 97 EP - 104 AB - Abstract. With the rapid growth of the low-altitude economy, path planning for Unmanned Aerial Vehicles (UAVs) in complex urban low-altitude environments has become increasingly critical. However, urban low-altitude scenarios are influenced by buildings, meteorological conditions, regulatory restrictions and numerous factors. Traditional path planning methods struggle to effectively consider the impact of multiple constraints, making it challenging to provide effective and interpretable decision support for flight operations. This study proposes an optimized UAV flight path planning method based on an Urban Low-Altitude Navigation Knowledge Graph (ULAN-KG). Utilizing the knowledge graph, it structures the association between low-altitude flight route elements and low-altitude flight constraint factors in the urban space. The experiment selects a densely built area of Beijing for validation, where the proposed method is compared with traditional algorithms. The experimental results show that the A* algorithm improved by ULAN-KG can effectively avoid flight segments affected by strong wind conditions. When conflicting with controlled airspace events, the path planning results prioritize avoiding no-fly zones. This approach offers efficient and reliable technical support for UAV applications in complex urban low-altitude scenarios, such as logistics and emergency response. ER - TY - THES TI - Clear to Land: Risk-Aware Rooftop Emergency Landing Site Selection and Safe Routing for Urban UAVs PY - 2025 T2 - Purdue DO - 10.25394/pgs.30801773 UR - https://doi.org/10.25394/pgs.30801773 DA - 2025-12-08 AU - Stawiarska, Zofia C1 - Purdue University System KW - Contingency plan KW - Emergency management KW - Work (physics) KW - Metric (unit) KW - Routing (electronic design automation) KW - Key (lock) KW - Aviation KW - Scalability AB - Technological advances supporting cargo delivery using unmanned aerial vehicles (UAVs) necessitate corresponding developments in strategic mission planning. Ensuring the safe integration of UAVs into cargo logistics is critical to meet the expectations of regulatory bodies, such as the Federal Aviation Administration (FAA), as well as societal standards. A key aspect of safety assurance is the development of contingency strategies. Regardless of system reliability, safety engineering best practices mandate emergency landing protocols for potential in-flight failures.This research work aims to support development of rooftop emergency landing protocols for UAVs in urban settings. Building on prior advances in UAV emergency planning, we developed a set of criteria to classify building rooftops as low, medium, and high-risk emergency landing sites (ELS). Our criteria address both safety and logistical considerations associated with landing on such urban rooftops in unplanned scenarios. Using our criteria, we performed ELS selection in a sample of urban areas using open-source data. We then implemented a modified A* path search algorithm which incorporates the availability of safe emergency landing sites into route selection.Our findings illustrate the interplay of risk acceptance concerns of operators, regulators, and the public on safe UAV mission performance. We translate our findings into a probability of forced unsafe landing for the UAV during flight, providing decision-makers with an actionable, evidence-based metric for flight authorization. In addition, our results demonstrate both the potential applications and limitations of open-source data for contingency planning. Our final recommendations outline further steps needed to support safe, scalable unmanned aerial vehicle missions. ER - TY - JOUR TI - SIMULATION OF UAV TRAFFIC IN URBAN AERIAL NETWORKS USING MONTE-CARLO AND DISCRETE-EVENT SIMULATION MODELS PY - 2025 SN - 2786-7854 T2 - Випробування та сертифікація DO - 10.37701/ts.07.2025.13 UR - https://doi.org/10.37701/ts.07.2025.13 DA - 2025-04-09 AU - Yena, Maksym C1 - Postgraduate student of department of Information Technologies of Design National Aerospace University M. E. Zhukovky "Kharkiv Aviation Institute" , Kharkiv , Ukraine LA - en KW - Monte Carlo method KW - Discrete event simulation KW - Computer science KW - Event (particle physics) KW - Environmental science KW - Simulation KW - Mathematics KW - Statistics KW - Physics IS - 1(7) SP - 115 EP - 122 AB - The research focuses on developing an integrated simulation model for managing unmanned aerial vehicle (UAV) traffic in urban airspace. By combining Monte Carlo and discrete-event modeling (DEM) methods, the study aims to optimize UAV route planning under high air traffic density conditions, considering spatial constraints and stochastic influences. The primary objective is to create an efficient UAV rerouting algorithm that minimizes collision risks, reduces delays, and improves the overall utilization of urban airspace. The research methodology incorporates Monte-Carlo techniques to model stochastic events such as weather changes, emergencies, and fluctuating traffic patterns, while DEM is used to analyze UAV traffic flow dynamics and optimize routing strategies based on real-time conditions. The proposed adaptive rerouting algorithm dynamically adjusts UAV trajectories based on real-time parameters, including flight speed, route configuration, and traffic density within specific zones. This ensures a quick response to evolving air traffic conditions, enabling more effective and safer UAV operations while minimizing fuel consumption and maximizing mission efficiency. Simulation results indicate that implementing the proposed model reduces the average UAV waiting time in congested zones by 20-30%, leading to more balanced airspace utilization and more efficient allocation. The adaptive redirection strategy prevents local traffic overloads, enhancing flight safety, lowers the probability of mid-air conflicts, and optimizing UAV navigation paths. The results confirm that integrating Monte-Carlo and DEM approaches is a robust method for modeling urban UAV traffic and assessing the impact of dynamic constraints. The developed framework can be employed in urban air traffic management systems, particularly in logistics, emergency response, infrastructure monitoring, and smart city applications. Future research can focus on enhancing real-time decision-making mechanisms, incorporating and improving UAV autonomy for large-scale deployment in complex urban environments. Additionally, further refinements of the model could include multi-agent reinforcement learning techniques to improve decision-making in high-density scenarios, ensuring even more efficient UAV coordination and airspace optimization. ER - TY - THES TI - Clear to Land: Risk-Aware Rooftop Emergency Landing Site Selection and Safe Routing for Urban UAVs PY - 2025 PB - Figshare (United Kingdom) T2 - Figshare DO - 10.25394/pgs.30801773.v1 UR - https://doi.org/10.25394/pgs.30801773.v1 DA - 2025-12-08 AU - Stawiarska, Zofia C1 - Purdue University System KW - Contingency plan KW - Emergency management KW - Work (physics) KW - Metric (unit) KW - Routing (electronic design automation) KW - Key (lock) KW - Aviation KW - Scalability AB - Technological advances supporting cargo delivery using unmanned aerial vehicles (UAVs) necessitate corresponding developments in strategic mission planning. Ensuring the safe integration of UAVs into cargo logistics is critical to meet the expectations of regulatory bodies, such as the Federal Aviation Administration (FAA), as well as societal standards. A key aspect of safety assurance is the development of contingency strategies. Regardless of system reliability, safety engineering best practices mandate emergency landing protocols for potential in-flight failures.This research work aims to support development of rooftop emergency landing protocols for UAVs in urban settings. Building on prior advances in UAV emergency planning, we developed a set of criteria to classify building rooftops as low, medium, and high-risk emergency landing sites (ELS). Our criteria address both safety and logistical considerations associated with landing on such urban rooftops in unplanned scenarios. Using our criteria, we performed ELS selection in a sample of urban areas using open-source data. We then implemented a modified A* path search algorithm which incorporates the availability of safe emergency landing sites into route selection.Our findings illustrate the interplay of risk acceptance concerns of operators, regulators, and the public on safe UAV mission performance. We translate our findings into a probability of forced unsafe landing for the UAV during flight, providing decision-makers with an actionable, evidence-based metric for flight authorization. In addition, our results demonstrate both the potential applications and limitations of open-source data for contingency planning. Our final recommendations outline further steps needed to support safe, scalable unmanned aerial vehicle missions. ER - TY - JOUR TI - Systematic Research and Application of a 5 G Medical Unmanned Aerial Vehicle to Deliver COVID- 19 Nucleic Acid Samples PY - 2024 PB - Mary Ann Liebert, Inc. SN - 2326-5094 T2 - Health Security DO - 10.1089/hs.2023.0090 UR - https://doi.org/10.1089/hs.2023.0090 DA - 2024-05-30 AU - Huang, Xuan C1 - Xuan Huang, MM, is Chief Technician, in the Department of Laboratory Medicine, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China AU - Ren, Feng C1 - Feng Ren, MM, is an Associate Chief Technician, in the Department of Laboratory Medicine, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China AU - Liu, Min C1 - Min Liu, MM, is an Associate Chief Technician, in the Department of Laboratory Medicine, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China AU - Jin, Pin C1 - Pin Jin, MM, is a Supervising Technician, in the Department of Laboratory Medicine, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China AU - Sun, Yifan C1 - Yifan Sun, MD, is a Supervising Technician, in the Department of Laboratory Medicine, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China LA - en KW - Coronavirus disease 2019 (COVID-19) KW - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) KW - Nucleic acid KW - 2019-20 coronavirus outbreak KW - Nucleic acid detection KW - Chemistry KW - Virology KW - Microbiology KW - Biology KW - Biochemistry KW - Medicine KW - Outbreak VL - 22 IS - 4 SP - 304 EP - 310 ER - TY - JOUR TI - A multi-objective matheuristic framework for sustainable truck-UAV last-mile delivery in smart urban built environments PY - 2026 PB - Emerald Publishing Limited SN - 2046-6099 T2 - Smart and Sustainable Built Environment DO - 10.1108/sasbe-10-2025-0599 UR - https://doi.org/10.1108/sasbe-10-2025-0599 DA - 2026-05-29 AU - Mahmoodi, Armin C1 - Toronto Metropolitan University Department of Civil Engineering, , , AU - Easa, Said C1 - Toronto Metropolitan University Department of Civil Engineering, , , LA - en KW - Sustainability KW - Scalability KW - Heuristics KW - Heuristic KW - Energy consumption KW - Benchmark (surveying) KW - Scheduling (production processes) KW - Vehicle routing problem SP - 1 EP - 48 ER - TY - THES TI - Safety-Aware Pre-Flight Trajectory Planning for Urban and Rural UAVs Under Mechanical and GPS Failure Scenarios PY - 2025 PB - King Abdullah University of Science and Technology T2 - King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) DO - 10.25781/kaust-k4108 UR - https://doi.org/10.25781/kaust-k4108 DA - 2025-01-01 AU - Almozel, Amin C1 - King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia KW - Global Positioning System KW - Context (archaeology) KW - Drone KW - Contingency plan KW - Trajectory KW - Aviation KW - Emergency management KW - Geospatial analysis AB - Unmanned Aerial Vehicles (UAVs) are rapidly emerging as a transformative technology with applications in agriculture, infrastructure inspection, environmental monitoring, and logistics. Among these, package and medical delivery stand out as especially promising, offering faster response times, reduced reliance on congested road networks, and the ability to provide critical supplies during emergencies. Yet, integrating UAVs safely into civilian and urban airspace remains a major challenge. Failures such as propulsion loss, communication outages, or GPS interference can compromise operations and endanger people and infrastructure, highlighting the need for systematic contingency planning and safety-aware trajectory design. As global aviation authorities such as the FAA, EASA, and GACA develop regulatory frameworks, the demand for rigorous and provably safe approaches to UAV traffic management is becoming increasingly urgent. This dissertation addresses these challenges by introducing a safety-aware pre-flight planning framework that systematically accounts for multiple UAV failure modes and urban operating constraints. The first stage of this research demonstrates how trajectories can be designed to always remain within gliding range of safe landing sites, validated in the context of medical drone delivery missions. Building on that foundation, the research expands to dense urban airspace and multirotor platforms, integrating considerations of GPS loss, communication blackouts, and no-fly zones. The framework is implemented in a high-fidelity simulator using realistic geospatial data from the KAUST campus, enabling evaluation under both nominal and emergency conditions. Together, these contributions establish a structured approach to contingency-aware trajectory planning, advancing the safe and scalable integration of UAVs into urban airspace. ER - TY - JOUR TI - Distance-Attention Augmented Reinforcement Learning: A Robust Approach for 3D Cooperative UAV Navigation in Dense Urban Environments PY - 2026 PB - IEEE Computer Society SN - 1536-1233 T2 - IEEE Transactions on Mobile Computing DO - 10.1109/tmc.2026.3668827 UR - https://doi.org/10.1109/tmc.2026.3668827 DA - 2026-02-27 AU - Zhang, Liang C1 - College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Lin, Hang C1 - College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Zhao, Shihong C1 - College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Wang, Fei C1 - College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Yan, Chao C1 - College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China AU - Gao, Pan C1 - College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China KW - Reinforcement learning KW - Trajectory KW - Benchmark (surveying) KW - Key (lock) KW - Generalization KW - Drone KW - Feature (linguistics) KW - Motion (physics) KW - Face (sociological concept) VL - 25 IS - 8 SP - 11520 EP - 11536 ER - TY - JOUR TI - Research on the optimization of delivery routes for vehicles with drones under no-fly zone restrictions PY - 2025 PB - Public Library of Science SN - 1932-6203 T2 - PLoS ONE DO - 10.1371/journal.pone.0335614 UR - https://doi.org/10.1371/journal.pone.0335614 DA - 2025-10-29 AU - Mao, Yingbo C1 - School of management, Harbin University of Commerce, Harbin, China AU - Jia, Guiqiong C1 - School of management, Harbin University of Commerce, Harbin, China LA - en KW - Scope (computer science) KW - Drone KW - Dual (grammatical number) KW - Service (business) KW - Heuristic KW - Customer satisfaction KW - Linear programming KW - Path (computing) VL - 20 IS - 10 SP - e0335614 EP - e0335614 AB - With the rapid development of e-commerce, logistics and distribution systems face the dual pressures of efficiency improvement and cost control. Unmanned Aerial Vehicle (UAV) delivery, featuring flexibility, high efficiency, and low carbon emissions, has become an effective means to solve the "last-mile" problem. However, the widespread no-fly zones in urban environments (e.g., airports, government agencies, and high-voltage power lines) severely limit the application scope of UAVs and increase the complexity of path planning. Against this backdrop, the vehicle-assisted UAV collaborative delivery model has emerged: through the division of labor and collaboration between ground vehicles and UAVs, it not only expands the service radius of UAVs but also overcomes the constraints of no-fly zones, achieving dual improvements in delivery efficiency and service quality.This study focuses on the optimization of vehicle-assisted UAV delivery paths under no-fly zone constraints, aiming to construct a multi-objective optimization model that balances delivery costs, carbon emissions, and customer satisfaction, and to design an efficient solution algorithm for providing scientific decision support to logistics enterprises. First, the paper systematically sorts out the classification and definition of no-fly zones as well as their impact mechanisms on UAV path planning, and elaborates on the theoretical basis of vehicle-UAV collaborative delivery, including the constituent elements of the problem, methods for quantifying customer satisfaction, and the application framework of heuristic algorithms. On this basis, a mixed-integer programming model is built with the objectives of minimizing total cost, minimizing carbon emissions, and maximizing customer satisfaction. Given that this model falls into the category of NP-hard problems, we have designed a four-stage heuristic solution. First, an improved K-means algorithm (IKM) is used to cluster customer points under the constraint of the UAV's maximum flight radius, so as to determine vehicle parking points. Second, a multi-objective genetic algorithm is applied to plan UAV delivery routes for customers in open areas. Next, the multi-objective genetic algorithm is continued to design initial routes for vehicles between parking points. Finally, the multi-objective genetic algorithm is utilized again to plan delivery routes for customers in no-fly zones, ultimately forming a complete collaborative "vehicle-UAV" delivery scheme.To verify the effectiveness of the model and algorithm, simulation experiments are conducted using two sets of cases: 30 customer points in a local area of Harbin and the large-scale R201 case from the Solomon dataset. The results show that compared with traditional vehicle-only or UAV-only delivery models, the vehicle-UAV collaborative delivery model exhibits significant advantages in total cost, carbon emissions, and customer satisfaction; the model maintains good robustness in stability tests under different no-fly zone settings; and parameter sensitivity analysis further reveals the impact of key parameters (e.g., UAV load capacity, endurance, and vehicle load capacity) on delivery performance, providing practical references for logistics enterprises in equipment selection and operation strategy formulation. ER - TY - GEN TI - Computational fluid dynamics-driven analysis of urban low-altitude wind fields on UAV flight safety with ALCFDoerX PY - 2026 PB - Institution of Engineering and Technology SN - 2732-4494 T2 - IET conference proceedings. DO - 10.1049/icp.2026.2605 UR - https://doi.org/10.1049/icp.2026.2605 DA - 2026-07-01 AU - Shao, Hang C1 - College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China AU - Cao, Junbin C1 - Aviation Maintenance NCO Academy, Air Force Engineering University of PLA, Xinyang, 464000, Henan, People's Republic of China AU - Xie, Xiaoyu C1 - College of Command and Control Engineering, Army Engineering University of PLA, Nanjing, 210007, Jiangsu, People's Republic of China AU - D, Liu C1 - School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, Hubei, People's Republic of China AU - Shao, Wei C1 - College of Biological and Pharmaceutical, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China AU - Bao, Chunpeng C1 - College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China AU - Xh, Xu C1 - College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China AU - Yang, Fan C1 - College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China AU - Lee, Mengru C1 - College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China LA - en KW - Trajectory KW - Computational fluid dynamics KW - Reliability (semiconductor) KW - Field (mathematics) KW - Wind power KW - Drone KW - Wind speed KW - Flight safety VL - 2025 IS - 61 SP - 597 EP - 604 ER - TY - JOUR TI - Efficient Strategies for Unmanned Aerial Vehicle Flights: Analyzing Battery Life and Operational Performance in Delivery Services Using Stochastic Models PY - 2024 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2024.3449283 UR - https://doi.org/10.1109/access.2024.3449283 DA - 2024-01-01 AU - Silva, Francisco C1 - PASID Laboratory, Federal University of Piauí (UFPI), Teresina, Picos, Piauí, Brazil AU - Barbosa, Vandirleya C1 - PASID Laboratory, Federal University of Piauí (UFPI), Teresina, Picos, Piauí, Brazil AU - Lima, Luiz C1 - PASID Laboratory, Federal University of Piauí (UFPI), Teresina, Picos, Piauí, Brazil AU - Sabino, Arthur C1 - PASID Laboratory, Federal University of Piauí (UFPI), Teresina, Picos, Piauí, Brazil AU - Rêgo, Paulo C1 - Group of Computer Networks, Software Engineering and Systems, Federal University of Ceará, Fortaleza, Brazil AU - Bittencourt, Luiz C1 - Institute of Computing, State University of Campinas (UNICAMP), Campinas, Brazil AU - Lee, Jae-Woo C1 - Department of Aerospace Information Engineering, Konkuk University, Seoul, South Korea AU - Min, Dugki C1 - Department of Computer Science and Engineering, College of Engineering, Konkuk University, Seoul, South Korea AU - Nguyễn, Tuấn C1 - Konkuk Aerospace Design-Airworthiness Research Institute (KADA), Konkuk University, Seoul, South Korea LA - en KW - Computer science KW - Battery (electricity) KW - Automotive engineering KW - Operations research KW - Aeronautics KW - Engineering VL - 12 SP - 144544 EP - 144564 AB - In the rapidly evolving field of autonomous unmanned aerial vehicles (UAVs), commonly referred to as drones, there is burgeoning interest from sectors such as logistics, online retail, and government agencies. The impetus for this interest is largely due to technological progress and innovation. This paper presents a model based on stochastic Petri nets (SPN) for evaluating the operational efficiency of drone delivery systems, with an emphasis on the completion times for tasks executed by either individual or multiple collaborating drones. Recognizing the limitation of drones’ operational duration, chiefly hindered by battery life, the paper suggests the creation of strategic charging stations and the adoption of cooperative delivery tactics as potential solutions, albeit with inherent costs. Two SPN models were developed within this study: one depicting solo drone missions and another depicting cooperative missions with package transfers between drones. These models facilitate the examination of several performance indicators, such as Utilization Level, Mean Mission Time, Delivery Rate, and the probability of delivering a set number of packages within a designated period. By utilizing Design of Experiments (DoE) methods for sensitivity analysis, the study pinpoints essential factors that impact the performance of deliveries and offers strategies for the enhancement of drone delivery networks. The paper concludes by asserting that strategic placement of charging stations coupled with collaborative delivery efforts can effectively mitigate the limitations posed by drones’ battery life, thereby augmenting the overall efficacy and feasibility of drone-based delivery services. The SPN models provide a robust predictive framework for drone delivery performance, aiding in improved planning and allocation of resources. The findings underscore the advantages of cooperative drone operations in urban logistics and call for additional research on the optimization of charging station placement and the refinement of collaborative drone strategies. ER - TY - JOUR TI - Energy Minimization for Fixed-Wing AAV Inspection System in Multi-Hangar and Windy Environment PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 0018-9545 T2 - IEEE Transactions on Vehicular Technology DO - 10.1109/tvt.2025.3574075 UR - https://doi.org/10.1109/tvt.2025.3574075 DA - 2025-05-30 AU - Wu, Fahui C1 - School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China AU - Luo, Yuanhua C1 - School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China AU - Xu, Yu C1 - School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China AU - Yang, Dingcheng C1 - School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China AU - Xiao, Lin C1 - School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, China LA - en KW - Minification KW - Fixed wing KW - Energy minimization KW - Engineering KW - Energy (signal processing) KW - Wing KW - Computer science KW - Aerospace engineering KW - Mathematics KW - Physics VL - 74 IS - 11 SP - 17840 EP - 17853 ER - TY - GEN TI - Unmanned aerial vehicles (UAVs) - drones in the service of urban and remote area distribution. Current status and prospects for their use PY - 2026 PB - Harokopio University of Athens T2 - Harokopio University DO - 10.26258/heal.hua.6876 UR - https://doi.org/10.26258/heal.hua.6876 DA - 2026-03-18 AU - Μαρία, Κωνσταντίνος AU - Maria, Konstantinos LA - el KW - Drone KW - Service (business) KW - Field (mathematics) KW - Empirical research KW - Aerial survey KW - Goods and services AB - This thesis systematically examines the use of Unmanned Aerial Vehicles (UAVs), widely known as drones, in the field of goods delivery in both urban and remote areas. It analyzes the current landscape, technological advancements, regulatory framework, and future prospects for their application. First, the theoretical background and historical development of UAVs are presented, from their early military uses to their modern commercial, social, and environmental applications. Special emphasis is placed on international and Greek practices in logistics, precision agriculture, healthcare, infrastructure inspection, and environmental monitoring, as well as on the factors influencing societal acceptance and the diffusion of the technology. Subsequently, the regulatory, legal, and institutional framework governing drone operations at the European and national level is analyzed, along with the technical, economic, and organizational challenges associated with their widespread adoption. At the same time, the activities of major international and Greek manufacturers are examined, highlighting the role of innovation, autonomy, and safety in shaping the contemporary UAV market. The empirical part of the thesis presents a quantitative study conducted over a three‑month period using an online questionnaire via Google Forms. A total of 157 individuals participated. The aim of the study was to investigate citizens’ attitudes, perceptions, and degree of acceptance toward the use of drones for goods delivery, with particular emphasis on issues of flight safety, personal data protection, and perceived usefulness. The findings indicate that although the majority of participants express a positive stance toward the use of UAVs in logistics, significant concerns remain regarding safety and privacy. At the same time, the potential of drones to improve service speed, operational efficiency, and accessibility—especially in remote or hard‑to‑reach areas—is widely recognized. In conclusion, the thesis highlights that UAVs can serve as a strategic innovation tool in the field of supply chain and transportation, provided that appropriate technological infrastructures are developed, clear and stable regulatory frameworks are established, and societal trust is strengthened. The integration of drones in the Greek context has the potential to substantially contribute to sustainable development, improved quality of life, and the overall strengthening of social and economic cohesion. ER - TY - JOUR TI - Thermal-Aware Particle Swarm Optimization for UAV Medical Delivery Route Planning in Hot-Climate Urban Environments PY - 2026 SN - 2305-6932 T2 - Wasit Journal of Engineering Sciences DO - 10.31185/wjes.vol14.iss3.994 UR - https://doi.org/10.31185/wjes.vol14.iss3.994 DA - 2026-09-01 AU - Al-Khashan, Mohammed C1 - University of Wasit AU - Alaidi, Abdul C1 - University of Wasit KW - Particle swarm optimization KW - Local search (optimization) KW - Metaheuristic KW - Ant colony optimization algorithms KW - Genetic algorithm KW - Margin (machine learning) KW - Initialization KW - Ant colony KW - Local optimum VL - 14 IS - 3 SP - 138 EP - 157 AB - Unmanned aerial vehicles (UAVs) can bypass the road congestion that delays urban medical logistics, but their use for temperature-sensitive cargo in hot-climate cities remains unproven: existing route planners optimize time, distance, or energy while ignoring the payload’s thermal state, and no published UAV energy model is calibrated above 40 °C. This paper formulates UAV medical routing in extreme heat as a four-objective minimization problem delivery time, thermally corrected energy, a Payload Integrity Index (PII), and JARUS SORA v2.5 ground risk over a verified 21-node Baghdad hospital network at 48 °C ambient, and proposes a Thermal-Aware Particle Swarm Optimization (T-PSO) algorithm to solve it. T-PSO is a memetic extension of discrete PSO combining greedy initialization with 2-opt local search, paired with a Thermal-Aware Intelligent Decision Engine (TA-IDE) that issues an ACCEPT, REPLAN, or REJECT decision under a hard payload-admissibility constraint. T-PSO is benchmarked against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) under an equal budget of 6,000 fitness evaluations across five scenarios, three weight profiles, and five mission types, giving 13,500 deterministically reproducible runs. T-PSO achieved the lowest mean composite fitness (0.1023), significantly outperforming GA (0.1052), ACO (0.1058), GWO (0.1111), WOA (0.1118), and PSO (0.1225) (Friedman χ²(5) = 143.66, p = 2.99 × 10⁻²⁹), with its largest margin 15.7–57.0% on the 20-node full-network scenario. A component ablation attributes essentially all of this advantage to the 2-opt local search rather than to greedy seeding, and shows that a spatially uniform thermal correction cannot influence route construction at all; thermal awareness therefore enters the method through route evaluation energy accounting, payload admissibility, and the TA-IDE rather than through route construction. Sensitivity analyses over the thermal coefficient (5.7–17.1% energy overhead), container insulation class (τ = 31–1,527 min), ambient temperature (35–52 °C), instance size (5–20 nodes), and objective weights (T-PSO gain 9.5–19.9%) establish the boundaries within which these conclusions hold. The framework gives healthcare logistics operators an auditable, safety-aware routing basis for hot-climate UAV medical delivery. All results are simulation-based; physical flight validation is identified as the necessary next step and a protocol for it is specified. ER - TY - GEN TI - An Airport Airspace Flow Control Method for Drones PY - 2019 T2 - 2019 IEEE International Conference on Unmanned Systems (ICUS) DO - 10.1109/icus48101.2019.8996012 UR - https://doi.org/10.1109/icus48101.2019.8996012 DA - 2019-10-01 AU - Guoqi, Zeng C1 - Unmanned Systems Research Institute Beijing, China Hangzhou Innovation Institute of Beihang University, Hangzhou, China AU - Cui, Kai C1 - School of Electronic and Information Engineering, Beijing, China AU - Quan, Quan C1 - School of Automation Science and Electrical Engineering, Beijing, China AU - Lin, Wei C1 - School of Electronic and Information Engineering, Beijing, China AU - Lei, Yuehan C1 - School of Electronic and Information Engineering, Beijing, China LA - en KW - Drone KW - Aeronautics KW - Computer science KW - Control (management) KW - Air traffic control KW - Transport engineering KW - Engineering KW - Aerospace engineering KW - Artificial intelligence SP - 178 EP - 182 ER - TY - CHAP TI - UAV and drone communication PY - 2026 DO - 10.1201/9781003637721-10 UR - https://doi.org/10.1201/9781003637721-10 DA - 2026-06-26 AU - Nayak, Suvra AU - Nanda, Gyana AU - Senapati, Dilip AU - Dash, Sasmita LA - en KW - Drone KW - SAFER KW - Key (lock) KW - Service (business) KW - Routing (electronic design automation) KW - Population KW - Queueing theory SP - 180 EP - 196 ER - TY - JOUR TI - PG-DWA: A Pheromone-Guided Global Planning and Local Obstacle Avoidance Method for UAVs in Complex Urban 3D Environments PY - 2026 T2 - Research Square DO - 10.21203/rs.3.rs-8577279/v1 UR - https://doi.org/10.21203/rs.3.rs-8577279/v1 DA - 2026-01-23 AU - Leng, Can C1 - Hunan First Normal University AU - Zhao, Yu C1 - Hunan First Normal University AU - Mo, Mengxia C1 - Hunan University AU - Ma, Junjie C1 - Hunan First Normal University AU - Peng, Zixuan C1 - Hunan First Normal University AU - Zhu, Xiaoxiong C1 - Central South University KW - Obstacle avoidance KW - Obstacle KW - Motion planning KW - Trajectory KW - Path (computing) KW - Key (lock) KW - Smoothing KW - Vehicle dynamics ER - TY - JOUR TI - Advanced Drone Routing and Scheduling for Emergency Medical Supply Chains in Essex PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9090664 UR - https://doi.org/10.3390/drones9090664 DA - 2025-09-22 AU - Esfahlani, Shabnam C1 - Faculty of Science and Engineering, School of Engineering and the Built Environment, Anglia Ruskin University, Bishop Hall Lane, Chelmsford CM1 1SQ, UK AU - Simandjuntak, Sarinova C1 - Faculty of Science and Engineering, School of Engineering and the Built Environment, Anglia Ruskin University, Bishop Hall Lane, Chelmsford CM1 1SQ, UK AU - Sanaei, Alireza C1 - Faculty of Science and Engineering, School of Engineering and the Built Environment, Anglia Ruskin University, Bishop Hall Lane, Chelmsford CM1 1SQ, UK AU - Fraess-Ehrfeld, Alex C1 - Airborne Robotics Worting House, Worting Road, Basingstoke RG23 8PY, UK C1 - Airborne Robotics Worting House, Worting Road, Basingstoke, RG23 8PY, UK LA - en KW - Scheduling (production processes) KW - Simulated annealing KW - Drone KW - Vehicle routing problem KW - Particle swarm optimization KW - Swarm behaviour KW - Embedding VL - 9 IS - 9 SP - 664 EP - 664 AB - Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at v=60kmh−1. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. ER - TY - GEN TI - Remote sensing–based urban floodplain mapping: the added value of UAV-LiDAR compared to global and GNSS-derived DEMs PY - 2026 DO - 10.5194/egusphere-egu26-15447 UR - https://doi.org/10.5194/egusphere-egu26-15447 DA - 2026-03-14 AU - Santana, Eduardo C1 - Federal University of Pelotas, Center for Technological Development, Postgraduate Program in Water Resources, Pelotas, Brazil (eduardoluceirosantana@hotmail.com) AU - Bueno, Laura C1 - Federal University of Pelotas, Center for Technological Development, Postgraduate Program in Water Resources, Pelotas, Brazil (Lmartinsbueno9@gmail.com) AU - da Paz, Gabriel C1 - Federal University of Pelotas, Center of Engineering, Civil Engineering, Pelotas, Brazil (gabrielsdapaz@gmail.com) AU - De Oliveira Alves, Rafael C1 - Federal University of Pelotas, Center of Engineering, Civil Engineering, Pelotas, Brazil (rafaeldeoliveiraalves2001@gmail.com) AU - Caldeira, Tamara C1 - Federal University of Pelotas, Center of Engineering, Pelotas, Brazil (tamaraleitzkecaldeira@gmail.com) AU - Beskow, Samuel C1 - Federal University of Pelotas, Center for Technological Development, Water Engineering, Pelotas, Brazil (samuelbeskow@gmail.com) AU - Rodrigues, Aryane C1 - Federal University of Pelotas, Center for Technological Development, Postgraduate Program in Water Resources, Pelotas, Brazil (aryane_03.2@hotmail.com) AU - Borges, Julio C1 - Federal University of Pelotas, Center for Technological Development, Postgraduate Program in Water Resources, Pelotas, Brazil (engjulioborges@gmail.com) AU - Teixeira, Denis C1 - Federal University of Pelotas, Center of Engineering, Pelotas, Brazil (denislealteixeira@gmail.com) AU - Weber, Gustavo C1 - Federal University of Pelotas, Center of Engineering, Agricultural Engineering, Pelotas, Brazil (gustavoweber113@gmail.com) AU - Leandro, Diuliana C1 - Federal University of Pelotas, Center of Engineering, Sanitary and Environmental Engineering, Pelotas, Brazil (diuliana.leandro@gmail.com) KW - Floodplain KW - Digital elevation model KW - Watershed KW - Flood myth KW - Elevation (ballistics) KW - Flooding (psychology) KW - Hydrology (agriculture) KW - Watershed area AB - Flood risk management in urban floodplains strongly depends on the spatial resolution of digital elevation models (DEMs), which control floodplain connectivity, flow pathways, and surface storage. In many developing countries, flood-related studies rely predominantly on publicly available global DEM products, whose spatial resolution and vertical accuracy are often insufficient to represent subtle topographic gradients, densely vegetated floodplains, and complex urban microtopography. These limitations are particularly critical in low-relief environments, where small elevation differences exert a disproportionate control on inundation extent and flood dynamics. This issue has become increasingly evident in subtropical lowland regions of southern Brazil, where extreme flood events in 2023–2024 exposed shortcomings of commonly used global DEMs for urban floodplain applications. Therefore, the Piratini River watershed has been the focus of ongoing efforts to develop a real-time hydrological forecasting system to support decision-making during flood emergencies under data-scarce conditions. The urban areas of Pedro Osório and Cerrito along the main floodplain of the Piratini River constitute the core operational domain of this system and are recurrently affected by flooding. The watershed drains approximately 4,700 km² upstream of the municipalities and is characterized by low relief and wide floodplains. This study investigates the applicability of publicly available global DEMs and locally derived high-resolution elevation datasets for floodplain mapping and hydrological–hydrodynamic applications in these urban areas. A comparative assessment was conducted using two global DEM products - ALOS PALSAR (12.5 m) and ANADEM (30 m) - and three locally derived DEMs generated from high-resolution surveys. Local datasets include two Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK)–based surveys (static and kinematic) acquired with an Emlid Reach RS2+ receiver using real-time corrections via NTRIP (Networked Transport of RTCM via Internet Protocol), and an unmanned aerial vehicle (UAV)–based Light Detection and Ranging (LiDAR) survey acquired with a DJI Matrice 350 RTK platform equipped with a Zenmuse L2 sensor. The static GNSS survey comprised 2,921 points, while the kinematic survey yielded approximately 34,000 at a 1-s sampling interval. The UAV–LiDAR survey covered 21.5 km² of the urban floodplain. Raw elevation data from local surveys were converted from ellipsoidal to orthometric altitude using the hgeoHNOR2020 geoid model. GNSS-derived altitudes were interpolated using ordinary kriging in ArcGIS Pro. LiDAR data were processed in DJI Terra, resulting in a high-density point cloud (> 98 points m⁻²) and a terrain model with decimetric spatial resolution. Results reveal clear differences among datasets. Global DEMs show limited capability to represent floodplain connectivity and microtopography, particularly in vegetated areas. GNSS RTK–based DEMs provide intermediate performance but are constrained by survey logistics and GNSS signal degradation. In contrast, the UAV-based LiDAR DEM provides the most detailed and hydrologically meaningful representation of floodplain morphology, including vegetated and off-street areas, enabling improved delineation of flow paths and floodplain storage. These findings highlight the critical role of high-resolution elevation data for floodplain mapping and hydrological–hydrodynamic analyses in low-relief urban environments, reinforcing UAV-based LiDAR as a key remote sensing tool for risk assessment and climate adaptation in data-scarce regions. ER - TY - JOUR TI - Modeling and Solving Multi-Objective Path Planning Problem for Cooperative Cable-Suspended Load Transportation Considering the Time Variable Risk PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2025.3529618 UR - https://doi.org/10.1109/access.2025.3529618 DA - 2025-01-01 AU - Haghrah, Amir C1 - Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran AU - Ghaemi, Sehraneh C1 - Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran AU - Badamchizadeh, Mohammad C1 - Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran LA - en KW - Computer science KW - Variable (mathematics) KW - Mathematical optimization KW - Motion planning KW - Path (computing) KW - Robot KW - Computer network KW - Mathematics KW - Artificial intelligence VL - 13 SP - 11704 EP - 11719 AB - Cooperative load transportation has the potential to revolutionize the urban logistics industry and can also be utilized in rescue operations following natural disasters. Utilizing cooperative cable-suspended load transportation in such applications involves addressing three problems: static path planning, dynamic path planning, and control. In this article, we aim to tackle the first problem, i.e., static path planning, which is largely separate from the other two tasks. In this regard, we propose a model for the offline path planning of the cooperative cable-suspended load transportation system, including practical constraints. Using the proposed model, we can limit parameters such as the speed and acceleration of UAVs, flight altitude, task completion time, and safety distances between UAVs and obstacles, while minimizing the path length and risk. In addition, we propose an improved hybrid fuzzy particle swarm optimization (PSO) and whale optimization algorithm (WOA) to solve the introduced path planning problem efficiently. Furthermore, a chaotic mutation operator prevents premature convergence in the PSO algorithm. To demonstrate the efficiency of the proposed model and algorithm, three case studies inspired by a fire incident in a forest park have been conducted, and the results have been compared with those of seven other well-known algorithms. ER - TY - JOUR TI - Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset PY - 2023 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2310.09165 UR - https://doi.org/10.48550/arxiv.2310.09165 DA - 2023-10-13 AU - Catalano, Iacopo AU - Yu, Xianjia AU - Queralta, Jorge LA - en KW - Lidar KW - Computer science KW - Drone KW - GNSS applications KW - Adaptability KW - Real-time computing KW - Benchmarking KW - Sensor fusion KW - Remote sensing KW - Global Positioning System KW - Artificial intelligence KW - Computer vision KW - Geography AB - With the increasing prevalence of drones in various industries, the navigation and tracking of unmanned aerial vehicles (UAVs) in challenging environments, particularly GNSS-denied areas, have become crucial concerns. To address this need, we present a novel multi-LiDAR dataset specifically designed for UAV tracking. Our dataset includes data from a spinning LiDAR, two solid-state LiDARs with different Field of View (FoV) and scan patterns, and an RGB-D camera. This diverse sensor suite allows for research on new challenges in the field, including limited FoV adaptability and multi-modality data processing. The dataset facilitates the evaluation of existing algorithms and the development of new ones, paving the way for advances in UAV tracking techniques. Notably, we provide data in both indoor and outdoor environments. We also consider variable UAV sizes, from micro-aerial vehicles to more standard commercial UAV platforms. The outdoor trajectories are selected with close proximity to buildings, targeting research in UAV detection in urban areas, e.g., within counter-UAV systems or docking for UAV logistics. In addition to the dataset, we provide a baseline comparison with recent LiDAR-based UAV tracking algorithms, benchmarking the performance with different sensors, UAVs, and algorithms. Importantly, our dataset shows that current methods have shortcomings and are unable to track UAVs consistently across different scenarios. ER - TY - JOUR TI - Tall Urban Tree Canopies May Amplify Low-Altitude UAV Noise: Evidence from Field Measurements and Psychoacoustic Assessment PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 1999-4907 T2 - Forests DO - 10.3390/f17080907 UR - https://doi.org/10.3390/f17080907 DA - 2026-08-01 AU - Lyu, Xurui C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Yao, Zhili C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Lin, Zhiying C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Luo, Tao C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Zhang, Binghua C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Chen, Hui C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China AU - Hong, Xin-Chen C1 - Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou University, Fuzhou 350108, China C1 - School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China LA - en KW - Psychoacoustics KW - Kriging KW - Soundscape KW - Noise (video) KW - Ambisonics KW - Ambient noise level KW - Tree (set theory) KW - Canopy KW - Field (mathematics) VL - 17 IS - 8 SP - 907 EP - 907 AB - The rapid growth of UAV use in urban logistics, infrastructure inspection, emergency response, and campus services has made low-altitude flights more common in residential environments. Previous studies have mainly examined aircraft acoustics, flight conditions, building transmission, or listener responses. Repeated field evidence on UAV noise under contrasting canopy-site configurations remains limited. This study compared UAV noise under low-canopy (<8 m) and tall-canopy (>15 m) conditions at a six-storey campus residential area with a regular row-type layout. The two focal sites had similar building layouts, façade conditions, measurement positions, flight altitudes, and source–receiver geometry. They differed mainly in tree height and canopy density. We tested a DJI Mavic 2 Pro and a DJI Matrice 200 V2 in three independent hovering trials for each condition. We measured sound levels using Class 1 sound level meters. We also compared indoor and outdoor exposure. The analysis included one-third-octave-band spectra from representative recordings, descriptive psychoacoustic indicators, 118 valid questionnaires, principal component analysis, and exploratory empirical Bayesian kriging based on 15 measurement points. At the outdoor focal sites, the Matrice 200 showed a LASmax 10.0 dB higher under the tall-canopy condition. Its LAeq was 2.7 dB higher, whereas its L90 was 10.5 dB lower. This pattern indicates a wider, more intermittent sound-level distribution rather than a uniform increase. The Mavic 2 outdoor LAeq was 5.0 dB higher under the tall-canopy condition. Indoor differences varied by UAV model, window condition, and acoustic indicator. Higher transient sound levels and stronger psychoacoustic responses were associated with greater annoyance. EBK interpolation was used to visualize spatial heterogeneity. The assessment framework may also support studies in campus dormitory areas and row-layout multi-storey residential compounds with similar spatial features. The findings suggest that canopy-site configuration, building enclosure, UAV characteristics, and flight geometry should be assessed together. ER - TY - GEN TI - Quantum Annealing Solutions for Drone Route Planning Problems PY - 2024 DO - 10.1109/qce60285.2024.00076 UR - https://doi.org/10.1109/qce60285.2024.00076 DA - 2024-09-15 AU - Hua, Richard C1 - CNRS@CREATE LTD,Singapore AU - Di Lorenzo, Daniele C1 - ENSAM Paris, France & ESI Group,PIMM lab,Bagneux,France AU - Chinesta, Francisco C1 - ENSAM Paris, France & ESI Group,PIMM lab,Bagneux,France AU - Codognet, Philippe C1 - Sorbonne University France & University of Tokyo,JFLI, CNRS,Tokyo,Japan LA - en KW - Drone KW - Computer science KW - Annealing (glass) KW - Simulated annealing KW - Quantum annealing KW - Quantum KW - Quantum computer KW - Materials science KW - Physics KW - Quantum mechanics KW - Algorithm KW - Biology SP - 600 EP - 610 AB - We present annealing solutions for route optimization problems for unmanned aerial vehicles (UAVs), The route planning problem is a generalised version of the Travelling Salesman Problem which has many applications in logistics planning. Our annealing solutions combine classical algorithms with quantum/digital annealers specifically designed for quadratic optimization problems and shows improvements over generic traditional solvers such as Gurobi. We empirically test the solvability of our annealing solutions on both hand-crafted hard test cases as well as simulated ‘real-world’ examples of urban environments where the operations of the UAVs can be affected by the direction and intensity of wind, which is conditioned by the presence of buildings. ER - TY - JOUR TI - Assessing sustainability of smart last mile delivery: a simulation-based decision support tool PY - 2025 PB - Elsevier BV SN - 2666-1888 T2 - Sustainable Futures DO - 10.1016/j.sftr.2025.100713 UR - https://doi.org/10.1016/j.sftr.2025.100713 DA - 2025-05-21 AU - Gnoni, Maria C1 - Department of Innovation Engineering, University of Salento, Via per Monteroni, 73100 Lecce AU - Rubrichi, Lorenzo C1 - Department of Architecture and Industrial Design, University of Campania "Luigi Vanvitelli", Via San Lorenzo, 81031 Aversa CE AU - Tornese, Fabiana C1 - Department of Innovation Engineering, University of Salento, Via per Monteroni, 73100 Lecce LA - en KW - Mile KW - Last mile (transportation) KW - Sustainability KW - Decision support system KW - Computer science KW - Environmental resource management KW - Environmental science KW - Environmental planning KW - Geography KW - Data mining VL - 9 SP - 100713 EP - 100713 AB - The increasing demand of e-commerce is forcing economic and environmental inefficiency in last mile logistics (LML). The adoption of smart and autonomous technologies, such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs), is being evaluated in LML in order to increase its effectiveness. UAVs offer advantages such as faster delivery times and reduced traffic congestion, but face challenges like weather sensitivity and the need for dedicated take-off and landing infrastructure. ADRs can reduce emissions and operational costs compared to traditional LML systems, but their full application is limited mainly due to slower speeds and complex interactions with pedestrians. Despite their limitations, in future years these technologies could be fully applied for LML: thus, evaluating their environmental impact during LML service is necessary to plan their full-scale application. This study proposes a simulation-based decision support tool for assessing the performance of traditional and smart LML technologies according to economic and environmental points of view. By leveraging advanced simulation models, the proposed tool allows to estimate these impacts under varying operational conditions, providing a comprehensive framework for decision-making the LML field by comparing traditional versus innovative LML services. The tool was validated through a case study application in an urban context, demonstrating its ability to highlight the potential benefits and challenges of applying UAVs and ADRs into LML networks. Results indicate that unmanned delivery vehicles allow for a substantial reduction in carbon emissions in the operational phase, confirming their potential as a more environmentally sustainable solution for urban last mile logistics. In addition, the total cost associated with unmanned systems is found to be comparable to that of conventional vehicles, particularly when these latter operate under medium-to-high traffic conditions. Researchers and logistic companies can use this tool to evaluate and optimize the impact of their innovative LML services strategies and achieve improved economic and environmental sustainability levels. ER - TY - JOUR TI - The Concept of Big Data Management with Various Transportation Systems Sources as a Key Role in Smart Cities Development PY - 2022 PB - Multidisciplinary Digital Publishing Institute SN - 1996-1073 T2 - Energies DO - 10.3390/en15249506 UR - https://doi.org/10.3390/en15249506 DA - 2022-12-15 AU - Dudek, Tomasz C1 - Faculty of Engineering and Economics of Transport, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland AU - Kujawski, Artur C1 - Faculty of Engineering and Economics of Transport, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland LA - en KW - Computer science KW - Big data KW - Data science KW - Key (lock) KW - Identification (biology) KW - Analytics KW - Smart city KW - Data collection KW - Data analysis KW - Thresholding KW - Field (mathematics) KW - Data mining KW - Artificial intelligence KW - Computer security VL - 15 IS - 24 SP - 9506 EP - 9506 AB - An increasing number of devices and their communication with each other generates huge amounts of data. The efficiency of processing such large and heterogeneous data is crucial for extracting the reliable and consistent information that is needed for the effective management of smart cities within the field of transport. Data heterogeneity and volume as well as its integration and analytics are big challenges for decision-makers. The development of urban agglomerations is largely dependent on the proper management of such data. Therefore, this paper explores the role of these data repositories, their acquisition from different sources, and the ways to combine them. The main goal of this paper is to propose a concept of Smart City management based on Big Data Analytics and technology related to UAVs (Unmanned Aerial Vehicle) which may reduce costs and resource consumption. The presented concept includes successive data generation and collection, data type identification, problem and requirement identification, filtering, classification, pre-processing, and data optimization, as well as decision support analysis. A key part of this analysis utilizes computer algorithms, such as Speeded Up Robust Features (SURF) and Thresholding and Blob detection, to develop a multi-camera image recognition system for freight transport management and logistics in smart cities. The objective is to design a system that optimizes the route planning and time of vehicle passage on selected road sections, ultimately leading to the reduction of emissions. During the study, data obtained from multiple sources were compared, and the analysis uncovered different results for the same assumptions. We discuss the reasons for these variances. Overall, the results obtained in the analysis indicated that it is necessary to correct the predictions of the multi-camera image recognition system with additional methods and algorithms. ER - TY - JOUR TI - An Integrated Cloud‐Based Failure Mode and Effects Analysis Framework for Risk Assessment of Unmanned Aerial Vehicle Operations in Urban Logistics Using Group Consensus PY - 2026 PB - Wiley SN - 0272-4332 T2 - Risk Analysis DO - 10.1111/risa.70246 UR - https://doi.org/10.1111/risa.70246 DA - 2026-04-20 AU - Gao, Fei C1 - School of Aeronautics Shandong Jiaotong University Jinan Shandong China LA - en KW - Weighting KW - Ranking (information retrieval) KW - Failure mode and effects analysis KW - Cloud computing KW - Risk assessment KW - Process (computing) KW - Relation (database) KW - Mode (computer interface) KW - Analytic hierarchy process VL - 46 IS - 5 SP - e70246 EP - e70246 ER - TY - GEN TI - D2DC: Mid - Air Drone- to- Drone Charging to Enhance Flight Endurance PY - 2024 DO - 10.1109/icuas60882.2024.10556931 UR - https://doi.org/10.1109/icuas60882.2024.10556931 DA - 2024-06-04 AU - Jaiswal, Archit C1 - University of Florida,Department of Electrical & Computer Engineering,Gainesville,FL,USA AU - Bhunia, Swarup C1 - University of Florida,Department of Electrical & Computer Engineering,Gainesville,FL,USA LA - en KW - Drone KW - Aeronautics KW - Aerospace engineering KW - Computer science KW - Engineering KW - Biology SP - 893 EP - 898 ER - TY - GEN TI - Simulated Annealing Driven Particle Swarm Optimization for 3D UAV Path Planning in Urban Environments from the Perspective of Industrial Design PY - 2026 DO - 10.1145/3807246.3807371 UR - https://doi.org/10.1145/3807246.3807371 DA - 2026-02-06 AU - Niu, Zhe C1 - Shandong University of Technology, Zibo, Shandong, China KW - Simulated annealing KW - Particle swarm optimization KW - Motion planning KW - Predictability KW - Benchmark (surveying) KW - Kinematics KW - Global optimization KW - Optimization problem SP - 801 EP - 808 AB - This paper put forward a particle swarm optimization algorithm founded on simulated annealing to tackle the difficulty of 3D route planning for unmanned aerial vehicles within densely-populated urban areas, which is a central matter in the future design of urban aerial industrial systems; the algorithm took on a service-design perspective, giving top priority to safe interactions, and seamless integration into the city, surmounting the restrictions of conventional optimization techniques to produce flight routes that strike a balance between computational effectiveness and human-centered design principles; this combined method ingeniously merged the global exploration abilities of PSO and the local optimization capabilities of simulated annealing, creating smooth and foreseeable paths that fulfilled stringent operational requirements; these paths had design features vital for public acceptance, namely predictability and safety; experimental outcomes showed that the algorithm performed better than benchmark methods, attaining the shortest collision-free flight time while completely meeting kinematic constraints; this work united computational optimization with service-system design, offering a basic means for developing technically reliable and socially acceptable urban aerial logistics and monitoring systems. ER - TY - JOUR TI - A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms PY - 2022 SN - 1546-2218 T2 - Computers, materials & continua/Computers, materials & continua (Print) DO - 10.32604/cmc.2022.024932 UR - https://doi.org/10.32604/cmc.2022.024932 DA - 2022-01-01 AU - Ragab, Mahmoud AU - Altalbe, Ali AU - ALGhamdi, Abdullah AU - Abdel‐Khalek, S. AU - Saeed, Rashid LA - en KW - Drone KW - Cluster analysis KW - Computer science KW - Swarm intelligence KW - Novelty KW - The Internet KW - Swarm behaviour KW - Particle swarm optimization KW - Data mining KW - Artificial intelligence KW - Machine learning KW - World Wide Web VL - 72 IS - 1 SP - 365 EP - 380 AB - The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches. ER - TY - JOUR TI - Optimizing Energy Efficiency in Last-Mile Delivery: A Collaborative Approach with Public Transportation System and Drones PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones9080513 UR - https://doi.org/10.3390/drones9080513 DA - 2025-07-22 AU - Romet, Pierre C1 - CIAD Laboratory UMR 7533, Université de Technologie de Belfort-Montbéliard, 25200 Belfort, France AU - Hage, Charbel C1 - DRIVE UR 1859, Université Bourgogne Europe, 58000 Nevers, France AU - Aglzim, El‐Hassane C1 - DRIVE UR 1859, Université Bourgogne Europe, 58000 Nevers, France AU - Sophy, Tonino C1 - DRIVE UR 1859, Université Bourgogne Europe, 58000 Nevers, France AU - Gechter, Franck C1 - CIAD Laboratory UMR 7533, Université de Technologie de Belfort-Montbéliard, 25200 Belfort, France LA - en KW - Mile KW - Drone KW - Last mile (transportation) KW - Public transport KW - Transport engineering KW - Aeronautics KW - Computer science KW - Engineering KW - Business KW - Geography VL - 9 IS - 8 SP - 513 EP - 513 AB - Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. ER - TY - GEN TI - The Convergence of UAV D2X Communication and Intelligent Networking PY - 2025 DO - 10.1109/ictc66702.2025.11387873 UR - https://doi.org/10.1109/ictc66702.2025.11387873 DA - 2025-10-14 AU - Liu, Yusen C1 - Qilu Normal University,School of Physics and Electronic Engineering,Jinan,China AU - Liu, Shuzhi C1 - Qilu Normal University,School of Physics and Electronic Engineering,Jinan,China AU - Wang, Zhongyi C1 - Qilu Normal University,School of Physics and Electronic Engineering,Jinan,China KW - Software deployment KW - Key (lock) KW - Enabling KW - Cloud computing KW - Architecture KW - Edge computing KW - Drone KW - Convergence (economics) KW - Telecommunications network SP - 278 EP - 282 ER - TY - GEN TI - Optimization of drone and truck emergency delivery using artificial bee colony-Clarke-Wright algorithm PY - 2021 T2 - International Conference on Smart Transportation and City Engineering 2021 DO - 10.1117/12.2613944 UR - https://doi.org/10.1117/12.2613944 DA - 2021-11-10 AU - Han, Shuguang C1 - Zhejiang Sci-Tech Univ. (China) AU - Yang, Hang C1 - Zhejiang Sci-Tech Univ. (China) AU - Hu, Jueliang C1 - Zhejiang Sci-Tech Univ. (China) AU - Zhang, Tianhui C1 - Beijing Electro-mechanical Engineering Institute (China) LA - en KW - Drone KW - Truck KW - Computer science KW - Artificial bee colony algorithm KW - Wright KW - Operations research KW - Algorithm KW - Transport engineering KW - Engineering KW - Artificial intelligence KW - Automotive engineering SP - 100 EP - 100 ER - TY - CHAP TI - UAV Wing Structure: Status and Future Trend PY - 2025 PB - Atlantis Press SN - 2352-5401 T2 - Advances in engineering research/Advances in Engineering Research DO - 10.2991/978-94-6463-821-9_106 UR - https://doi.org/10.2991/978-94-6463-821-9_106 DA - 2025-01-01 AU - Hu, Chengcheng C1 - Faculty of Maritime and Transportation , Ningbo University , Zhejiang Province , 315211 , China LA - en KW - Wing KW - Geography KW - Economic geography KW - Aeronautics KW - Aerospace engineering KW - Engineering SP - 1102 EP - 1117 AB - Unmanned Aerial Vehicles (UAVs) have emerged as a critical technological component with widespread applications in military reconnaissance, civil logistics, medical delivery, and environmental monitoring, with technological advancements fundamentally dependent on continuous innovations in structural design, materials science, and electronic integration.Structural design, serving as the core of UAV technology, directly determines flight performance, mission adaptability, and operational lifespan, and this comprehensive review systematically examines fundamental design principles, analyzing fixed-wing, rotary-wing, and hybrid configurations while exploring innovative approaches such as lightweight technologies, modular design, and biomimetic strategies.By critically evaluating the current challenges including payload limitations, wind resistance, regulatory constraints, and the delicate balance between durability and cost, the research illuminates the trajectory of UAV structural design towards intelligent, lightweight, and sustainable development, emphasizing the potential for interdisciplinary breakthroughs in urban air mobility, deep space exploration, and swarm mission capabilities that will ultimately promote the global technological accessibility and the sustainable advancement. ER - TY - THES TI - Research on the Synergistic Development Model of Unmanned Aerial Vehicle Logistics and Traditional Air Logistics under the Background of Low-altitude Economy PY - 2026 T2 - Theseus (Ammattikorkeakoulujen) DA - 2026-01-01 AU - Huang, Yurou LA - en KW - Transshipment (information security) KW - Backup KW - Truck KW - Scope (computer science) KW - Resource (disambiguation) KW - Air traffic control KW - Track (disk drive) KW - Traffic management KW - Plan (archaeology) AB - Against the backdrop of the low-altitude economy and rising demand for time-sensitive logistics, traditional air freight still faces the structural problem of “fast trunk lines, congested last mile”. This thesis takes SF Express as a single case and, through literature review, case analysis, and comparative analysis, examines how its trunk aircraft, FH-98 branch UAVs, and Arkseries terminal UAVs form a three-level logistics network. The scope is bounded to SF's publicly disclosed operations in the Guangdong-Hong Kong-Macao Greater Bay Area and selected mountainous and island routes from 2022 to 2025. The analysis shows that the model performs well in scenarios that are difficult for ground transport. On the Shenzhen Dapeng Bay—Zhuhai Doumen route, UAV relay cuts transit time by about 61% compared with a truck detour; on the Hong Kong Cyberport—Cheung Chau route, delivery time falls from 68 minutes to 24 minutes. The main constraints are managerial rather than technological: transshipment SOPs are not standardised, information systems are not fully linked, emergency switching under extreme weather remains weak, and airspace approval is uneven. These factors create handoff waiting times of 48 to 110 minutes and limit UAV competitiveness in short urban routes where ground transport still has a cost advantage. To address these issues, the thesis proposes a standardised transshipment SOP, dynamic time-slot matching, an air-ground backup mechanism, and scenario-based capacity allocation. It argues that UAV logistics should be treated as a complementary capacity in specific scenarios rather than as a universal replacement for ground transport, with the goal of improving efficiency, resilience, and sustainable resource use. ER - TY - CHAP TI - Drones in Delivery and Logistics Transforming Transportation Systems PY - 2025 DO - 10.1201/9781003481584-4 UR - https://doi.org/10.1201/9781003481584-4 DA - 2025-07-09 AU - Srivastav, Aditya LA - en KW - Drone KW - Business KW - Aeronautics KW - Transport engineering KW - Engineering KW - Biology SP - 86 EP - 117 ER - TY - JOUR TI - ISAC-empowered deep reinforcement learning scheme for eVTOL approach trajectory optimization with radar point cloud PY - 2026 PB - Elsevier BV SN - 1366-5545 T2 - Transportation Research Part E Logistics and Transportation Review DO - 10.1016/j.tre.2026.104681 UR - https://doi.org/10.1016/j.tre.2026.104681 DA - 2026-01-15 AU - Chen, Leyan C1 - School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China C1 - State Key Laboratory of Intelligent Green Vehicles and Mobility, Tsinghua University, Beijing, 100084, China AU - Wang, Yuhao C1 - School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China C1 - State Key Laboratory of Intelligent Green Vehicles and Mobility, Tsinghua University, Beijing, 100084, China AU - Chen, Shulu C1 - School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China C1 - State Key Laboratory of Intelligent Green Vehicles and Mobility, Tsinghua University, Beijing, 100084, China AU - Sun, Yong C1 - School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China AU - Wang, Kai C1 - School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China LA - en KW - Robustness (evolution) KW - Reinforcement learning KW - Radar KW - Cloud computing KW - Trajectory KW - Efficient energy use KW - Air traffic control KW - Benchmark (surveying) KW - Point cloud VL - 208 SP - 104681 EP - 104681 ER - TY - JOUR TI - Vision-Based UAV Localization by Aerial and Satellite Image Matching PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2026.3684630 UR - https://doi.org/10.1109/access.2026.3684630 DA - 2026-01-01 AU - Lin, Huei‐Yung C1 - Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan AU - Chen, Po-Hsun C1 - Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan AU - Yeh, Chi-Wei C1 - Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan LA - en KW - Satellite KW - Matching (statistics) KW - Image matching KW - Satellite image KW - Image (mathematics) KW - Aerial image KW - Image processing VL - 14 SP - 61452 EP - 61461 AB - The deployment of Unmanned Aerial Vehicles (UAVs) in tasks such as urban surveillance, disaster assessment, and logistics necessitates enhanced autonomous navigation capabilities. Traditionally, the localization of UAVs relies on Global Navigation Satellite Systems (GNSS). However, in urban canyons, indoor environments, or bad weather conditions, GNSS signals are prone to obstruction and interference, leading to degraded navigation performance. To overcome the limitations, the development of GNSS-denied alternative localization techniques has become an important topic. In this paper we present an UAV visual localization and heading estimation in GNSS-denied environments by matching the UAV overhead imagery with satellite maps. The proposed NavCLIP constructs an image database using satellite data and retrieves the best-matched by querying with aerial images. A homography matrix is then employed to estimate the UAV location and yaw angle. Due to the lack of publicly available datasets, this work collects an aerial and satellite image dataset (UAV-Satellite Dataset) for training and validation. The experimental results show strong localization performance on the Google Earth dataset, while UAV flight experiments exhibit larger localization errors under the real-world conditions. In addition, the proposed technique improves over the GeoCLIP baseline and achieves low heading estimation error. Code and dataset are available on GitHub at https://github.com/PHChenGit/NavCLIP. ER - TY - CHAP TI - Blockchain governance and legal frameworks for sustainable UAV ecosystems PY - 2026 DO - 10.1201/9781003731580-13 UR - https://doi.org/10.1201/9781003731580-13 DA - 2026-08-17 AU - Sinha, Rituraj AU - Singh, Sheetal AU - Verma, Vijeta AU - Solanki, Shashank AU - Pandey, Vandana LA - en KW - Agency (philosophy) KW - Context (archaeology) KW - Drone KW - European union KW - Corporate governance KW - Civil aviation KW - Data Protection Act 1998 KW - Aviation KW - Software deployment KW - General partnership SP - 253 EP - 277 ER - TY - GEN TI - Assessing Drone-Based Last-Mile Logistics—A Hybrid Solution PY - 2023 PB - Springer Nature SN - 2195-4356 T2 - Lecture notes in mechanical engineering DO - 10.1007/978-981-99-6874-9_13 UR - https://doi.org/10.1007/978-981-99-6874-9_13 DA - 2023-11-18 AU - Lamiscarre, Bruno C1 - Durban University of Technology, DUT, Berea, South Africa C1 - Neometsys, Toulouse, France AU - Davidson, Innocent C1 - Durban University of Technology, DUT, Berea, South Africa AU - Mykoniatis, Georges C1 - ENAC-Toulouse, Toulouse, France AU - Cruz, Luis C1 - Universidade Federal Fluminense, UFF, Niterói, Brazil AU - Mora‐Camino, Félix C1 - Durban University of Technology, DUT, Berea, South Africa C1 - ENAC-Toulouse, Toulouse, France C1 - Neometsys, Toulouse, France C1 - Universidade Federal Fluminense, UFF, Niterói, Brazil LA - en KW - Last mile (transportation) KW - Transport engineering KW - Metropolitan area KW - City logistics KW - Drone KW - Operations research KW - Computer science KW - Service level KW - Business KW - Mile KW - Engineering KW - Geography SP - 161 EP - 171 ER - TY - JOUR TI - Hybrid VTOL UAV Architectures and Their Implementation in Modern Aerial Systems PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6002534 UR - https://doi.org/10.2139/ssrn.6002534 DA - 2026-01-01 AU - Alqudsi, Yunes C1 - King Fahad University of Petroleum and Minerals C1 - Kocaeli University C1 - Kocaeli University, Umuttepe Kampus, Hukuk Fakultesi, Kocaeli, Kocaeli +90, Turkey AU - Sulaiman, Husam C1 - Kocaeli University AU - Aydemir, Ebru C1 - Kocaeli University AU - Alyoussef, Fadi C1 - King Fahd University of Petroleum and Minerals (KFUPM) KW - Drone KW - Takeoff and landing KW - Air traffic control KW - Propulsion KW - Aviation KW - Multirotor KW - Multidisciplinary approach KW - Air traffic management KW - Control (management) ER - TY - JOUR TI - Hybrid VTOL UAV Architectures and Their Implementation in Modern Aerial Systems PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6002016 UR - https://doi.org/10.2139/ssrn.6002016 DA - 2026-01-01 AU - Alqudsi, Yunes C1 - King Fahad University of Petroleum and Minerals C1 - Kocaeli University C1 - Kocaeli University, Umuttepe Kampus, Hukuk Fakultesi, Kocaeli, Kocaeli +90, Turkey AU - Sulaiman, Husam C1 - Kocaeli University AU - Aydemir, Ebru C1 - Kocaeli University AU - Alyoussef, Fadi C1 - King Fahd University of Petroleum and Minerals (KFUPM) KW - Drone KW - Takeoff and landing KW - Air traffic control KW - Propulsion KW - Aviation KW - Multirotor KW - Multidisciplinary approach KW - Air traffic management KW - Control (management) ER - TY - JOUR TI - Hybrid VTOL UAV Architectures and their Implementation in Modern Aerial Systems PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6000474 UR - https://doi.org/10.2139/ssrn.6000474 DA - 2026-01-01 AU - Alqudsi, Yunes C1 - King Fahad University of Petroleum and Minerals C1 - Kocaeli University C1 - Kocaeli University, Umuttepe Kampus, Hukuk Fakultesi, Kocaeli, Kocaeli +90, Turkey AU - Sulaiman, Husam C1 - Kocaeli University AU - Aydemir, Ebru C1 - Kocaeli University AU - Alyoussef, Fadi C1 - King Fahd University of Petroleum and Minerals (KFUPM) KW - Drone KW - Takeoff and landing KW - Air traffic control KW - Propulsion KW - Aviation KW - Multirotor KW - Multidisciplinary approach KW - Air traffic management KW - Control (management) ER - TY - GEN TI - Research on Distribution Scheduling Based on UAV Energy Self-sustaining System PY - 2023 DO - 10.1109/ic2ecs60824.2023.10493493 UR - https://doi.org/10.1109/ic2ecs60824.2023.10493493 DA - 2023-12-29 AU - Li, Hanxu C1 - School of Civil Engineering, Xiamen University Tan Kah Kee College,Zhangzhou,China C1 - School of Civil Engineering, Xiamen University Tan Kah Kee College, Zhangzhou, China AU - An, Xiaoyan C1 - School of Accounting and Finance, Xiamen University Tan Kah Kee College,Zhangzhou,China C1 - School of Accounting and Finance, Xiamen University Tan Kah Kee College, Zhangzhou, China AU - Wu, Jingyao C1 - School of Management, Xiamen University Tan Kah Kee College,Zhangzhou,China C1 - School of Management, Xiamen University Tan Kah Kee College, Zhangzhou, China AU - Zhao, Meiling C1 - School of International Business, Xiamen University Tan Kah Kee College,Zhangzhou,China C1 - School of International Business, Xiamen University Tan Kah Kee College, Zhangzhou, China LA - en KW - Computer science KW - Scheduling (production processes) KW - Distributed computing KW - Real-time computing KW - Engineering KW - Operations management VL - 46 SP - 160 EP - 164 ER - TY - CHAP TI - Introduction to Path Planning in an Unmanned Aerial Vehicles (UAV) Network PY - 2025 T2 - Apple Academic Press eBooks DO - 10.1201/9781779643599-4 UR - https://doi.org/10.1201/9781779643599-4 DA - 2025-09-17 AU - Vashisth, Anshu AU - Singh, Balraj LA - en KW - Scalability KW - Motion planning KW - Adaptability KW - Path (computing) KW - Adaptation (eye) KW - Drone KW - Collision avoidance KW - Enhanced Data Rates for GSM Evolution SP - 57 EP - 85 ER - TY - ENTRY TI - Drone Urbanisms PY - 2026 DO - 10.1093/9780197852712.003.0126 UR - https://doi.org/10.1093/9780197852712.003.0126 DA - 2026-06-25 AU - Shee, Siew C1 - Durham University , C1 - Singapore Management University LA - en KW - Drone KW - Agency (philosophy) KW - CONTEST KW - Corporate governance KW - Software deployment KW - Narrative KW - Temporality KW - Perspective (graphical) AB - Summary Across contemporary cities, unmanned aerial vehicles (UAVs) particularly drones have emerged as critical instruments for sensing, visualizing, and reconfiguring the built environment. Yet, urban space is not merely a passive backdrop for drone activity; it both shapes and is reshaped by them. The central question for understanding drone urbanism, then, is not only what drones do, but how their deployment redistributes the agency to see, know, and intervene across human operators, infrastructures, and algorithms. This renegotiation of visibility, authority, and knowledge takes shape through the ways drones mediate the governance and experiential textures of the city. Departing from the distant totalities of satellite vision, drones enact a proximate and mobile mode of perception that renders vertical surfaces and interstitial volumes newly legible. In doing so, they reconfigure the epistemic conditions of urban knowledge: how environments are modeled, risks anticipated, and publics addressed. Their integration into planning, logistics, and environmental monitoring translates speculative imaginaries of efficiency or safety into tangible mechanisms of spatial control. At the same time, the rise of consumer and community-operated drones has begun to pluralize the authority to observe and intervene in urban space. In some cases, these practices enable counter-visual practices that contest official narratives and expand civic participation in aerial domains that were once the exclusive domain of corporations or the state. Together, these dynamics reveal drones as infrastructural agents that mediate between computational systems, governance practices, and everyday urban experience. Beyond simply extending surveillance or mobility, drones reorganize the very terms of how cities are sensed and managed, defining who can see, what can be known and intervened, and how action is coordinated through the air. As such, drone urbanisms mark the rise of volumetric infrastructures that make perception and control integral to the very fabric of contemporary urban life. ER - TY - JOUR TI - Drone urbanisms PY - 2026 PB - Singapore Management University T2 - Singapore Management University Institutional Knowledge (InK) (Singapore Management University) DA - 2026-06-01 AU - Shee, Siew C1 - Singapore Management University LA - en KW - Drone KW - Agency (philosophy) KW - CONTEST KW - Corporate governance KW - Software deployment KW - Narrative KW - Temporality AB - Across contemporary cities, unmanned aerial vehicles (UAVs) particularly drones have emerged as critical instruments for sensing, visualizing, and recon guring the built environment.Yet, urban space is not merely a passive backdrop for drone activity; it both shapes and is reshaped by them.The central question for understanding drone urbanism, then, is not only what drones do, but how their deployment redistributes the agency to see, know, and intervene across human operators, infrastructures, and algorithms.This renegotiation of visibility, authority, and knowledge takes shape through the ways drones mediate the governance and experiential textures of the city.Departing from the distant totalities of satellite vision, drones enact a proximate and mobile mode of perception that renders vertical surfaces and interstitial volumes newly legible.In doing so, they recon gure the epistemic conditions of urban knowledge: how environments are modeled, risks anticipated, and publics addressed.Their integration into planning, logistics, and environmental monitoring translates speculative imaginaries of ef ciency or safety into tangible mechanisms of spatial control.At the same time, the rise of consumer and community-operated drones has begun to pluralize the authority to observe and intervene in urban space.In some cases, these practices enable counter-visual practices that contest of cial narratives and expand civic participation in aerial domains that were once the exclusive domain of corporations or the state.Together, these dynamics reveal drones as infrastructural agents that mediate between computational systems, governance practices, and everyday urban experience.Beyond simply extending surveillance or mobility, drones reorganize the very terms of how cities are sensed and managed, de ning who can see, what can be known and intervened, and how action is coordinated through the air.As such, drone urbanisms mark the rise of volumetric infrastructures that make perception and control integral to the very fabric of contemporary urban life. ER - TY - GEN TI - Integrated Mobile Command Platform for UAV Operation, Inspection and Dispatching PY - 2022 DO - 10.1145/3582084.3582090 UR - https://doi.org/10.1145/3582084.3582090 DA - 2022-11-25 AU - Ma, Yonglong C1 - Guangzhou Conghua Power Supply Bureau, Guangzhou Power Supply Bureau, China AU - Fang, Binghua C1 - Guangzhou Conghua Power Supply Bureau, Guangzhou Power Supply Bureau, China AU - Mingjie, Xiao C1 - Guangzhou Conghua Power Supply Bureau, Guangzhou Power Supply Bureau, China AU - Wang, Xiaocong C1 - Guangzhou Conghua Power Supply Bureau, Guangzhou Power Supply Bureau, China LA - en KW - Computer science KW - Robust optimization KW - Robustness (evolution) KW - Scheduling (production processes) KW - Stochastic programming KW - Mathematical optimization KW - Operations research KW - Service level KW - Real-time computing KW - Engineering VL - 2 SP - 26 EP - 35 AB - In order to effectively solve the problems of poor infrastructure in remote areas and high human distribution cost in developed areas, the design of integrated UAV network for urban and rural logistics and the scheduling method of UAV onboard platform are proposed.Considering the situation that self-run logistics stocks goods in advance according to the forecast of demand, in this essay, under the distribution situation of dispersed customer demand and frequent demand fluctuation over time, the uncertain demand of customers is described by polyhedron set, and the two-stage robust optimization model of plane-carrying platform scheduling is established, and the L-type algorithm is designed to solve the problem.The experimental results show that when, the target value obtained by the robust optimization method is close to that of the stochastic programming, and the scheduling path obtained by the two methods in the first stage is basically the same, except for the difference in the determination of the service relationship in the second stage.When the budget level was increased from 0.0 to 0.2, the number of on-board platforms used remained at 8, while the penalty cost and unmet requirements decreased.Conclusion: The efficiency of robust optimization is better than that of stochastic programming, and the decision economy is greatly improved by relaxing the model robustness appropriately.In the face of uncertain demand, the system will first adjust the scheduling path of the on-board platform, and then consider increasing the number of on-board platforms. The scheduling scheme shows a rule of gradual adjustment. ER - TY - GEN TI - Research on Dynamic Obstacle Avoidance and Collaborative Control for Low-Altitude Aircraft Based on Deep Reinforcement Learning and Adaptive Signal Filtering PY - 2025 DO - 10.1109/icsp65755.2025.11086680 UR - https://doi.org/10.1109/icsp65755.2025.11086680 DA - 2025-05-16 AU - Zhang, Ning C1 - Guangzhou Civil Aviation College,Guangzhou,China LA - en KW - Reinforcement learning KW - Obstacle avoidance KW - Computer science KW - SIGNAL (programming language) KW - Obstacle KW - Adaptive control KW - Low altitude KW - Control (management) KW - Control theory (sociology) KW - Artificial intelligence KW - Control engineering KW - Aeronautics KW - Altitude (triangle) KW - Engineering KW - Mobile robot KW - Robot KW - Geography KW - Mathematics SP - 841 EP - 846 ER - TY - JOUR TI - Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks PY - 2022 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2210.00490 UR - https://doi.org/10.48550/arxiv.2210.00490 DA - 2022-10-02 AU - Lu, Ziyi AU - Yu, Na AU - Wang, Xuehe LA - en KW - Pathfinding KW - Computer science KW - Incentive KW - Path (computing) KW - Motion planning KW - Scheme (mathematics) KW - Real-time computing KW - Simulation KW - Computer network KW - Shortest path problem KW - Artificial intelligence KW - Graph AB - Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only. ER - TY - GEN TI - Innovative Research on a Joint UAV-Vehicle Delivery System for the Low-Altitude Economy PY - 2026 DO - 10.1145/3807211.3807236 UR - https://doi.org/10.1145/3807211.3807236 DA - 2026-02-06 AU - Lu, Xue C1 - Foreign Languages College, Kashi University, Kashgar, Xinjiang, China AU - Zhang, Jizhe C1 - College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou, Gansu, China KW - Payload (computing) KW - Context (archaeology) KW - Sustainability KW - Vehicle routing problem KW - Resource allocation KW - Routing (electronic design automation) KW - Operational efficiency KW - Joint (building) KW - Resource (disambiguation) SP - 175 EP - 182 AB - Urban logistics is increasingly challenged by the “last-mile” problem, traffic congestion, and high operational costs. To address these issues, this paper proposes a joint delivery paradigm that integrates unmanned aerial vehicles (UAVs) with ground vehicles. Focusing on route optimization in the cooperative setting, we model practical operational constraints of both vehicles and UAVs, including payload capacity, travel speed, endurance, and delivery time windows. A mixed-integer programming formulation is developed to optimize routing decisions, reduce operating costs, and mitigate environmental impacts. To solve the resulting complex resource allocation and routing problem, we employ a Water Wave Optimization (WWO) algorithm and conduct simulation experiments across four urban scenarios of different scales. The results indicate that, compared with conventional delivery approaches, the proposed joint delivery system reduces average operating costs by approximately 26.65% and decreases carbon emissions by 53.74%. Notably, in high-density urban areas, delivery efficiency is substantially improved, achieving the intended optimization objectives. These findings demonstrate the practical effectiveness of UAV-vehicle cooperative delivery for enhancing urban distribution efficiency and environmental sustainability in the context of the low-altitude economy, and provide actionable strategies and technical support for future optimization of urban logistics systems. ER - TY - JOUR TI - Routing optimization for UAV-rider joint delivery in multi-center networks PY - 2026 T2 - DOAJ (DOAJ: Directory of Open Access Journals) DO - 10.3724/sp.j.1249.2026.04397 UR - https://doi.org/10.3724/sp.j.1249.2026.04397 DA - 2026-07-01 AU - Yaozhen, Bai AU - Li, Xueyou AU - Mingyao, Qi LA - zh KW - Payload (computing) KW - Queue KW - Benchmark (surveying) KW - Routing (electronic design automation) KW - Linear programming KW - Integer programming KW - Queueing theory KW - Operating cost KW - Bottleneck ER - TY - JOUR TI - Vertical Drone Delivery with Obstacle Avoidance PY - 2025 PB - Elsevier BV SN - 2405-8963 T2 - IFAC-PapersOnLine DO - 10.1016/j.ifacol.2025.07.033 UR - https://doi.org/10.1016/j.ifacol.2025.07.033 DA - 2025-01-01 AU - Al-Musallami, Nasser C1 - Dalhousie University, Industrial Engineering, Halifax, Nova Scotia, Canada AU - O’Neil, Ryan C1 - Dalhousie University, Industrial Engineering, Halifax, Nova Scotia, Canada AU - Khatab, Abdelhakim C1 - Université de Lorraine, LGIPM, F-57000 Metz, France AU - Venkatadri, Uday C1 - Dalhousie University, Industrial Engineering, Halifax, Nova Scotia, Canada AU - Diallo, Claver C1 - Dalhousie University, Industrial Engineering, Halifax, Nova Scotia, Canada LA - en KW - Drone KW - Obstacle avoidance KW - Obstacle KW - Computer science KW - Aeronautics KW - Psychology KW - Artificial intelligence KW - Engineering KW - History KW - Biology KW - Mobile robot KW - Robot VL - 59 IS - 3 SP - 192 EP - 197 AB - This paper introduces a hybrid drone-truck delivery model to optimize UAV-based logistics in last-mile delivery settings. The study combines energy consumption dynamics with advanced route optimization, considering factors such as payload weight, elevation changes, and obstacle avoidance. Utilizing the Theta ⁎ algorithm, the model achieves efficient path-finding while optimizing energy use through a scheme that applies energy over-consumption or under-consumption for ascent and descent, respectively. Additionally, the study incorporates profit modeling for successful deliveries to assess energy-revenue trade-offs. Results confirm the model’s ability to improve the operational efficiency of last-mile delivery systems, thus advancing sustainable urban logistics and addressing limitations in current drone delivery systems. ER - TY - GEN TI - Time-Varying Formation Control and Collision Avoidance Strategy for UAV Clusters PY - 2025 DO - 10.1109/cpsi66656.2025.11343905 UR - https://doi.org/10.1109/cpsi66656.2025.11343905 DA - 2025-11-07 AU - Lu, Rongwei C1 - School of Astronautics Northwestern Polytechnical University,Xi'an,China AU - XU, Yixin C1 - School of Astronautics Northwestern Polytechnical University,Xi'an,China AU - Li, Yue C1 - School of Astronautics Northwestern Polytechnical University,Xi'an,China AU - Yang, Jun C1 - School of Astronautics Northwestern Polytechnical University,Xi'an,China KW - Collision avoidance KW - Obstacle avoidance KW - Maxima and minima KW - Convergence (economics) KW - Control theory (sociology) KW - Swarm behaviour KW - Lyapunov function KW - Protocol (science) KW - Collision SP - 1 EP - 6 ER - TY - JOUR TI - Route efficiency and carbon emissions of commercial UAV delivery: Evidence from Shenzhen PY - 2026 PB - Elsevier BV SN - 1361-9209 T2 - Transportation Research Part D Transport and Environment DO - 10.1016/j.trd.2026.105548 UR - https://doi.org/10.1016/j.trd.2026.105548 DA - 2026-07-27 AU - Wang, Jing-Peng C1 - School of Management, Shandong University, Jinan, Shandong, 250000, China AU - Zhou, Yong C1 - Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen, Guangdong, 518058, China AU - Cui, Yang C1 - MOE Key Laboratory of Complex System Analysis and Management Decision, School of Economics and Management, Beihang University, Beijing, 100191, China AU - Tian, Qiong C1 - MOE Key Laboratory of Complex System Analysis and Management Decision, School of Economics and Management, Beihang University, Beijing, 100191, China AU - Liu, Peng C1 - MOE Key Laboratory of Complex System Analysis and Management Decision, School of Economics and Management, Beihang University, Beijing, 100191, China LA - en KW - Baseline (sea) KW - Software deployment KW - Greenhouse gas KW - Telemetry KW - Carbon fibers KW - Trajectory VL - 159 SP - 105548 EP - 105548 ER - TY - GEN TI - Deep reinforcement learning for path optimization in UAV-enabled emergency material distribution PY - 2026 DO - 10.1117/12.3112477 UR - https://doi.org/10.1117/12.3112477 DA - 2026-04-28 AU - Liu, Hao C1 - Shandong University of Science and Technology (China) KW - Path (computing) KW - Reinforcement learning KW - Distribution (mathematics) KW - Reinforcement KW - Control (management) KW - Minification SP - 40 EP - 40 ER - TY - THES TI - Intelligent UAV systems: payload monitoring for spraying and collaborative cargo transport PY - 2025 DO - 10.47328/ufvbbt.2025.433 UR - https://doi.org/10.47328/ufvbbt.2025.433 DA - 2025-03-26 AU - Barcelos, Celso KW - Payload (computing) KW - Drone KW - Lift (data mining) KW - Precision agriculture KW - Global Positioning System KW - Control (management) AB - The use of unmanned aerial vehicles (UAVs) has been expanding rapidly, bringing innovations to various areas, such as precision agriculture and logistics. This dissertation investigates the integration of payload sensors into UAVs to improve agricultural spraying tasks and collaborative cargo transportation. Initially, a real-time payload monitoring system is proposed, which allows the UAV to adjust the application of agricultural inputs according to the variation in the weight of the spray tank. This mechanism avoids waste and improves the efficiency of the operation. In addition, an intelligent navigation system was developed that monitors the tank level and commands automatic return to base when necessary. To validate the proposal, experiments were conducted in controlled environments, demonstrating the system's effectiveness in controlling the distribution of inputs. In addition to agricultural applications, this research addresses the cooperative transportation of cargo by UAVs and unmanned ground vehicles (UGVs). An aerial transportation system was developed using a UAV equipped with an electromagnet suspended from a cable, making it possible to lift and move loads to overcome obstacles in the path of a UGV. Experimental tests have shown that the proposed approach is viable for applications such as urban deliveries, operations in remote areas and logistical support in disaster zones. The results obtained show that the integration of cargo sensors with intelligent control strategies promotes a significant improvement in UAV performance in various operational scenarios. This improvement is mainly due to the systems' ability to provide real-time information on the weight carried, allowing the UAV to make more precise autonomous decisions. This makes it possible to identify the ideal time to return to base for refuelling or to end the spraying mission, reducing waste and optimizing the use of inputs. In addition, there is more efficient management of energy consumption and flight time, based on the actual load carried. Another important advance is the automation of the load coupling and uncoupling processes, increasing agility in logistics missions and reducing the need for human intervention. These functionalities together result in safer, efficient and sustainable operations. Thus, the contributions of this work represent a relevant advance in the state of the art of UAV application in precision agriculture and autonomous cargo transportation. Keywords: Precision Agriculture; Path following; Load Transportation; Multi-robot Systems; Cable-suspended cargo; UAV-UGV Cooperation ER - TY - JOUR TI - Optimization of Vehicle-UAV Collaborative Delivery Path Based on Improved Genetic Algorithm PY - 2025 SN - 2633-4569 T2 - Theory and Practice of Science and Technology DO - 10.47297/taposatwsp2633-456935.20250610 UR - https://doi.org/10.47297/taposatwsp2633-456935.20250610 DA - 2025-01-01 AU - Jiahui, Wang C1 - School of Information Science and Engineering , Shandong University , Qingdao Campus , Qingdao , Shandong , 266237 , China LA - en KW - Crossover KW - Robustness (evolution) KW - Genetic algorithm KW - Convergence (economics) KW - Stability (learning theory) KW - Task (project management) VL - 6 IS - 10 SP - 173 EP - 179 AB - As urban demand for last-mile logistics grows rapidly, vehicle-UAV collaborative delivery has drawn widespread interest as an efficient distribution mode. To address the path optimization problem in the collaborative delivery of a single vehicle with two UAVs, this study proposes an optimization approach based on an improved genetic algorithm. Multi-chromosome hybrid encoding is employed to simultaneously represent the collaborative paths of the vehicle and UAVs. The algorithm's global search capability and convergence speed are enhanced by integrating elitism preservation, adaptive crossover and mutation, neighborhood transformation, and task perturbation strategies. A multi-objective optimization model is established to comprehensively consider delivery time, energy consumption, and cost, with simulation experiments conducted in the MATLAB.The experimental results demonstrate that the improved algorithm significantly reduces total delivery time, energy consumption, and costs compared to traditional single-vehicle delivery and standard genetic algorithms, while also enabling reasonable task allocation and path planning. Additionally, the algorithm shows good stability and robustness under different parameter conditions. This research offers an effective approach and theoretical reference for optimizing vehicle-UAV collaborative delivery routes in intelligent logistics systems, and has practical significance for improving the efficiency of urban last-mile delivery. ER - TY - JOUR TI - H.A.W.K.: Hybrid Adaptive Waypoint Knowledge for Multi-Domain UAV Navigation PY - 2026 SN - 2583-6129 T2 - International Scientific Journal of Engineering and Management DO - 10.55041/isjem06067 UR - https://doi.org/10.55041/isjem06067 DA - 2026-04-03 AU - Anupoju, Sasiram AU - Abhinav, Samala AU - Tingirikar, Rishikesh AU - Nalamasu, Tharun KW - Waypoint KW - Drone KW - Domain (mathematical analysis) KW - Adaptation (eye) KW - Navigation system KW - Mobile robot navigation KW - Adaptive system VL - 05 IS - 04 SP - 1 EP - 8 ER - TY - BOOK TI - Intelligent and Sustainable Engineering Systems for Industry 4.0 and Beyond PY - 2025 DO - 10.1201/9781003511298 UR - https://doi.org/10.1201/9781003511298 DA - 2025-01-02 AU - Fowdur, Tulsi AU - Milovanovic, Dragorad AU - Bojkovic, Zoran LA - en KW - Industry 4.0 KW - Business KW - Engineering management KW - Engineering KW - Manufacturing engineering KW - Construction engineering AB - Drones, or unmanned aerial vehicles (UAVs), are remotely or autonomously controlled aircraft crucial for data collection and real-time information delivery, pivotal in Industry 4.0. Originally utilized in military contexts, drones now play integral roles across diverse industries. Notably, they are transforming logistics with advancements in parcel and medical delivery systems. Companies like Amazon, UPS, and DHL are at the forefront, investing heavily in drone delivery technologies, with regulatory milestones paving the way for autonomous operations. In urban environments, loitering missions are increasingly common, encompassing tasks such as police surveillance, transportation network monitoring, and infrastructure inspection. These missions bolster efficiency, cut costs, and enhance safety across sectors. However, integrating drones into urban settings poses challenges. Technologically, issues such as battery life, flight time, and power consumption necessitate innovations like strategic charging stations and optimized battery use. Socially, concerns range from privacy and employment impacts to environmental considerations such as noise and visual pollution. Regulatory frameworks worldwide are evolving to manage drone integration, balancing innovation with safety and privacy. Implementing no-fly zones and camera restrictions addresses these concerns, requiring ongoing research and collaboration to fully leverage drone technology’s potential while mitigating associated risks. chapter 11|15 pages Smart Digitalization Technologies for Future Resilient and Sust ER - TY - JOUR TI - Human detection with YOLO for last-mile delivery applications using UAVs PY - 2026 PB - Copernicus Publications SN - 2194-9042 T2 - ISPRS annals of the photogrammetry, remote sensing and spatial information sciences DO - 10.5194/isprs-annals-x-3-w4-2025-349-2026 UR - https://doi.org/10.5194/isprs-annals-x-3-w4-2025-349-2026 DA - 2026-03-13 AU - Simões, Débora C1 - Department of Surveying and Cartography, Federal Institute of Education, Science and Technology of Southern Minas Gerais – IFSULDEMINAS, Inconfidentes - MG, Brazil C1 - Graduate Program in Civil Engineering, School of Civil Engineering, Architecture and Urban Design, University of Campinas – Unicamp, Campinas - SP, Brazil AU - de Oliveira, Henrique C1 - Department of Infrastructure and Environment, School of Civil Engineering, Architecture and Urban Design, University of Campinas – Unicamp, Campinas - SP, Brazil AU - dos Santos, Rafael C1 - Graduate Program in Civil Engineering, School of Civil Engineering, Architecture and Urban Design, University of Campinas – Unicamp, Campinas - SP, Brazil LA - en KW - Inertial measurement unit KW - Orientation (vector space) KW - Photogrammetry KW - Global Positioning System KW - Path (computing) KW - Inference KW - Object detection KW - Rotation (mathematics) VL - X-3/W4-2025 SP - 349 EP - 356 AB - Abstract. Low-cost alternative solutions have advanced last-mile delivery, with Unmanned Aerial Vehicles (UAVs) emerging as a promising option for logistics tasks. However, as UAV operations increasingly occur in densely crowded urban areas, safety concerns - especially for people nearby - have intensified. To ensure safe deliveries, real-time UAV path planning is essential for avoiding no-fly zones defined around individuals detected along the route. This study addresses this challenge by evaluating human detection confidence in UAV imagery using the YOLOv7 model and a custom dataset. It also estimates individuals’ positions through the Monoplotting technique. The customized YOLOv7 model achieved an average precision of 53.8% and an inference time of 9.9 ms, supporting real-time deployment. However, challenges in UAV-based human detection significantly influenced detection confidence. In one scenario, confidence exceeded 85% for individuals identified at a flight height of 30 m, while in another, the highest confidence reached 60% for imagery captured at 20 m. Despite numerous false negatives, the individual closest to the UAV was consistently detected, underscoring the applicability of the method for real-time path replanning. Regarding coordinate estimation of detected individuals, the Inertial Measurement Unit (IMU) system exerted the greatest influence on the accuracy of 3D positions obtained through Monoplotting. The phototriangulation process, which provided the sensor orientation parameters, directly impacted the results. To reduce discrepancies between estimated and actual coordinates, mathematical correction strategies may be applied; however, these require further investigation. The integration of Simultaneous Localization and Mapping (SLAM) techniques offers a promising direction for refining UAV rotation angles. ER - TY - GEN TI - Strategy Optimization of Low-Altitude Logistics Systems based on Belief Reliability PY - 2025 DO - 10.1109/icsrs68021.2025.11422259 UR - https://doi.org/10.1109/icsrs68021.2025.11422259 DA - 2025-11-26 AU - Cao, Xuezhi C1 - Hangzhou International Innovation Institute of Beihang University Beihang University,Hangzhou,China AU - Li, Yingyi C1 - Hangzhou International Innovation Institute of Beihang University Beihang University,Hangzhou,China AU - Kang, Rui C1 - Hangzhou International Innovation Institute of Beihang University Beihang University,Hangzhou,China KW - Scheduling (production processes) KW - Reliability (semiconductor) KW - Key (lock) KW - Stability (learning theory) KW - Job shop scheduling KW - Quality (philosophy) KW - Service quality SP - 343 EP - 349 ER - TY - JOUR TI - METHODOLOGY AND APPLICATIONS OF AI ENABLED IMAGE RECOGNITION IN UAVS PY - 2025 PB - National Technical University "Kharkiv Polytechnic Institute" T2 - eNTUKhPIIR Repository (Kharkiv Polytechnic Institute) DO - 10.13140/rg.2.2.20489.30569 UR - https://doi.org/10.13140/rg.2.2.20489.30569 DA - 2025-01-01 AU - Balayev, F.R. AU - Hasanov, Arif AU - Hashimov, Elshan LA - en KW - Image (mathematics) KW - Image processing KW - Automatic target recognition KW - Feature (linguistics) KW - Image segmentation KW - Noise (video) KW - Feature extraction AB - This paper provides a systematic examination of artificial intelligence integration for image recognition on unmanned aerial vehicles, covering methodology, application domains, and operational implications.The motivation is the need to secure information superiority by reducing the sensing to decision latency while maintaining accuracy and resilience in contested environments.We propose an end-to-end Edge AI pipeline that runs entirely on the platform and combines multisensor inputs with optimized deep learning models.The architecture comprises five sequential stages: sensor acquisition from EO IR and SAR payloads, preprocessing for compression and normalization, object detection and tracking using configurable families such as YOLO, Faster R CNN, and DETR paired with DeepSORT, human in the loop validation for accountability, and mission management that route confirmed detections to navigation and payload subsystems.The design emphasizes real time operation on GPU or NPU based system on chip modules with strict energy budgets.Dataset construction follows three principles.First, domain relevance through acquisition in mountainous and urban terrains at varied altitudes and illumination.Second, interoperable labeling using COCO and VOC formats with classes such as armored vehicles, artillery, air defense, logistics vehicles, and personnel.Third, robust splits and class balance using a 70 15 15 partition with focal loss and targeted relabeling when necessary.Performance is evaluated by four axes.Accuracy uses mAP@[0.5:0.95],F1 score, false alarm rate, and ID switch count for tracking.Speed is measured as end-to-end latency and frames per second with a target below 50 ms total latency.Energy efficiency is quantified as joules per frame, where quantization and pruning yield 20 to 35 percent savings.Reliability is assessed under fog, night, urban clutter, camouflage, communications degradation, and GNSS spoofing.Experimental results demonstrate the inherent speed accuracy trade off.A quantized YOLO S achieves roughly 35 FPS with mAP near 0.55, YOLO M improves accuracy to about 0.62 at 22 FPS, while Faster R CNN reaches mAP near 0.64 with only 8 FPS.These findings support a configurable model portfolio where mission requirements and onboard compute define the operating point.Multisensor fusion at the AI level reduces false positives by 18 to 27 percent when EO IR and SAR are jointly exploited, which is especially beneficial at night and in low visibility.A representative latency profile allocates 8 to 12 ms to sensor and preprocessing, 15 to 28 ms to inference, and 5 to 10 ms to post processing, remaining under the 50 ms threshold.Thermal headroom in hot climates emerges as a practical constraint that motivates improved heat sinking and dynamic frequency scaling. ER - TY - JOUR TI - Exploring the New Blue Ocean of Low-altitude Economy: An Empirical Analysis of Industrial Status Quo and Technological Innovation References PY - 2025 SN - 2277-842X T2 - Journal of Social Science Humanities and Literature DO - 10.53469/jsshl.2025.08(05).05 UR - https://doi.org/10.53469/jsshl.2025.08(05).05 DA - 2025-05-30 AU - Li, Guiying C1 - School of Economics and Management, Guangxi Normal University, Guilin, Guangxi, China AU - Li, Wenyan C1 - School of Economics and Management, Guangxi Normal University, Guilin, Guangxi, China AU - Peng, Yixuan C1 - School of business administration, Guilin University, Guilin, Guangxi, China LA - en KW - Status quo KW - Economic geography KW - Business KW - Natural resource economics KW - Economics KW - Market economy VL - 8 IS - 5 SP - 28 EP - 38 AB - Low-altitude economy, as an important component of the modern economic system, is becoming a new engine for promoting high-quality regional development and industrial upgrading. The low-altitude economy industry, centered on unmanned aerial vehicles (UAVs), general aviation, and air traffic management, not only significantly enhances the efficiency of traditional industries but also gives rise to new business forms such as low-altitude tourism, urban air mobility (UAM), and smart logistics, forming a trillion-yuan market scale. According to statistics, the scale of China's low-altitude economy has exceeded 500 billion yuan in 2023, and it is expected to drive an industrial chain value of over 2 trillion yuan by 2030. The development of low-altitude economy is deeply in line with the national strategy. By unleashing the potential of low-altitude airspace resources, optimizing regional transportation networks, and promoting the integration of scientific and technological innovation and high-end manufacturing, it provides brand-new solutions for the coordinated development of urban agglomerations and rural revitalization. The breakthrough in this field is not only a technological revolution, but also a comprehensive test of institutional innovation, industrial collaboration and social governance capabilities. Its significance has risen to the dimensions of national competitiveness and economic security. Therefore, based on the perspective of the development of low-altitude economic industries in Guangdong and Guangxi, this paper conducts research and analysis on the current cognition status and future development trend of the low-altitude industries of the people in Guangdong and Guangxi. ER - TY - GEN TI - LLM Inference Offloading Optimization for UAVs in Low Altitude Intelligent Networking PY - 2026 PB - Springer Science+Business Media SN - 1865-0929 T2 - Communications in computer and information science DO - 10.1007/978-981-95-9615-7_27 UR - https://doi.org/10.1007/978-981-95-9615-7_27 DA - 2026-01-01 AU - Jiang, Aoyu C1 - The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, Jiangsu, China AU - Dong, Chao C1 - The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, Jiangsu, China AU - Qu, Yuben C1 - The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, Jiangsu, China AU - Dai, Haipeng C1 - Nanjing University, Nanjing, 210023, Jiangsu, China LA - en KW - Knapsack problem KW - Inference KW - Low latency (capital markets) KW - Latency (audio) KW - Enhanced Data Rates for GSM Evolution KW - Drone KW - Edge computing SP - 414 EP - 425 ER - TY - GEN TI - Research on UAV Path Planning Strategy Based on Fused Improved PSO Algorithm PY - 2025 DO - 10.1109/aiim67611.2025.11232595 UR - https://doi.org/10.1109/aiim67611.2025.11232595 DA - 2025-09-19 AU - Liu, Hanchen C1 - Chang'an University, School of Construction Machinery,Xi'an,China KW - Smoothness KW - Motion planning KW - Trajectory KW - Particle swarm optimization KW - Obstacle KW - Path (computing) KW - Key (lock) KW - Energy consumption KW - Potential field SP - 243 EP - 246 ER - TY - JOUR TI - A Study on the Integrated Application and Development Strategies of Low-Altitude Drones in the Field of Smart Transport and Logistics PY - 2026 SN - 2052-2576 T2 - Modern management science & engineering. DO - 10.22158/mmse.v8n3p1 UR - https://doi.org/10.22158/mmse.v8n3p1 DA - 2026-06-01 AU - Yuan, Haitao C1 - Liaoning Economy Vocational and Technical College , Shenyang , Liaoning , China KW - Drone KW - Enabling KW - Field (mathematics) KW - Process (computing) KW - Key (lock) KW - Smart city KW - Quality (philosophy) KW - Humanitarian Logistics VL - 8 IS - 3 SP - p1 EP - p1 AB - Against the backdrop of the implementation of low-altitude airspace reforms and the comprehensive upgrading of smart logistics systems, low-altitude UAVs, with their advantages of manoeuvrability, minimal spatial constraints and rapid response times, are gradually being integrated into multiple sectors including urban delivery, traffic inspection, emergency supply support and urban-rural logistics, becoming a key enabler in the construction of an integrated air-ground smart transport and logistics system.Based on the economic environment of the low-altitude sector, this paper systematically analyses the current status of low-altitude UAVs’ practical applications in the field of smart transport and logistics. It objectively identifies the practical shortcomings in the industry’s development process and, by combining policy directions with industry realities, proposes optimisation strategies that are both feasible and implementable. The aim is to provide theoretical references and practical insights for the standardised development of UAV logistics and the enhancement of quality and efficiency in the smart transport industry. ER - TY - JOUR TI - A Garden–Hydrology–UAV Collaborative Infrastructure and Scheduling Framework Under the Low-Altitude Economy PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su18115727 UR - https://doi.org/10.3390/su18115727 DA - 2026-06-04 AU - Guo, Shuyu C1 - College of Economics & Management, Nanjing Tech University, Nanjing 211816, China AU - Chen, Sihan C1 - College of Economics & Management, Nanjing Tech University, Nanjing 211816, China AU - Ma, Shuo C1 - College of Economics & Management, Nanjing Tech University, Nanjing 211816, China AU - Jiang, Zhenbang C1 - College of Environmental Science & Engineering, Nanjing Tech University, Nanjing 211816, China AU - Du, Qiushuang C1 - College of Architecture, Nanjing Tech University, Nanjing 211816, China LA - en KW - Stormwater KW - Stylized fact KW - Green infrastructure KW - Scheduling (production processes) KW - Urban infrastructure KW - Geographic information system KW - Node (physics) VL - 18 IS - 11 SP - 5727 EP - 5727 AB - The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km2 high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. ER - TY - JOUR TI - Deep Reinforcement Learning Framework for Multi-UAV Collision-Free Navigation and Cooperative Control PY - 2026 T2 - Research Square DO - 10.21203/rs.3.rs-8248621/v1 UR - https://doi.org/10.21203/rs.3.rs-8248621/v1 DA - 2026-08-20 AU - Bhookya, Jailsingh C1 - National Institute of Technology Calicut AU - Mondal, Abhishek C1 - National Institute of Technology Calicut AU - Das, Suvam C1 - National Institute of Technology Calicut KW - Reinforcement learning KW - Software deployment KW - Scalability KW - Control (management) KW - Drone KW - Deep learning KW - Intelligent transportation system KW - Trajectory ER - TY - GEN TI - A Ground Mounted UAV Sensing System for UAM Air Traffic Control Management PY - 2024 DO - 10.2514/6.2024-4362 UR - https://doi.org/10.2514/6.2024-4362 DA - 2024-07-27 AU - Rathour, Swarn C1 - Kabushiki Kaisha Hitachi Seisakusho AU - Pandey, Jyotsna C1 - Kabushiki Kaisha Hitachi Seisakusho AU - Ito, Takahiro C1 - Kabushiki Kaisha Hitachi Seisakusho AU - Kimiyoshi, Machii C1 - Kabushiki Kaisha Hitachi Seisakusho AU - Shimizu, Taku C1 - Kabushiki Kaisha Hitachi Seisakusho AU - BANDO, Mikio C1 - Kabushiki Kaisha Hitachi Seisakusho LA - en KW - Air traffic control KW - Computer science KW - Remote sensing KW - Real-time computing KW - Aeronautics KW - Environmental science KW - Aerospace engineering KW - Engineering KW - Geology ER - TY - GEN TI - Bi-objective UAV Route Planning for Medicine Delivery: Balancing Profit and Risk PY - 2026 DO - 10.1109/isbdas69350.2026.11484384 UR - https://doi.org/10.1109/isbdas69350.2026.11484384 DA - 2026-03-06 AU - Li, Zeyang C1 - Shanghai University,SILC Business School,Shanghai,China AU - Sun, Xuting C1 - Shanghai University,SILC Business School,Shanghai,China KW - Route planning KW - Profit (economics) KW - Operation planning KW - Data collection SP - 426 EP - 430 ER - TY - GEN TI - Low-Noise Route and Operations Planning for Food Delivery Unmanned Aerial Vehicles PY - 2025 DO - 10.2514/6.2025-3303 UR - https://doi.org/10.2514/6.2025-3303 DA - 2025-07-16 AU - Dulia, Esrat C1 - Kent State University AU - Shihab, Syed C1 - Kent State University AU - Adams, Caleb C1 - Kent State University AU - Burnett, Jordan C1 - Case Western Reserve University LA - en KW - Computer science KW - Noise (video) KW - Route planning KW - Motion planning KW - Transport engineering KW - Engineering KW - Robot KW - Artificial intelligence ER - TY - GEN TI - Research on delivery route optimization for unmanned aerial vehicle logistics (UAL) based on wireless networks PY - 2026 DO - 10.1117/12.3110849 UR - https://doi.org/10.1117/12.3110849 DA - 2026-04-24 AU - Feng, Yingjian C1 - Yantai Automobile Engineering Professional College (China) AU - Song, Yingfei C1 - Yantai Automobile Engineering Professional College (China) AU - Jiao, Shuang C1 - Yantai Automobile Engineering Professional College (China) AU - Li, Yue C1 - Yantai Automobile Engineering Professional College (China) AU - Kong, Qingrong C1 - Yantai Automobile Engineering Professional College (China) AU - Yang, Yukang C1 - Yantai Academy of Agricultural Sciences (China) KW - Reliability (semiconductor) KW - Obstacle KW - Base station KW - Key (lock) KW - Greedy algorithm KW - Wireless KW - Telecommunications network KW - Wireless network SP - 54 EP - 54 ER - TY - JOUR TI - Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2506.14857 UR - https://doi.org/10.48550/arxiv.2506.14857 DA - 2025-06-17 AU - Raj, Suman AU - Padhi, Swapnil AU - Bhoot, Ruchi AU - Modi, Payal AU - Simmhan, Yogesh LA - en KW - Drone KW - Motion planning KW - Obstacle avoidance KW - Collision avoidance KW - Global Positioning System KW - Obstacle KW - Planner KW - Visually impaired KW - Path (computing) AB - Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logistics, and disaster management. In this paper, we examine the use of drones for assisting visually impaired people (VIPs) in navigating through outdoor urban environments. Specifically, we present a perception-based path planning system for local planning around the neighborhood of the VIP, integrated with a global planner based on GPS and maps for coarse planning. We represent the problem using a geometric formulation and propose a multi DNN based framework for obstacle avoidance of the UAV as well as the VIP. Our evaluations conducted on a drone human system in a university campus environment verifies the feasibility of our algorithms in three scenarios; when the VIP walks on a footpath, near parked vehicles, and in a crowded street. ER - TY - CHAP TI - Challenges of Civil Application of 'Drones' for Commercial Purposes in the Logistics and Transportation Sector PY - 2024 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.11396768 UR - https://doi.org/10.5281/zenodo.11396768 DA - 2024-04-19 AU - Raya, Antonio AU - Esteve, Joaquín LA - en KW - Drone KW - Obstacle KW - Spare part KW - Government (linguistics) KW - Emerging technologies KW - Point (geometry) KW - Aviation ER - TY - JOUR TI - Advances in truck–drone cooperative delivery routing and coordination: a review PY - 2026 PB - Springer Science+Business Media SN - 1866-8887 T2 - European Transport Research Review DO - 10.1186/s12544-026-00808-2 UR - https://doi.org/10.1186/s12544-026-00808-2 DA - 2026-07-10 AU - Liu, Yang C1 - School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China AU - Fei, Yi C1 - School of Software, Henan University of Engineering, Zhengzhou, 451191, China AU - Ma, Changxi C1 - School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China LA - en KW - Routing (electronic design automation) KW - Key (lock) KW - Scheduling (production processes) KW - Production (economics) VL - 18 IS - 1 AB - Unmanned Aerial Vehicle (UAV)–vehicle cooperative delivery has emerged as a promising paradigm for addressing the growing complexity of urban and regional logistics. By combining the capacity and network coverage of ground vehicles with the flexibility and accessibility of UAVs, such systems offer new opportunities to improve delivery efficiency, responsiveness, and sustainability across diverse operational contexts. In recent years, research in this field has expanded rapidly, producing a wide range of models, algorithms, and application-driven studies. This paper provides a comprehensive review of UAV–vehicle cooperative delivery research, synthesizing advances in system configurations, coordination mechanisms, modeling approaches, and solution methodologies, including optimization-based, heuristic, hybrid, and learning-driven frameworks. Empirical evidence from major application domains, such as urban parcel logistics, emergency and humanitarian operations, infrastructure inspection, and on-demand services, is examined to reveal how system design and operational conditions jointly shape performance. Beyond summarizing existing work, this review identifies fundamental modeling, operational, and sustainability challenges that continue to hinder large-scale deployment, including synchronization fragility, energy and uncertainty management, regulatory constraints, and scalability. By consolidating fragmented research strands into a unified analytical perspective, this review aims to support the transition of UAV–vehicle cooperative delivery from isolated pilot studies toward robust, scalable, and policy-relevant transportation systems. ER - TY - THES TI - Drone For Urban Last-mile Delivery PY - 2025 PB - KTH Royal Institute of Technology T2 - KTH Publication Database DiVA (KTH Royal Institute of Technology) DA - 2025-01-01 AU - Sharif, Hussein C1 - Department of Computer and Systems Sciences ; LA - en KW - Drone KW - Payload (computing) KW - Modular design KW - Artifact (error) KW - Work (physics) KW - Conceptual framework KW - Conceptual model KW - Last mile (transportation) KW - Cover (algebra) AB - Introduction This thesis explores how a modular conceptual framework can support UAV-based last-mile delivery for logistics SMEs operating in regulated urban environments. As urban traffic and environmental demands increase, drones are emerging as a viable alternative for small-package logistics, yet technical, regulatory, and operational barriers hinder their widespread adoption, particularly among small and mid-sized operators. Research Question How can a cost-effective, regulation-compliant, and operationally feasible drone delivery model be designed to support small package last- mile delivery in urban environments? Method To investigate this, the study applied a Design Science Research (DSR) methodology. The artifact was developed through iterative synthesis of academic literature,regulatory protocols (notably EASAs SORA) and industry benchmarks (Zipline, Wing,Amazon).Evaluation involved benchmarking, regulatory fit assessment, and a GIS-based scenario simulation using real urban data from Stockholm. Results The results show that the proposed framework meets core functional and regulatory requirements for SME deployment. With a 5 kg payload capacity, a 10–20 km range, and integration capabilities with ERP and routing systems, the system was shown to cover much of Stockholm’s delivery needs from just three hubs, while maintaining compliance with BVLOS and no-fly–zone restrictions. Discussion These findings suggest that modular, regulation ready UAV systems could be realistically implemented by SMEs under EU drone policy. While the framework has not been field-tested, its theoretical validation indicates strong potential for scalable, sustainable urban drone delivery. Future work should focus on empirical trials, system integration with live logistics networks, and public acceptance modeling. ER - TY - GEN TI - Energy-Efficient UAV Delivery Optimization via Clustering and Layered Path Planning PY - 2026 DO - 10.1109/iraset68627.2026.11538454 UR - https://doi.org/10.1109/iraset68627.2026.11538454 DA - 2026-05-14 AU - Li, Zhiyuan C1 - The Hong Kong Polytechnic University,Dept. of Industrial System Engineering,Hong Kong,SAR,China AU - Zhou, Shiran C1 - The Hong Kong Polytechnic University,Dept. of Logistics and Maritime Studies,Hong Kong,SAR,China AU - Al-Musallami, Nasser C1 - Dalhousie University,Dept. of Ind. Eng,Halifax,NS,Canada AU - Khatab, Abdelhakim C1 - Lorraine University,UFR MIM (LGIPM),Metz,France AU - Diallo, Claver C1 - Dalhousie University,Dept. of Ind. Eng,Halifax,NS,Canada KW - Cluster analysis KW - Motion planning KW - Path (computing) KW - Fuzzy clustering KW - Trajectory KW - Minification SP - 1 EP - 7 ER - TY - GEN TI - A review of autonomous obstacle avoidance and path planning technology for micro-UAVs in complex environments PY - 2026 DO - 10.1109/icsi70302.2026.11544395 UR - https://doi.org/10.1109/icsi70302.2026.11544395 DA - 2026-04-24 AU - Wang, Xunyao C1 - Civil Aviation University of China,Tianjin,P. R. China,300300 KW - Obstacle avoidance KW - Motion planning KW - Path (computing) KW - Obstacle KW - Key (lock) KW - Collision avoidance KW - Control (management) SP - 519 EP - 523 ER - TY - JOUR TI - Liquid Neural Network-Based Multi-UAV PathPlanning in Dynamic, Obstacle-AwareEnvironments PY - 2026 T2 - Research Square DO - 10.21203/rs.3.rs-8834329/v1 UR - https://doi.org/10.21203/rs.3.rs-8834329/v1 DA - 2026-03-05 AU - Güzey, Alparslan C1 - Atam Robotic and Software Technology AU - Çifçi, Mehmet C1 - Duy Tan University KW - Robustness (evolution) KW - Yaw KW - Benchmark (surveying) KW - Artificial neural network KW - Motion planning KW - Vehicle dynamics KW - Recurrent neural network KW - Control theory (sociology) ER - TY - JOUR TI - Pickup and Delivery in Two-Echelon UGV-UAV Networks: A Location-Routing Problem PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7349897 UR - https://doi.org/10.2139/ssrn.7349897 DA - 2026-01-01 AU - Zhang, Wenwei AU - Shi, Jialu AU - Wei, Zhengyang AU - Shan, Hongmei AU - Zhang, Lele C1 - University of Melbourne, Carlton, Parkville, 3010, Australia AU - Li, Pengfei KW - Agile software development KW - Leverage (statistics) KW - Pickup KW - Robustness (evolution) KW - Mode (computer interface) KW - Economies of scale KW - Computation KW - Outsourcing ER - TY - JOUR TI - A novel hybrid framework to optimize UAVs selection paths using LAP and RH trajectory planning PY - 2025 PB - Springer Nature SN - 2511-2104 T2 - International Journal of Information Technology DO - 10.1007/s41870-025-02921-2 UR - https://doi.org/10.1007/s41870-025-02921-2 DA - 2025-11-21 AU - Sumati, Sumati C1 - Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India AU - Marriwala, Nikhil C1 - Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India AU - Jaswal, Ram C1 - Department of Electrical Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India LA - en KW - Trajectory KW - Adaptability KW - Motion planning KW - Consistency (knowledge bases) KW - Path (computing) KW - Trajectory optimization KW - Unification KW - Planner KW - Time horizon ER - TY - CHAP TI - Drone Technology in Industry 4.0 PY - 2025 DO - 10.1201/9781003511298-10 UR - https://doi.org/10.1201/9781003511298-10 DA - 2025-01-02 AU - Kuljanin, Jovana AU - Zivojinović, Tanja AU - Bojković, Nataša AU - Barrado, Cristina LA - en KW - Drone KW - Leverage (statistics) KW - Engineering KW - Computer security KW - Business KW - Computer science SP - 180 EP - 220 ER - TY - JOUR TI - Cooperative Control of Unmanned Aerial Vehicle (UAV) Swarms and Swarm Intelligence PY - 2025 SN - 2755-2721 T2 - Applied and Computational Engineering DO - 10.54254/2755-2721/2025.ld28117 UR - https://doi.org/10.54254/2755-2721/2025.ld28117 DA - 2025-10-22 AU - Huang, Ziyuan C1 - Jiangxi Science and Technology University High School, Nanchang, China KW - Drone KW - Swarm behaviour KW - Obstacle KW - Task (project management) KW - Field (mathematics) KW - Adaptation (eye) KW - Swarm intelligence KW - Control (management) VL - 191 IS - 1 SP - 243 EP - 248 AB - The rapid advancement of drone swarm technology offers immense potential in areas such as disaster relief and anti-flood efforts. However, in some cases, they still have many problems, for example, unexpected emergent behaviours, difficulty in chronic communication and cybersecurity problems. This paper aims to improve the gait and efficiency of cooperative drone swarms by developing an integrated framework that combines hybrid A*-based path planning, multiple-mode communication architectures, and a fault-tolerant control system. Besides, the paper also applies the energy-aware heuristics, dynamic obstacle prediction and decentralised consensus algorithms for real-time adaptation and strong coordination. The results show remarkable improvement in formation stability, enabling the task to be completed and work appropriately in a dynamic environment. In real-life situations, the paper can utilise this app in earthquake rescue missions and urban logistics organisations to demonstrate the system's expandability and adaptability. This research advances the field of drone swarm technology, utilising both civilian and defence applications. The paper also explores the ethics and sustainable design implications for future advancements. ER - TY - JOUR TI - Radar-based interactive multi-model multi-target tracking algorithm for UAV swarms PY - 2026 PB - EDP Sciences SN - 2107-6839 T2 - International Journal of Metrology and Quality Engineering DO - 10.1051/ijmqe/2026002 UR - https://doi.org/10.1051/ijmqe/2026002 DA - 2026-01-01 AU - Ren, Jianwei C1 - Guangxi Beibu Gulf Investment Group Co., Ltd AU - Wang, Hua C1 - Guangxi Transportation Science and Technology Group Co., Ltd AU - Rong, Hao C1 - Guangxi Transportation Science and Technology Group Co., Ltd AU - Wei, Zexian C1 - Guangxi Transportation Science and Technology Group Co., Ltd AU - Lv, Peixia C1 - Guangxi Transportation Science and Technology Group Co., Ltd AU - Li, Xiuqiong C1 - Guangxi Transportation Science and Technology Group Co., Ltd AU - Bai, Mou C1 - Guangxi Transportation Science and Technology Group Co., Ltd KW - Clutter KW - Thresholding KW - Tracking (education) KW - Constant false alarm rate KW - Cluster analysis KW - Computation KW - Point cloud KW - Mean squared error VL - 17 SP - 5 EP - 5 AB - With the gradual opening of low-altitude airspace and the rapid development of UAV technology, large-scale UAV swarms are increasingly used in logistics, inspection, and security scenarios. To achieve accurate and robust tracking of highly dynamic, high-density, and strongly interactive UAV swarms, this study proposes a radar-based interactive multi-model multi-target tracking algorithm. Point cloud quality is improved by integrating velocity vector density clustering with adaptive constant false alarm rate detection. An interactive multi-model framework incorporating uniform velocity, uniform acceleration, and coordinated turning modes is established, together with a group potential field–driven state transition mechanism. Adaptive thresholding and dynamic track splitting and merging strategies are further introduced to enhance tracking stability. Experimental results show that the proposed method achieves an average distance RMSE of 1.47 m and a speed RMSE of 1.18 m/s, representing reductions of 49.8% and 42.3% compared with the traditional joint probability data association algorithm. The average tracking accuracy reaches 88.19% and remains 84.31% under a clutter density of 80 points/scan, while the average computation time is 36.92 ms, satisfying real-time requirements. The results demonstrate improved accuracy and stability for low-altitude UAV surveillance in high-density urban scenarios. ER - TY - JOUR TI - Design And Evaluation of a Lightweight Multi-Sensor Fusion Visual Navigation System for Uavs PY - 2026 SN - 3006-0842 T2 - Mathematical Modeling and Algorithm Application DO - 10.54097/acsmyx32 UR - https://doi.org/10.54097/acsmyx32 DA - 2026-03-15 AU - Song, Yunyi C1 - Department of Air Traffic Management, Civil Aviation Flight University of China,Deyang , 618000, China AU - Sun, Xuyang C1 - Department of Houston International Institute, Dalian Maritime University, Dalian, 116000, China KW - Software deployment KW - Power consumption KW - Trajectory KW - Resource (disambiguation) KW - Line (geometry) KW - Resource consumption KW - Sensitivity (control systems) KW - Odometry KW - Approximation error VL - 9 IS - 1 SP - 206 EP - 213 AB - With the development of visual–inertial navigation technology, this paper evaluates the deployment performance of visual–inertial odometry (VIO) algorithms on lightweight UAV platforms. Three representative algorithms, MSCKF, VINS-Mono, and OpenVINS, are selected and tested on the EuRoC MH_01_easy dataset under a same virtual platform (ROS Noetic + Ubuntu 20.04, 4-core CPU, 8GB RAM). Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) are used as the main evaluation metrics. The results show that VINS-Mono achieves the highest accuracy, with a mean ATE of 0.078 m and RPE ranging from 0.18 to 0.33 m. MSCKF presents slightly lower accuracy (mean ATE of 0.090 m) but has the lowest CPU and memory usage with strong stability. OpenVINS reaches accuracy close to VINS-Mono (mean ATE of 0.080 m), with moderate resource consumption but higher sensitivity during initialization. Resource monitoring shows that the computational cost of VINS-Mono is about 1.8 times that of MSCKF. These findings highlight the trade-offs among accuracy, computational efficiency, and deployment adaptability, providing practical references for low-altitude applications such as urban logistics and power line inspection. ER - TY - JOUR TI - FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2505.12835 UR - https://doi.org/10.48550/arxiv.2505.12835 DA - 2025-05-19 AU - Cai, Hengxing AU - Dong, Jinhan AU - Tan, Jingjun AU - Deng, Jingcheng AU - Li, Sihang AU - Gao, Zhifeng AU - Wang, Haidong AU - Su, Zicheng AU - Sumalee, Agachai AU - Zhong, Renxin LA - en KW - Generalization KW - Baseline (sea) KW - Initialization KW - Perception KW - Training (meteorology) KW - Field (mathematics) AB - Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. To address these challenges, we propose FlightGPT, a novel UAV VLN framework built upon Vision-Language Models (VLMs) with powerful multimodal perception capabilities. We design a two-stage training pipeline: first, Supervised Fine-Tuning (SFT) using high-quality demonstrations to improve initialization and structured reasoning; then, Group Relative Policy Optimization (GRPO) algorithm, guided by a composite reward that considers goal accuracy, reasoning quality, and format compliance, to enhance generalization and adaptability. Furthermore, FlightGPT introduces a Chain-of-Thought (CoT)-based reasoning mechanism to improve decision interpretability. Extensive experiments on the city-scale dataset CityNav demonstrate that FlightGPT achieves state-of-the-art performance across all scenarios, with a 9.22\% higher success rate than the strongest baseline in unseen environments. Our implementation is publicly available. ER - TY - JOUR TI - Radar-based interactive multi-model multi-target tracking algorithm for UAV swarms PY - 2026 PB - Chiba Institute of Technology T2 - Springer Link (Chiba Institute of Technology) DO - 10.1051/ijmqe/2026002/pdf UR - https://doi.org/10.1051/ijmqe/2026002/pdf DA - 2026-04-03 AU - Ren, Jianwei C1 - Guangxi Beibu Gulf Investment Group Co., Ltd , Nanning , 530029 , PR China AU - Wang, Hua C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China AU - Rong, Hao C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China AU - Wei, Zexian C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China AU - Lv, Peixia C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China AU - Li, Xiuqiong C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China AU - Bai, Mou C1 - Guangxi Transportation Science and Technology Group Co., Ltd , Nanning , 530007 , PR China LA - en KW - Clutter KW - Thresholding KW - Tracking (education) KW - Cluster analysis KW - Constant false alarm rate KW - Computation KW - Mean squared error KW - Trajectory AB - With the gradual opening of low-altitude airspace and the rapid development of UAV technology, large-scale UAV swarms are increasingly used in logistics, inspection, and security scenarios. To achieve accurate and robust tracking of highly dynamic, high-density, and strongly interactive UAV swarms, this study proposes a radar-based interactive multi-model multi-target tracking algorithm. Point cloud quality is improved by integrating velocity vector density clustering with adaptive constant false alarm rate detection. An interactive multi-model framework incorporating uniform velocity, uniform acceleration, and coordinated turning modes is established, together with a group potential field–driven state transition mechanism. Adaptive thresholding and dynamic track splitting and merging strategies are further introduced to enhance tracking stability. Experimental results show that the proposed method achieves an average distance RMSE of 1.47 m and a speed RMSE of 1.18 m/s, representing reductions of 49.8% and 42.3% compared with the traditional joint probability data association algorithm. The average tracking accuracy reaches 88.19% and remains 84.31% under a clutter density of 80 points/scan, while the average computation time is 36.92 ms, satisfying real-time requirements. The results demonstrate improved accuracy and stability for low-altitude UAV surveillance in high-density urban scenarios. ER - TY - GEN TI - FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models PY - 2025 T2 - Underline Science Inc. DO - 10.48448/f3hc-8h35 UR - https://doi.org/10.48448/f3hc-8h35 DA - 2025-10-10 AU - 2025, Association AU - Cai, Hengxing AU - Deng, Jingcheng C1 - Southwest University AU - Dong, Jinhan C1 - Beijing University Of Posts and Telecommunications AU - Gao, Zhifeng C1 - DP Technology AU - Li, Sihang C1 - University of Science and Technology of China, Hefei, China AU - Su, Zicheng C1 - Tongji University AU - Sumalee, Agachai C1 - School of Integrated Innovation, Chulalongkorn University, Bangkok, Thailand AU - Tan, Jingjun C1 - School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China AU - Wang, Haidong C1 - School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China AU - Zhong, Renxin C1 - School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China KW - Generalization KW - Baseline (sea) KW - Initialization KW - Perception KW - Training (meteorology) KW - Field (mathematics) AB - Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. To address these challenges, we propose FlightGPT, a novel UAV VLN framework built upon Vision-Language Models (VLMs) with powerful multimodal perception capabilities. We design a two-stage training pipeline: first, Supervised Fine-Tuning (SFT) using high-quality demonstrations to improve initialization and structured reasoning; then, Group Relative Policy Optimization (GRPO) algorithm, guided by a composite reward that considers goal accuracy, reasoning quality, and format compliance, to enhance generalization and adaptability. Furthermore, FlightGPT introduces a Chain-of-Thought (CoT)-based reasoning mechanism to improve decision interpretability. Extensive experiments on the city-scale dataset CityNav demonstrate that FlightGPT achieves state-of-the-art performance across all scenarios, with a 9.22\% higher success rate than the strongest baseline in unseen environments. Our implementation is publicly available. ER - TY - GEN TI - Applications and Assessments of Uncrewed Aerial Vehicles for Dual-Use Public Good and Military Missions PY - 2026 DO - 10.4050/sm-2026-vlada-5184 UR - https://doi.org/10.4050/sm-2026-vlada-5184 DA - 2026-01-27 AU - Scott, Robert C1 - VFS Member AU - Conley, Sarah C1 - NASA Ames Research Center KW - Drone KW - Aerial survey KW - Search and rescue KW - Public transport KW - Emergency response KW - Emergency management SP - 1 EP - 13 ER - TY - JOUR TI - Coordinating Rail-Transit Freight and UAV Last-Mile Delivery under Train-Arrival Time-Base Constraints PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7075420 UR - https://doi.org/10.2139/ssrn.7075420 DA - 2026-01-01 AU - Fan, Liuying AU - ZHAO, Peng AU - Li, Dewei AU - Yin, Yonghao AU - Duolong, Wang KW - Payload (computing) KW - Column generation KW - Routing (electronic design automation) KW - Transit (satellite) KW - Task (project management) KW - Scale (ratio) KW - Resource (disambiguation) KW - Vehicle routing problem KW - Quality (philosophy) ER - TY - GEN TI - Towards a Mobile App Platform for Personalized UAV Fleets using Edge and Cloud PY - 2023 DO - 10.1109/ccgridw59191.2023.00075 UR - https://doi.org/10.1109/ccgridw59191.2023.00075 DA - 2023-05-01 AU - Raj, Suman C1 - Indian Institute of Science,Department of Computational and Data Sciences,Bangalore,INDIA,560012 AU - Simmhan, Yogesh C1 - Indian Institute of Science,Department of Computational and Data Sciences,Bangalore,INDIA,560012 LA - en KW - Drone KW - Cloud computing KW - Computer science KW - Enhanced Data Rates for GSM Evolution KW - Edge computing KW - Middleware (distributed applications) KW - Mobile apps KW - Embedded system KW - Mobile device KW - Operating system KW - Real-time computing KW - Artificial intelligence KW - World Wide Web SP - 331 EP - 333 ER - TY - GEN TI - An Empirical Study on Genetic Algorithm for 2D Path Planning of Unmanned Aerial Vehicles with Obstacle Avoidance PY - 2023 DO - 10.1145/3627341.3630400 UR - https://doi.org/10.1145/3627341.3630400 DA - 2023-08-25 AU - Zhao, Ya-Hong C1 - School of Computer and Artificial Intelligence, Xiangnan University, China and Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, China AU - Lan, Yulin C1 - School of Computer and Artificial Intelligence, Xiangnan University, China and Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, China AU - Li, Shang-Lin C1 - School of Computer and Artificial Intelligence, Xiangnan University, China and Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, China AU - Zhang, Wenfen C1 - School of Computer and Artificial Intelligence, Xiangnan University, China and Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, China LA - en KW - Crossover KW - Obstacle avoidance KW - Genetic algorithm KW - Motion planning KW - Obstacle KW - Computer science KW - Context (archaeology) KW - Path (computing) KW - Trajectory KW - Mathematical optimization KW - Population KW - Empirical research KW - Artificial intelligence KW - Machine learning KW - Mobile robot KW - Mathematics KW - Geography KW - Robot SP - 1 EP - 6 AB - Unmanned aerial vehicle (UAV), with high efficiency, low manpower costs, and flexible convenience, has attracted widespread attention in logistics. UAV trajectory planning is an essential component in this context. Genetic algorithm(GA) is an effective tool for solving complex route optimization problems. However, selecting the optimal combination of parameters for UAV path planning remains a challenging problem, especially in complex urban environments with numerous obstacles. This paper proposes a genetic algorithm-based UAV path planning approach(UAV_GA) that considers obstacle avoidance on a two-dimensional grid-based map. Furthermore, to investigate the general patterns in selecting parameters for GA in such problems, a comprehensive empirical study is conducted. The study analyzes the effects of population size, number of iterations, crossover, and mutation probabilities on algorithm performance. It aims to identify universal patterns for optimal parameter combinations and provide guidance for parameter selection in GA for UAV trajectory planning with obstacle avoidance. ER - TY - GEN TI - Research on UAV route planning strategy under the background of low-altitude economy PY - 2025 DO - 10.1109/girst67753.2025.11382148 UR - https://doi.org/10.1109/girst67753.2025.11382148 DA - 2025-11-27 AU - Yu, Lin C1 - Power China Chengdu Engineering Corporation Limited,Chengdu,China AU - Wu, Qifan C1 - Power China Chengdu Engineering Corporation Limited,Chengdu,China KW - Obstacle KW - Key (lock) KW - Obstacle avoidance KW - Motion planning KW - Plan (archaeology) KW - Reliability (semiconductor) KW - Global Positioning System KW - Path (computing) KW - Route planning SP - 173 EP - 177 ER - TY - GEN TI - Deep Reinforcement Learning Unmanned Aerial Vehicle Autonomous Cruise System with Fusion of Visual Information PY - 2023 DO - 10.1145/3660043.3660158 UR - https://doi.org/10.1145/3660043.3660158 DA - 2023-12-22 AU - Liu, Rui C1 - School of Automation, Wuhan University of Technology, China AU - Shi, Ying C1 - School of Automation, Wuhan University of Technology, China AU - Xie, Yufei C1 - School of Automation, Wuhan University of Technology, China LA - en KW - Computer science KW - Reinforcement learning KW - Coding (social sciences) KW - Process (computing) KW - Drone KW - Real-time computing KW - Cruise control KW - Obstacle avoidance KW - Obstacle KW - Cruise KW - Simulation KW - Artificial intelligence KW - Mobile robot KW - Control (management) KW - Engineering KW - Robot SP - 639 EP - 645 AB - UAV have a wide range of applications in military, aviation, agriculture, logistics, and other fields. However, when conducting real drone testing, there are high costs, safety risks, and environmental impacts.In this case, the virtual simulation scheme of UAV automatic cruise based on Airsim in Unreal urban environment proposed in this article can provide an economical and reliable solution, which has broad application prospects in the field of UAV research and development.This scheme utilizes the open-source cross platform simulator provided by Airsim to simulate real urban environmental scene information. By inputting UAV parameters and actual environmental data into the simulation system, it can simulate the real UAV flight and control process, effectively test and verify the performance and algorithm of the UAV.Meanwhile, virtual simulation can also easily replicate and adjust experimental results, improving testing efficiency and data reliability.The advantages of this paper are as follows: 1. Visual information coding strategy is integrated into deep reinforcement learning algorithm, which solves the defects of traditional algorithm in processing visual information to a certain extent. 2. A multi-information fusion Reward function based on distance reward, position-angle-deviation punishment and obstacle avoidance items is designed and introduced, which can make UAV adapt to more complex and changeable physical environment. ER - TY - GEN TI - Truck-UAV Joint Distribution Route Optimization Model with Dynamic Recycling in New Retail Scenarios PY - 2026 PB - Springer Science+Business Media SN - 1865-0929 T2 - Communications in computer and information science DO - 10.1007/978-3-032-15579-5_13 UR - https://doi.org/10.1007/978-3-032-15579-5_13 DA - 2026-01-01 AU - Wang, Yiting C1 - Xi’an University of Technology, Xi’an, China AU - Wang, Xinran C1 - Xi’an University of Technology, Xi’an, China AU - Guo, Haixiao C1 - Xi’an University of Technology, Xi’an, China AU - Bian, Jiaxin C1 - Xi’an University of Technology, Xi’an, China AU - Ma, Xiaohui C1 - Xi’an University of Technology, Xi’an, China AU - Wang, Xinmeng C1 - Xi’an University of Technology, Xi’an, China LA - en KW - Truck KW - Heuristic KW - Scheduling (production processes) KW - Joint (building) KW - Mode (computer interface) KW - Core (optical fiber) KW - Traffic congestion KW - Job shop scheduling SP - 207 EP - 223 ER - TY - JOUR TI - Zero emission freight transport and impacts on last mile delivery PY - 2024 SN - 0359-9345 T2 - Liikenne-vuosikirja DO - 10.58956/liikenne.143000 UR - https://doi.org/10.58956/liikenne.143000 DA - 2024-02-09 AU - Babjak, Samuel C1 - Aalto University AU - Yung, Chirs C1 - Aalto University LA - en KW - Mile KW - Last mile (transportation) KW - Zero (linguistics) KW - Zero emission KW - Business KW - Transport engineering KW - Environmental science KW - Engineering KW - Geography KW - Waste management KW - Geodesy VL - 6 AB - This literature review explores mainly the challenges and potential solutions in urban freight transportation, for last-mile logistics. The growing number of delivery vehicles in urban areas has led to problems such as traffic congestion, competition for parking spaces, noise, and air pollution. To address these issues, the paper focuses on the last mile problem in transportation logistics and the potential of disruptive innovations, a potential of disruptive innovations such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs) as alternatives to traditional Internal Combustion Engine (ICE) vehicles. It highlights the drastic operational differences between aerial drones and ADRs and the need for changes in operational procedures to fully leverage these technologies. The study also discusses routing strategies and delivery routes, emphasizing the shift from one-route delivery to a point-to- point model with the advent of autonomous vehicle technologies. The paper concludes that while these technologies can reduce costs and emissions, their effectiveness depends on the specific urban setting and operational factors. ER - TY - JOUR TI - Optimal Cruise Speeds and Environmental Effects on Energy Consumption of Multirotor UAVs for Last-Mile Delivery PY - 2026 T2 - Preprints.org DO - 10.20944/preprints202604.0751.v1 UR - https://doi.org/10.20944/preprints202604.0751.v1 DA - 2026-04-10 AU - Choi, Sung-Hyuk C1 - School of Engineering and Materials Science , Queen Mary University of London , London E1 4NS , UK; KW - Multirotor KW - Cruise KW - Energy consumption KW - Payload (computing) KW - Telemetry KW - Range (aeronautics) KW - Energy (signal processing) KW - Fuel efficiency AB - Unmanned aerial vehicles (UAVs) are increasingly recognized as a viable option for urban parcel delivery. However, their energy performance under varying environmental and mission conditions remains underexplored. This paper presents a simulation-based analysis of multirotor UAV energy consumption using the PX4-Gazebo platform, calibrated with real-world telemetry from a publicly available DJI Matrice 100 dataset [12]. Three UAV models—Iris, Typhoon H480, and Octocopter—were evaluated across a range of payloads (0.1–5 kg), cruise speeds (2–16 m/s), and environmental factors, including wind, temperature, and humidity. Results revealed consistent U-shaped energy speed curves, with optimal cruise speeds ranging from 8 to 10 m/s, depending on the payload and platform. Headwinds alone increased energy consumption by up to 25% and combined cold–dry and headwind conditions resulted in increases of up to 53% for lightweight platforms. Validation against field telemetry showed mean absolute percentage errors below 11%. These findings offer a simulation-grounded framework for UAV mission planning, platform selection, and integration into energy-efficient logistics networks, and development of data-driven optimization frameworks. The three platforms span a 10-fold mass range (1.4–14 kg), enabling systematic analysis of how energy scaling behavior varies across lightweight, mid-range, and heavy-lift delivery configurations. ER - TY - JOUR TI - Reinforcement Learning for Autonomous Drone Navigation PY - 2023 SN - 2454-308X T2 - Innovative Research Thoughts DO - 10.36676/irt.2023-v9i5-002 UR - https://doi.org/10.36676/irt.2023-v9i5-002 DA - 2023-10-14 AU - Gupta, Myra LA - en KW - Drone KW - Computer science KW - Visibility KW - Reinforcement learning KW - Law enforcement KW - Process (computing) KW - Component (thermodynamics) KW - Software KW - Real-time computing KW - Aeronautics KW - Computer security KW - Artificial intelligence KW - Human–computer interaction KW - Engineering KW - Geography VL - 9 IS - 5 SP - 11 EP - 20 AB - Drone navigation involves the process of controlling the movement and flight path of unmanned aerial vehicles (UAVs). It encompasses both the hardware and software systems that enable drones to navigate and maneuver autonomously or under the guidance of a human operator. The utility of drone navigation is vast and varied, making it a critical component in numerous industries and applications. Firstly, drone navigation plays a crucial role in aerial surveillance and reconnaissance. Drones equipped with advanced navigation systems can efficiently patrol large areas, monitor activities, and gather real-time data from various perspectives. This capability is particularly valuable in security and law enforcement operations, disaster response, and environmental monitoring, where access and visibility might be limited.Drone navigation is critical in aerial mapping and surveying. Drones outfitted with GPS and other positioning technologies may record photographs and collect data with pinpoint accuracy, allowing for the creation of comprehensive 3D maps, topographic models, and land surveys. This allows for more precise, quicker, and cost-effective data collecting for urban planning, agricultural, infrastructure inspection, and building projects. Furthermore, drone navigation is critical in the transportation and logistics industries. Autonomous drones can fly predetermined paths to rapidly and effectively transfer commodities, medical supplies, and other payloads to distant or inaccessible regions. This technology has the potential to transform last-mile distribution, especially in rural areas or during emergencies when regular transportation routes may be hampered. ER - TY - JOUR TI - Beyond the Last Mile: A Systematic Review Exploring Indoor Delivery-UAV Requirements in the Last-Meter Context PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2071-1050 T2 - Sustainability DO - 10.3390/su18136728 UR - https://doi.org/10.3390/su18136728 DA - 2026-07-02 AU - Li, Y C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong AU - Ng, S C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong AU - Ling, Mingzhuo C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong AU - Pan, Qi C1 - Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong C1 - Research Center for Underground Space & Department of Geotechnical Engineering, Tongji University, Shanghai 200000, China LA - en KW - Framing (construction) KW - USable KW - Context (archaeology) KW - Cognitive reframing KW - Architecture KW - Usability KW - Set (abstract data type) KW - Rendering (computer graphics) VL - 18 IS - 13 SP - 6728 EP - 6728 AB - The final stage of urban logistics does not end at the building entrance but continues within complex, vertically structured indoor environments, where conventional ground-based delivery systems face limitations in efficiency, flexibility, and scalability. This study introduces the concept of last-meter delivery, defined as unmanned aerial vehicle (UAV)-enabled transport from the building envelope to the recipient within global navigation satellite system (GNSS)-denied, building-regulated indoor space, and systematically reviews the literature from two traditionally separate domains: indoor-UAV operation in GNSS-denied spaces, and outdoor-UAV-based logistics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 297 studies are synthesized through a two-stream thematic synthesis. The review makes three contributions. First, a unified analytical framework is developed across four dimensions (spatial mobility, logistical capability, social acceptance, and operational coordination) through which the two bodies of literature are shown to be largely complementary, with the gaps in one stream coinciding with the strengths of the other. Second, indoor aerial delivery is found to be subject to a distinct set of operational constraints, including micro-scale navigation accuracy, strict geometric safety envelopes, close human–UAV interaction, and privacy sensitivity, implying that indoor transport-UAVs cannot be realized through simple miniaturization of outdoor platforms but require precision-oriented, human-centric, and building-aware design. Third, the four dimensions are translated into a building-management-oriented indicator framework covering spatial compliance, handover standardization, building information modeling (BIM) integration, occupant consent, and liability allocation, reframing last-meter requirements in terms that are actionable for building planners and facility managers. By framing these challenges within the last-meter perspective, this review identifies the gap between current last-mile theories and emerging in-building aerial logistics and provides a structured foundation for future research. ER - TY - JOUR TI - Collaborative Routing With Multi-Heuristic Algorithm for Enhanced Delivery Efficiency in Truck-Multi Unmanned Aerial Vehicles Systems PY - 2024 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2024.3503686 UR - https://doi.org/10.1109/access.2024.3503686 DA - 2024-01-01 AU - Nugraha, Ramdhan C1 - Department of IT Convergence Engineering, Kumoh National Institute of Technology (KIT), Gumi, South Korea AU - Masuduzzaman, C1 - Department of IT Convergence Engineering, Kumoh National Institute of Technology (KIT), Gumi, South Korea AU - Shin, Soo C1 - Department of IT Convergence Engineering, Kumoh National Institute of Technology (KIT), Gumi, South Korea LA - en KW - Truck KW - Computer science KW - Heuristic KW - Routing (electronic design automation) KW - Vehicle routing problem KW - Algorithm KW - Real-time computing KW - Distributed computing KW - Computer network KW - Artificial intelligence KW - Engineering KW - Automotive engineering VL - 12 SP - 175988 EP - 176008 AB - This paper proposes the integration of Unmanned Aerial Vehicles (UAVs) into conventional delivery trucks as a solution to the challenges caused by the growing requirements of e-commerce and the congestion in urban areas in the logistics sector. The variable neighborhood priority routing problem using truck and multi-UAV (VNPRPTmD) is presented as a multi-heuristic technique aimed at minimizing delivery delays by utilizing both terrestrial and aerial delivery methods. The VNPRPTmD model improves delivery efficiency by utilizing consumer segregation and clustering, routing optimization, and multi-vehicle collaborative routing. The concept enables UAVs to operate with flexibility, departing from and returning to any specified spot, hence eliminating the requirement to return to their original location. This approach minimizes operational expenses and enhances the efficiency of delivering parcels by effectively leveraging the capabilities of trucks and multiple UAVs. The research provides an elaborate mathematical model, evaluates the materials and methods employed, and concludes with findings that clearly establish the effectiveness of this innovative delivery system. ER - TY - JOUR TI - An Adaptive Sliding-Window Ant Colony Optimization Approach for Multi-UAV Delivery Routing with Safety and Lateness Objectives PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 1999-4893 T2 - Algorithms DO - 10.3390/a19070570 UR - https://doi.org/10.3390/a19070570 DA - 2026-07-11 AU - Chen, Xin-Yi C1 - College of Software, Nankai University, Tianjin 300350, China AU - Shi, Lin C1 - School of Statistics and Data Science, Guangdong University of Finance & Economics, Guangzhou 510320, China AU - Li, Jian-Yu C1 - College of Artificial Intelligence, Nankai University, Tianjin 300350, China LA - en KW - Ant colony optimization algorithms KW - Routing (electronic design automation) KW - Heuristic KW - Adaptability KW - Multi-objective optimization KW - Ant colony KW - Pareto principle KW - Convergence (economics) VL - 19 IS - 7 SP - 570 EP - 570 AB - Urban unmanned aerial vehicle (UAV) logistics systems require routing algorithms that can balance delivery efficiency and operational safety under complex physical and environmental constraints. However, existing multi-objective routing methods often show limited adaptability when the search landscape changes during optimization. In particular, conventional ant colony optimization (ACO) algorithms usually rely on fixed pheromone update rules, which may lead to premature convergence, uneven Pareto front distributions, and pheromone-induced search stagnation. To address these limitations, this paper proposes a Sliding-Window Adaptive Multi-Strategy Ant Colony Optimization (SW-MSACO) framework for complex urban UAV delivery routing. First, a Complex Multi-objective Urban Routing Problem (CMURP) is formulated by jointly considering cumulative delivery lateness and safety risk under payload and nonlinear battery energy constraints. Second, a multi-strategy pheromone evolution pool is designed to provide complementary search behaviors for convergence acceleration, safety-efficiency balancing, and Pareto front diversity preservation. Third, a Sliding-Window Adaptive Strategy Selection (SW-ASS) mechanism is introduced to adjust strategy selection probabilities according to recent search performance rather than long-term cumulative rewards. Comprehensive simulation experiments under different map scales and task-load conditions demonstrate that SW-MSACO achieves improved Pareto solution quality and search stability compared with existing heuristic optimization approaches, particularly under large-scale and high-load scenarios, confirming the effectiveness of the proposed framework for complex UAV logistics optimization. ER - TY - JOUR TI - Research on the Whole-Life-Cycle Engineering Management and Control System for Safety Risks in Low-Altitude Logistics PY - 2026 SN - 2167-664X T2 - Management Science and Engineering DO - 10.12677/mse.2026.155093 UR - https://doi.org/10.12677/mse.2026.155093 DA - 2026-01-01 AU - 占, 龙辉 KW - Risk management KW - Control (management) KW - Management system KW - Control system KW - System safety KW - Risk Control VL - 15 IS - 05 SP - 966 EP - 971 AB - With the rapid development of the low-altitude economy, unmanned aerial vehicle (UAV) last-mile delivery has become a vital component of urban three-dimensional logistics systems.The 15th Five-Year Plan positions the low-altitude economy as an "emerging pillar industry", explicitly calling for enhanced refined management of low-altitude airspace, strengthened low-altitude flight safety assurance, and the promotion of healthy and orderly development of the low-altitude economy.As a key application area of this economy, low-altitude logistics is accelerating its large-scale deployment.However, multiple safety risks arising from complex urban low-altitude airspace conditions-such as airborne hardware failures, airspace operational conflicts, electromagnetic link interference, and unauthorized flight operations-seriously hinder the industrial-scale implementation.Drawing on UTM (Unmanned Aircraft System Traffic Management) engineering and airspace risk assessment technologies, and grounded in classic aviation safety theories including the Swiss Cheese Model and the accident chain theory, this paper systematically identifies full-chain safety risks in low-altitude logistics from a five-dimensional framework of "human, machine, environment, management, and information".It further delves into the underlying formation mechanisms and regulatory deficiencies, and constructs a closed-loop safety management system throughout the full life cycle, encompassing dynamic airspace planning, empowerment by intelligent supervision platforms, regulatory standards and norms, personnel training, and emergency support.This study aims to provide a reference for the safe operation and engineering-oriented governance of low-altitude logistics. ER - TY - JOUR TI - Routing, Scheduling, and Synchronization of Unmanned Aerial Vehicles (UAVs) with Bus Hitchhiking for Last-mile Parcel Delivery PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6773309 UR - https://doi.org/10.2139/ssrn.6773309 DA - 2026-01-01 AU - Zhen, Yizhi C1 - The University of Hong Kong, Pokfulam Road, Hong Kong, China AU - Zhang, Fangni C1 - The University of Hong Kong - University of Hong Kong AU - Kuo, Yong‐Hong C1 - The University of Hong Kong - Department of Industrial and Manufacturing Systems Engineering, 8/F Haking Wong Building, Pokfulam Road, Hong Kong, China AU - Ji, Kaifeng C1 - Tsinghua University, Beijing, 100084, China KW - Drone KW - Scalability KW - Synchronization (alternating current) KW - Simulated annealing KW - Linear programming KW - Scheduling (production processes) KW - Range (aeronautics) KW - Cost reduction ER - TY - GEN TI - Enhancing UAV Navigation: Predictive modeling of metro station locations based on spatial analysis and machine learning techniques PY - 2025 DO - 10.1109/eaic66483.2025.11101456 UR - https://doi.org/10.1109/eaic66483.2025.11101456 DA - 2025-06-05 AU - Fozdar, Shubham C1 - Thapar Institute of Engineering & Technology,Computer Science and Engineering,Patiala,Punjab,India AU - Maggu, Jyoti C1 - Thapar Institute of Engineering & Technology,Computer Science and Engineering,Patiala,Punjab,India AU - Kumar, Rajiv C1 - Thapar Institute of Engineering & Technology,Computer Science and Engineering,Patiala,Punjab,India LA - en KW - Computer science KW - Artificial intelligence KW - Machine learning KW - Real-time computing SP - 1 EP - 8 ER - TY - GEN TI - Application of Self-Play Deep Reinforcement Learning in Task Planning for UAV Swarms under Capacity Constraints PY - 2025 DO - 10.1145/3757110.3757143 UR - https://doi.org/10.1145/3757110.3757143 DA - 2025-05-16 AU - Chen, Jiaxi C1 - Sichuan Tengden Sci-tech Innovation Co., Ltd., Chengdu, Sichuan, China AU - Chen, Zilong C1 - School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China AU - Wen, Pengcheng C1 - Sichuan Tengden Sci-tech Innovation Co., Ltd., Chengdu, Sichuan, China AU - Chen, Binqi C1 - School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China AU - Yang, Wenjun C1 - School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China LA - en KW - Reinforcement learning KW - Variety (cybernetics) KW - Task (project management) KW - Oracle KW - Motion planning KW - Adversarial system KW - State space KW - State (computer science) SP - 202 EP - 212 AB - This paper develops an Adaptive Staircase-based Policy Space Response Oracle (ASP) architecture that uses a self-play deep reinforcement learning (DRL) method to solve the problem of planning tasks for UAV swarms in urban logistics and post-disaster rescue situations when there isn't enough time and resources. We show that UAV swarm path planning may be optimized across different scales by combining a two-player meta-game structure with adversarial instance creation and developing a staircase curriculum learning mechanism. The framework lets you learn from small amounts of pre-training (10 to 100 inspection points) and apply that knowledge to larger applications (200 to 1000 inspection points). The results of the experiments indicate that the ASP-based method works better than traditional Policy Optimization for Multi-Agent (POMO) algorithms. It cuts the average solution time by 41% and the total flight distance by 55.6% in tasks with a thousand nodes. This paper offers a systematic engineering approach for dynamic task planning in UAV swarms, overcoming the problem of balancing exploration and exploitation in complicated state spaces and proving that it can be used in a variety of situations. ER - TY - JOUR TI - GeoHopNet: Hopfield-Augmented Sparse Spatial Attention for Dynamic UAV Site Location Problem PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6576147 UR - https://doi.org/10.2139/ssrn.6576147 DA - 2026-01-01 AU - Zhi, Jianing C1 - Jiaxing University C1 - Jiaxing University, Zhejiang, China AU - Li, Xinghua C1 - Zhejiang University C1 - Zhejiang University, 38 Zheda Road, Hangzhou, 310058, China AU - Chen, Zidong LA - en KW - Computational complexity theory KW - Reinforcement learning KW - Regularization (linguistics) KW - Face (sociological concept) KW - Boundary (topology) KW - Trajectory KW - Core (optical fiber) KW - Artificial neural network ER - TY - GEN TI - Reinforcement Learning for Optimization of Collision Avoidance in Multiple Autonomous Aerial Robots Systems PY - 2025 DO - 10.1109/codit66093.2025.11321786 UR - https://doi.org/10.1109/codit66093.2025.11321786 DA - 2025-07-15 AU - Vizzotto, Marcos C1 - Federal University of Rio Grande do Sul,Graduate Program in Electrical Engineering,Porto Alegre,Brazil AU - Kohl, Guilherme C1 - Federal University of Rio Grande do Sul,Bachelor in Computer Science,Porto Alegre,Brazil,91.501-970 AU - Pereira, Carlos C1 - Federal University of Rio Grande do Sul,Graduate Program in Electrical Engineering,Porto Alegre,Brazil AU - de Freitas, Edison C1 - Federal University of Rio Grande do Sul,Graduate Program in Electrical Engineering,Porto Alegre,Brazil KW - Collision avoidance KW - Reinforcement learning KW - Trajectory KW - Obstacle avoidance KW - Motion planning KW - Robot KW - Collision KW - Collision avoidance system SP - 2143 EP - 2148 ER - TY - JOUR TI - Trajectory optimization for UAV-based medical delivery with temporal logic constraints and convex feasible set collision avoidance PY - 2025 PB - Elsevier BV SN - 2589-7918 T2 - Global Transitions DO - 10.1016/j.glt.2025.10.002 UR - https://doi.org/10.1016/j.glt.2025.10.002 DA - 2025-10-11 AU - Chen, Kaiyuan C1 - State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences,Beijing, 100190, China C1 - Vanke School of Public Health, Tsinghua University, Beijing, 100084,China AU - Suo, Yuhan C1 - School of Automation, Beijing Institute of Technology, Beijing, 100081, China AU - Cui, Shaowei C1 - State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences,Beijing, 100190, China AU - Xia, Yuanqing C1 - School of Automation, Beijing Institute of Technology, Beijing, 100081, China AU - Liang, Wannian C1 - Vanke School of Public Health, Tsinghua University, Beijing, 100084,China AU - Wang, Shuo C1 - State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences,Beijing, 100190, China LA - en KW - Collision avoidance KW - Obstacle avoidance KW - Trajectory KW - Convex optimization KW - Set (abstract data type) KW - Scalability KW - Temporal logic KW - Obstacle KW - Regular polygon VL - 8 IS - 1 SP - 110 EP - 119 AB - This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3-degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem—combining UAV dynamics, STL satisfaction, and collision avoidance—is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics. • A convex STL-based planner enables precise multi-goal UAV medical delivery. • Combines temporal logic, dynamics, and obstacle avoidance in a unified framework. • Iterative convexification ensures real-time safe navigation in 3D urban environments. ER - TY - JOUR TI - Exploring Autonomous Aircraft for Package Delivery in Urban Environments PY - 2023 SN - 2688-108X T2 - American Journal of Aerospace and Aeronautical Engineering DO - 10.71465/ajaae508 UR - https://doi.org/10.71465/ajaae508 DA - 2023-06-30 AU - Johnson, Michael C1 - Department of Aerospace Engineering, University of California, Los Angeles, USA KW - Drone KW - Autonomous system (mathematics) KW - Air traffic control KW - Delivery system KW - Aviation VL - 4 IS - 3 SP - 6 EP - 10 AB - The rise of autonomous aircraft presents new opportunities for efficient and rapid package delivery in urban environments. These unmanned aerial vehicles (UAVs) offer the potential to reduce traffic congestion, enhance delivery speeds, and improve logistical efficiency. This article explores the technological advancements, regulatory challenges, and operational considerations involved in using autonomous aircraft for package delivery in cities. We examine the role of AI, machine learning, and advanced sensors in enabling autonomous flight and discuss the future potential for these systems in transforming the logistics and transportation industries ER - TY - JOUR TI - Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2506.06038 UR - https://doi.org/10.48550/arxiv.2506.06038 DA - 2025-06-06 AU - Chen, Kaiyuan AU - Suo, Yuhan AU - Cui, Shaowei AU - Xia, Yuanqing AU - Liang, Wannian AU - Wang, Shuo LA - en KW - Collision avoidance KW - Convex optimization KW - Trajectory KW - Set (abstract data type) KW - Obstacle avoidance KW - Scalability KW - Temporal logic KW - Regular polygon KW - Obstacle AB - This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem-combining UAV dynamics, STL satisfaction, and collision avoidance-is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics. ER - TY - JOUR TI - Cooperative Control of Heterogeneous UAV-UGV Systems for Low-Altitude Last-Mile Delivery with Mobile Ground Support PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7353598 UR - https://doi.org/10.2139/ssrn.7353598 DA - 2026-01-01 AU - Xu, Weihan AU - Bian, Xuehang AU - Wang, Tianqi KW - Trajectory KW - Task (project management) KW - Energy consumption KW - Controller (irrigation) KW - Model predictive control KW - Control (management) KW - Energy (signal processing) KW - Vehicle dynamics ER - TY - RPRT TI - Multimodal Strategies for Mitigating Congestion from Urban Parcel Delivery PY - 2024 DO - 10.5038/cutr-nicr-y2-1-5 UR - https://doi.org/10.5038/cutr-nicr-y2-1-5 DA - 2024-10-01 AU - Hansen, Mark AU - Lin, Pei-Sung AU - Wang, Zhenyu AU - Zhang, Yu AU - Li, Ang LA - en KW - Computer science KW - Transport engineering KW - Engineering ER - TY - GEN TI - The Trade-Off Between Energy Consumption and Communication Quality in UAV Delivery: A Bayesian Optimization Deep Reinforcement Learning Approach PY - 2026 PB - Springer Science+Business Media SN - 1876-1100 T2 - Lecture notes in electrical engineering DO - 10.1007/978-981-92-3574-2_54 UR - https://doi.org/10.1007/978-981-92-3574-2_54 DA - 2026-09-15 AU - Zhou, Xiaofeng C1 - Navigation College, Jimei University, Xiamen, China AU - Sun, Yanglong C1 - Navigation College, Jimei University, Xiamen, China AU - Gao, Zhibin C1 - Navigation College, Jimei University, Xiamen, China LA - en KW - Reinforcement learning KW - Energy consumption KW - Trajectory KW - Efficient energy use KW - Simulated annealing KW - Energy (signal processing) KW - Path (computing) KW - Scheme (mathematics) SP - 655 EP - 664 ER - TY - JOUR TI - Research on Cooperative Obstacle Avoidance Method for Multi-Rotor UAV Swarms Based on Adaptive Sliding Mode Control PY - 2026 SN - 2832-6024 T2 - Frontiers in Computing and Intelligent Systems DO - 10.54097/besh1h10 UR - https://doi.org/10.54097/besh1h10 DA - 2026-04-16 AU - Nana, Pan C1 - Qingdao Hengxing University of Science and Technology, School of Intelligent Vehicles and Low-Altitude Emergency Response, China KW - Obstacle avoidance KW - Obstacle KW - Context (archaeology) KW - Trajectory KW - Collision avoidance KW - Controller (irrigation) KW - Control theory (sociology) KW - Mode (computer interface) KW - Sliding mode control VL - 16 IS - 3 SP - 7 EP - 14 AB - In the context of complex urban environments, the demand for dynamic obstacle avoidance and formation control in multi-rotor unmanned aerial vehicle (UAV) swarms has become increasingly prominent. This study addresses these challenges by proposing a collaborative control method that integrates lidar perception with adaptive sliding mode control. An enhanced YOLOv8 algorithm is employed to achieve real-time detection and trajectory prediction of dynamic obstacles, thereby constructing a constrained space for safe avoidance. Subsequently, an adaptive sliding mode controller is designed, which, combined with an adjacency graph communication topology, enables distributed cooperative control within the swarm. This approach effectively mitigates the chattering issues commonly associated with traditional sliding mode control. Validation through hardware-in-the-loop simulations and actual flight experiments demonstrates that the proposed method significantly reduces the response time for obstacle avoidance in dense obstacle scenarios while maintaining formation position errors within a low range. The results indicate a marked improvement in both operational safety and efficiency for UAV swarms engaged in urban logistics and emergency rescue missions, offering a practical and reliable solution for real-world applications. ER - TY - JOUR TI - Autonomous Drone Swarms for Last-Mile Delivery in Central American Logistics: An AI-Driven Framework PY - 2022 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20807237 UR - https://doi.org/10.5281/zenodo.20807237 DA - 2022-01-01 AU - Alfaro, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Hernandez, Vanessa C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Quevedo, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) LA - en KW - Drone KW - Software deployment KW - Resilience (materials science) KW - Flexibility (engineering) KW - Scalability KW - Key (lock) KW - Swarm behaviour KW - Disaster recovery KW - Aerospace AB - ABSTRACTThis research proposes an advanced artificial intelligence (AI)-driven framework for the deployment of autonomous drone swarms in last-mile delivery operations within Central American logistics systems. As e-commerce expansion, urbanization, and infrastructure limitations continue to reshape supply chain dynamics, last-mile delivery has emerged as one of the most complex and costly segments in logistics networks. This study addresses these challenges by exploring how coordinated multi-agent drone systems, supported by AI algorithms and aerospace telemetry, can optimize delivery efficiency, reduce operational costs, and enhance logistical resilience in emerging regional economies. The study begins by analyzing the structural constraints of last-mile delivery in Central America, including fragmented infrastructure, traffic congestion, rural accessibility limitations, and inconsistent logistics connectivity. These challenges significantly impact delivery speed, cost efficiency, and service reliability. Traditional transportation systems often struggle to meet increasing demand, particularly in geographically diverse environments. The proposed framework introduces autonomous drone swarms as a scalable and adaptable solution capable of navigating complex terrains while maintaining high delivery performance. At the core of the framework is a multi-agent system in which each drone operates as an intelligent autonomous unit while simultaneously coordinating with the swarm to achieve collective optimization goals. The system utilizes deep reinforcement learning and swarm intelligence algorithms to enable dynamic route planning, collision avoidance, adaptive task allocation, and energy-efficient operations. Real-time data inputs—including spatial positioning, weather conditions, traffic patterns, and delivery priorities—are integrated into the decision-making process, allowing the swarm to continuously adapt to changing operational environments. A key innovation of this research is the integration of aerospace telemetry systems to ensure real-time communication, coordination, and control among swarm units and ground-based logistics nodes. Telemetry data supports high-precision navigation, synchronization, and performance monitoring, enabling the swarm to operate effectively even in environments with limited infrastructure. Additionally, the framework incorporates predictive analytics to anticipate demand fluctuations, optimize scheduling, and minimize delivery delays. This predictive capability enhances the resilience and reliability of last-mile delivery operations. The proposed architecture is structured as a multi-layered cyber-physical system comprising data acquisition, data processing, and operational control layers. The data acquisition layer includes UAV sensors, GPS systems, and environmental monitoring devices. The processing layer leverages AI models, edge computing, and cloud-based analytics to manage real-time data streams and generate optimized decisions. The operational layer coordinates swarm execution, integrates with logistics platforms, and supports human-in-the-loop supervision when necessary. This integrated system enables seamless interaction between physical drone operations and digital decision-making processes. Simulation-based evaluations and scenario modeling are conducted to assess the performance of the framework under various logistical conditions, including urban delivery environments, rural distribution networks, and emergency response scenarios. Results indicate substantial improvements in delivery time reduction, route optimization, and energy efficiency compared to traditional single-drone or ground-based delivery systems. The swarm-based model also demonstrates enhanced scalability, allowing for rapid adaptation to increased demand and expanded delivery coverage. Furthermore, the distributed nature of the swarm provides redundancy and fault tolerance, ensuring continuous operation even in the event of individual unit failures. From a strategic perspective, the adoption of autonomous drone swarms presents significant opportunities for transforming logistics systems in Central America. The framework supports economic development by improving supply chain efficiency, enabling access to remote regions, and fostering innovation in logistics technologies. It also aligns with broader efforts toward digital transformation, smart infrastructure development, and sustainable logistics practices by reducing carbon emissions associated with traditional transportation modes. In conclusion, this research demonstrates that AI-driven autonomous drone swarms provide a powerful and scalable solution for optimizing last-mile delivery in emerging logistics markets. The proposed framework enhances operational efficiency, adaptability, and resilience, offering a pathway toward next-generation logistics systems that integrate aerospace technology and intelligent systems for regional and global impact. ER - TY - JOUR TI - Optimal Cruise Speeds and Environmental Impacts on Energy use of Unmanned Aerial Vehicles for Last-Mile Delivery PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5388681 UR - https://doi.org/10.2139/ssrn.5388681 DA - 2025-01-01 AU - Choi, SungHyuk C1 - Queen Mary University of London C1 - Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom LA - en KW - Mile KW - Cruise KW - Last mile (transportation) KW - Aeronautics KW - Environmental science KW - Energy (signal processing) KW - Marine engineering KW - Transport engineering KW - Engineering KW - Geography KW - Aerospace engineering KW - Geodesy KW - Physics ER - TY - JOUR TI - Autonomous Drone Swarms for Last-Mile Delivery in Central American Logistics: An AI-Driven Framework PY - 2022 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20807238 UR - https://doi.org/10.5281/zenodo.20807238 DA - 2022-01-01 AU - Alfaro, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Hernandez, Vanessa C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Quevedo, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) LA - en KW - Drone KW - Software deployment KW - Resilience (materials science) KW - Flexibility (engineering) KW - Scalability KW - Key (lock) KW - Swarm behaviour KW - Disaster recovery KW - Aerospace AB - ABSTRACTThis research proposes an advanced artificial intelligence (AI)-driven framework for the deployment of autonomous drone swarms in last-mile delivery operations within Central American logistics systems. As e-commerce expansion, urbanization, and infrastructure limitations continue to reshape supply chain dynamics, last-mile delivery has emerged as one of the most complex and costly segments in logistics networks. This study addresses these challenges by exploring how coordinated multi-agent drone systems, supported by AI algorithms and aerospace telemetry, can optimize delivery efficiency, reduce operational costs, and enhance logistical resilience in emerging regional economies. The study begins by analyzing the structural constraints of last-mile delivery in Central America, including fragmented infrastructure, traffic congestion, rural accessibility limitations, and inconsistent logistics connectivity. These challenges significantly impact delivery speed, cost efficiency, and service reliability. Traditional transportation systems often struggle to meet increasing demand, particularly in geographically diverse environments. The proposed framework introduces autonomous drone swarms as a scalable and adaptable solution capable of navigating complex terrains while maintaining high delivery performance. At the core of the framework is a multi-agent system in which each drone operates as an intelligent autonomous unit while simultaneously coordinating with the swarm to achieve collective optimization goals. The system utilizes deep reinforcement learning and swarm intelligence algorithms to enable dynamic route planning, collision avoidance, adaptive task allocation, and energy-efficient operations. Real-time data inputs—including spatial positioning, weather conditions, traffic patterns, and delivery priorities—are integrated into the decision-making process, allowing the swarm to continuously adapt to changing operational environments. A key innovation of this research is the integration of aerospace telemetry systems to ensure real-time communication, coordination, and control among swarm units and ground-based logistics nodes. Telemetry data supports high-precision navigation, synchronization, and performance monitoring, enabling the swarm to operate effectively even in environments with limited infrastructure. Additionally, the framework incorporates predictive analytics to anticipate demand fluctuations, optimize scheduling, and minimize delivery delays. This predictive capability enhances the resilience and reliability of last-mile delivery operations. The proposed architecture is structured as a multi-layered cyber-physical system comprising data acquisition, data processing, and operational control layers. The data acquisition layer includes UAV sensors, GPS systems, and environmental monitoring devices. The processing layer leverages AI models, edge computing, and cloud-based analytics to manage real-time data streams and generate optimized decisions. The operational layer coordinates swarm execution, integrates with logistics platforms, and supports human-in-the-loop supervision when necessary. This integrated system enables seamless interaction between physical drone operations and digital decision-making processes. Simulation-based evaluations and scenario modeling are conducted to assess the performance of the framework under various logistical conditions, including urban delivery environments, rural distribution networks, and emergency response scenarios. Results indicate substantial improvements in delivery time reduction, route optimization, and energy efficiency compared to traditional single-drone or ground-based delivery systems. The swarm-based model also demonstrates enhanced scalability, allowing for rapid adaptation to increased demand and expanded delivery coverage. Furthermore, the distributed nature of the swarm provides redundancy and fault tolerance, ensuring continuous operation even in the event of individual unit failures. From a strategic perspective, the adoption of autonomous drone swarms presents significant opportunities for transforming logistics systems in Central America. The framework supports economic development by improving supply chain efficiency, enabling access to remote regions, and fostering innovation in logistics technologies. It also aligns with broader efforts toward digital transformation, smart infrastructure development, and sustainable logistics practices by reducing carbon emissions associated with traditional transportation modes. In conclusion, this research demonstrates that AI-driven autonomous drone swarms provide a powerful and scalable solution for optimizing last-mile delivery in emerging logistics markets. The proposed framework enhances operational efficiency, adaptability, and resilience, offering a pathway toward next-generation logistics systems that integrate aerospace technology and intelligent systems for regional and global impact. ER - TY - JOUR TI - Optimizing Urban Delivery Systems with Innovative Drone Technology for Sustainability PY - 2025 SN - 2454-3896 T2 - International Academic Journal of Science and Engineering DO - 10.71086/iajse/v12i3/iajse1230 UR - https://doi.org/10.71086/iajse/v12i3/iajse1230 DA - 2025-09-30 AU - Yadav, Kamlesh C1 - Assistant Professor, Department of Information Technology , Kalinga University , Naya Raipur , Chhattisgarh , India AU - Nandy, Manish C1 - Assistant C1 - Department of Information Technology , Kalinga University , Naya Raipur , Chhattisgarh , India KW - Drone KW - Sustainability KW - City logistics KW - Fuel efficiency KW - Consumption (sociology) KW - Urban sustainability VL - 12 IS - 3 SP - 90 EP - 98 AB - The article focuses on the optimization of city logistics using the assistance of drone-delivery solutions on the last mile. The growing pressure to implement efficient and effective delivery systems in the urban centers has shown that there is a need to embrace innovative logistics approaches. This research provides the solution to traffic congestion, delays during the delivery process, and other environmental problems by uniting the use of unmanned aerial vehicles (UAVs). The research will be conducted based on a mixed-method design where the simulation models will be applied in conjunction with the actual case studies to calculate the impacts of the drone application on the time spent on delivery, fuel consumption amounted to, and carbon emissions. The findings indicate that the benefits are enormous considering that the delivery time will be reduced by 40 (75 minutes to 45 minutes) and there will be less fuel consumption by 75 (40 liters to 10 liters) compared to the traditional processes. In addition, the carbon emission was also switch to 72 percent (18 kg CO 2 to 5 kg CO 2) which also saved the total cost of operations by 58 percent. The effect of the distance of the package delivery, the weight of the package and urban infrastructure on the use of drone delivery are also studied in the paper. The results identify the potential of drones to enhance urban mobility, urban sustainability, and cost-efficiency of logistics. The results are applicable to the existing literature on sustainable urban logistics and may be applied to educate policymakers, logistic businesses, and urban planners. The paper concludes by providing recommendations on how the drone delivery systems can be integrated into the already established urban logistics systems to make the operations more sustainable and efficient. ER - TY - CHAP TI - Future Implications of the Drone Industry PY - 2025 PB - IGI Global SN - 2327-0411 T2 - Advances in computational intelligence and robotics book series DO - 10.4018/979-8-3693-8497-8.ch007 UR - https://doi.org/10.4018/979-8-3693-8497-8.ch007 DA - 2025-05-07 AU - Tahir, Muhammad C1 - School of Software Technology, Dalian University of Technology, China AU - Aamir, Muhammad C1 - Huanggang Normal University, China AU - Rajper, Sajjad C1 - Iqra University, Karachi, Pakistan AU - Kumar, Rajesh C1 - Department of Computer Science, University of Milano Bicocca, Italy LA - en KW - Drone KW - Aeronautics KW - Business KW - Engineering KW - Biology SP - 167 EP - 182 ER - TY - JOUR TI - Urban Infrastructure Systems Integration PY - 2025 DO - 10.22541/au.175693426.63852917/v1 UR - https://doi.org/10.22541/au.175693426.63852917/v1 DA - 2025-09-03 AU - Rothschild, Keith LA - en KW - Computer science KW - Business KW - Environmental planning KW - Environmental science AB - This article posits a new paradigm for understanding the design of urban infrastructure, framing the constituent disciplines of Civil, Surveying, and Architectural Engineering as components of a single, integrated master planning function. We argue that the traditional, siloed view of these professions is obsolete in the face of modern urban complexity and the transformative power of digital technologies. Using the guiding analogies of infrastructure as ’The Skeleton of Society’ and’The Operating System of a City,’ this paper synthesizes the distinct yet interdependent roles of these engineering fields. Civil engineering provides the foundational framework—the societal skeleton. Surveying engineering creates the high-fidelity geospatial blueprint—the anatomical map and hardware abstraction layer. Architectural engineering designs the high-performance, system-rich buildings—the user-facing applications. We demonstrate how digital platforms, particularly Building Information Modeling (BIM) and Urban Digital Twins, serve as the unifying connective tissue, enabling a truly integrated and collaborative design process. Through a critical analysis of landmark urban redevelopment projects—including Hudson Yards in New York, King’s Cross in London, and the ”smart city” exemplars of Masdar City and Songdo—we illustrate this integrated practice in action. The article concludes that the contemporary engineer’s most critical role is that of a master integrator, orchestrating a complex interplay of physical and digital systems to create resilient, sustainable, and functional urban environments for the 21st century.1.0 Introduction: Engineering on a Grand ScaleThe practice of engineering in the 21st century is undergoing a profound transformation, driven by the escalating complexity of urban environments and the pervasive influence of digital technologies. The traditional model, which often treated Civil, Surveying, and Architectural Engineering as discrete, sequential disciplines, is proving inadequate for the challenges of modern infrastructure development. Today’s grand-scale projects—the redevelopment of entire city districts, the creation of smart cities from the ground up, and the retrofitting of existing urban cores for resilience and sustainability—demand a new mode of thinking. This new paradigm recasts the engineer not merely as a technical specialist, but as a master planner and integrator, a conductor of a complex orchestra of physical systems, digital information, and societal needs. This article will articulate and defend this integrated vision of engineering practice, demonstrating that the future of our built environment depends on our ability to synthesize these disciplines into a cohesive, collaborative whole.1.1 The Evolving Role of the EngineerThe contemporary engineer’s mandate has expanded far beyond the narrow confines of technical calculation and design. As stewards of our nation’s infrastructure, engineers are now explicitly entrusted with the mission of ”building a better world”.1 This is not a rhetorical flourish but a reflection of a deep-seated professional obligation to enhance the health, safety, and welfare of the communities they serve.2 This expanded role is codified in the ethical frameworks of professional bodies like the American Society of Civil Engineers (ASCE), which emphasize that public trust and safety depend on the ethical performance of an engineer’s duties.3 This responsibility extends to the social fabric of the city; modern engineering practice must be inclusive, recognizing and addressing the unique needs of diverse demographic, social, and economic groups.4 Consequently, the engineer is increasingly called upon to act as a trusted advisor to policymakers, leveraging technical expertise to inform the development of infrastructure policies and laws that support vibrant and equitable communities.1 This evolution demands a shift in perspective: from a focus on isolated projects to a holistic vision for the future, where engineers are leaders in reimagining the very fabric of our cities.51.2 The Challenge of Complexity and IntegrationThe defining characteristic of modern urban infrastructure projects is their immense complexity. A typical project team brings together a diverse array of specialists, including architects, civil engineers, structural engineers, mechanical engineers, planners, and surveyors, each contributing a unique perspective and set of objectives.6 The central challenge of ”Engineering on a Grand Scale” is the synthesis of this disparate information into a coherent and functional physical form.7 Each team member must develop discipline-specific information and design features, which must then be shared and reconciled with the work of every other team member whose decisions are impacted by or dependent upon that information.6 The traditional, linear workflow—where a surveyor produces a map, a civil engineer designs the site, and an architect designs the building—is fraught with inefficiency, redundancy, and the potential for costly conflicts discovered late in the process. The contemporary solution is a move towards a deeply integrated, interdisciplinary approach, where collaboration begins at the earliest stages of conceptual design and continues throughout the project lifecycle.8 This requires not only a cultural shift but also new technological platforms that can facilitate this level of seamless information exchange.1.3 Conceptual Framework IntroductionTo navigate and articulate this new paradigm of integrated engineering, this paper employs a set of powerful conceptual analogies that serve as lenses for understanding the distinct yet complementary roles of each discipline. These analogies are not merely illustrative; they form an analytical framework that reframes our understanding of the city itself.The Skeleton of Society: This analogy positions civil engineering as the provider of the fundamental, load-bearing structures that support all societal functions. Much like a biological skeleton, infrastructure networks for transportation, water, and energy are the essential framework that gives a city its shape, strength, and capacity for movement.7The Operating System of a City: This novel framework extends the biological metaphor into the cyber-physical realm, viewing the city as a dynamic, computational system. This analogy deconstructs the integrated engineering practice into the core components of a computer operating system. Civil engineering designs the foundational ”kernel”—the essential, low-level services. Surveying engineering creates the ”hardware map”—the high-fidelity digital representation of the physical world. Architectural engineering develops the ”applications”—the complex, user-facing buildings that run on the city’s platform.111.4 Roadmap of the ArticleThis article will systematically develop this integrated framework. Section 2.0 will explore the role of civil engineering as ’The Skeleton of Society,’ focusing on its foundational, structural, and ethical responsibilities. Section 3.0 will detail the function of surveying engineering in creating the high-precision ’anatomical map’ that serves as the geospatial blueprint for all development. Section 4.0 will present the central theoretical contribution of the paper, fully articulating the ’Operating System of a City’ analogy as a comprehensive model for infrastructure integration. Section 5.0 will ground this theory in practice through a detailed comparative analysis of landmark urban redevelopment projects that exemplify the principles of integrated design. Finally, Section 6.0 will conclude by synthesizing these arguments and positing a future where the engineer’s most vital role is that of the master planner and integrator, orchestrating the complex systems that will define the cities of tomorrow.2.0 The Skeleton of Society: Civil Engineering as the Foundational FrameworkThe analogy of infrastructure as ”the skeleton of society” provides a potent conceptual tool for understanding the fundamental role of civil engineering. Like the skeletal system of a living organism, the infrastructure designed and built by civil engineers provides the essential structure, support, and conduits for movement that enable all other societal functions to exist and thrive.7 This framework is largely invisible during its normal operation, yet its integrity is paramount to the health and vitality of the urban body. This section explores the macro-scale systems that form this skeleton, the modern imperatives of resilience and sustainability that ensure its longevity, and the profound ethical responsibilities that guide its creation.2.1 Macro-Scale Systems: The Bones of the Urban AnatomyCivil engineering is the discipline responsible for the conception, design, construction, and maintenance of the large-scale public works and systems that form the physical backbone of civilization.9 These are the ”bones” of the urban anatomy, the primary structures that dictate the city’s form, facilitate its metabolism, and define its capacity for growth and interaction. These systems can be broadly categorized into several critical domains:Transportation Networks: This includes the comprehensive system of roads, highways, bridges, tunnels, railways, and airports that enable the movement of people and goods.13These networks are the primary load-bearing elements of the economic skeleton, connecting centers of production with markets and communities with workplaces.Water Resources and Management: Civil engineers design and oversee the systems for water supply, treatment, and distribution, as well as the collection and treatment of wastewater and stormwater.10 These systems function as the city’s circulatory system, essential for public health and environmental protection.Energy Infrastructure: While often involving collaboration with electrical and mechanical engineers, civil engineers are crucial in the siting, structural design, and construction of power generation facilities, transmission networks, and distribution grids that power the urban environment.The historical legacy of civil engineering is written in the monumental projects that have defined nations and eras, from the Roman aqueducts to the Hoover Dam and the Golden Gate Bridge.5 These are not merely functional objects but enduring testaments to the profession’s capacity for innovation, creativity, and the organization of human effort on a grand scale.5 They physically embody the aspirations of a society and form a permanent part of its physical and cultural landscape.The success of these foundational systems gives rise to a significant professional challenge. The most effective infrastructure is often the most unobtrusive. A reliable water supply, a structurally sound bridge, and an efficient sanitation system all function silently in the background of daily life, becoming conspicuous only in their or This a challenge for public and The very success of civil engineering in creating systems can to a societal that the critical for and must in by like the for to the of this skeleton to the public and to This reframes the civil engineer’s to be not only and but also and for the health of the societal skeleton they are with Engineering for and A Skeleton to skeleton of a modern city be designed as a must be a and of and to particularly with The engineering has this through the of resilience and which are now central of infrastructure provides a comprehensive of resilience as the to and to from to enable this systems that infrastructure system beyond its design an for not but a of and be of The is to critical systems operating or to to a that is to the as defined in is a set of and social in which all of society has the capacity and to and its of the foundational systems upon which that of This a to design and these engineers analytical and design is a critical that the of an infrastructure its entire from construction to operation, and design decisions that and is a core of modern a framework that to the and performance of infrastructure principles of resilience and sustainability are also through in and design The of as and the environmental of construction the of of systems like and to a paradigm These work in with the to water and enhance creating infrastructure that is functional and The The the entire practice of civil engineering is a ethical on the paramount of public health, safety, and This is the engineer’s a that every every design and every construction civil engineers the can be involving the of and immense societal The a of from of like the to inform to and construction the public the modern this ethical responsibility has expanded to societal is for infrastructure to be merely and must also be and its are to engineering that and the unique needs of diverse demographic, social, and cultural This how infrastructure projects communities and that and are This to social responsibility the engineer from a technical into an in the creation of a and a of the public the Surveying Engineering as the civil engineering provides the structural skeleton of then surveying engineering provides the anatomical is the discipline that the complex, of the physical into a and digital This act of and representation is not a in the development is the creation of a foundational that is the of all design, and construction the engineering on a grand be to a operating an understanding of the to Digital The of historical and enduring role of the surveyor is to to This function is of profound and societal as the and water for and other the very of these with and that development projects with and the of This of the urban fabric into discrete, defined is the fundamental act of upon which all urban planning is the of modern surveying engineering has expanded beyond this traditional function. The contemporary surveyor is a master of geospatial responsible for creating a digital model of the physical This and not but all physical of the including the of like and bodies of water, and the of existing This the critical for the work of civil engineers, architects, and urban planners, the essential for all design The of the of surveying from a discipline of and to of digital has driven by a of technologies. These for the collection of geospatial with and provides a for all from a of a surveyor can the of or the This all project from to to be to a as the American of and all project and and or is a that a to with A of the for each to a and This a set of or of each with a and This can be to create detailed and of and complex infrastructure, an tool for and the creation of foundational for digital Surveying and The of surveying with or has the and safety of can or for or to detailed This to traditional surveying for a of Information is the powerful that brings all of this disparate A and to and geospatial in a map is the digital environment where networks, and environmental can be and in to The is the of the a dynamic, model of the urban environment that The of to geospatial blueprint by the surveyor is not merely a of existing is a tool that the design process. The has and profound for engineering and which the and is essential for effective and that and which the and of the the design for buildings and their environmental can or potential from to environmental and with this critical information at the of a surveying engineering serves as the of in creating and the project team to potential as or and to into the design from its very The work the into the with and the for a and built foundational role is in the digital The of the dynamic, model of a physical on the of a and geospatial The creation of this model to the like to the of the or the physical is not during construction and throughout the of an the role is from that of a to the of the Through and the of systems, the surveyor the of that the digital a representation of its physical This responsibility is as the and of the entire digital smart city platforms to maintenance on the of this The Operating System of a City: A New for fully the interdependent of modern infrastructure design, is to move beyond traditional mechanical or biological analogies and a paradigm from the of information the city as an operating system. Urban Operating System is a powerful conceptual framework for understanding the complex, cyber-physical systems that a modern This model reframes the urban environment not as a collection of but as a dynamic, computational where are and are in The Urban is not a of but a metaphor for a new mode of that physical infrastructure with digital of and This section will this powerful roles its to the disciplines of and engineering, and the digital that enable its the Urban of an Urban from the rise of ”smart where digital are to and urban life, city and have and hardware that to run cities like a operating system, in from a of to and This creates a system that urban and the daily of into a single, that and This paradigm provides a coherent for how the of civil engineering physical surveying and engineering can be into a single, The of the Urban a operating system, the Urban can be as a with each a set of functions and the complexity of the The the very core of the Urban is the the is the central that the most fundamental as the and the urban the is to the foundational infrastructure systems designed by civil These are the low-level that are for the city’s and and Systems: The networks of and treatment that the city’s water and The infrastructure for and and Networks: The roads, bridges, and public systems that enable physical Systems: The and for and systems are the city’s essential They are designed for and the and upon which all urban functions as a largely to the this civil infrastructure provides the support that urban The the to the city’s physical needs a to and with This is the role of the ”hardware abstraction or ”hardware a the is a of that the unique of physical hardware components into a that the can the Urban this function is by the work of surveying of geospatial and by defined of the built and the of the city’s hardware This the complex and physical of the city and into a and a Information System This abstraction the infrastructure and to with the physical through a to with the complexity of the ground The map is the critical that the physical city The civil infrastructure is the and geospatial is the hardware map, then buildings are the that run on the city’s operating system. They are the primary through which the city’s and The design and of these complex is the of engineering modern is a system. essential water, from the city’s provides a for its and increasingly to the as energy or generation The engineer is the responsible for this complex of and primary is the seamless of the which can be of as its and circulatory Systems: The The system that and and Systems: The that the and its and Systems: The circulatory and The that and all other systems, performance and energy rise of ”smart is the of this where buildings are designed as that are fully integrated into the Urban The and Digital conceptual of the Urban is by a new generation of digital that serve as unifying platforms for interdisciplinary Information Modeling is the that the of the Urban to be designed and is a that provides architects, engineers, and construction with the and to design, and buildings and the the civil engineer’s the and the engineer’s systems can all be integrated into a single, This for which conflicts structural, and components construction immense of and the of our is the for the Urban where all the components are and Digital is the development the Digital is the of the Urban A digital is a model of a physical system, or that is with from in the physical urban digital is a of the entire its infrastructure, its environmental This model city to the of a new on or the of a maintenance and the of the entire urban The digital is the of the Urban a where the of the physical city is and in the digital model to a critical the future of infrastructure design. the Urban to its physical components must be to their This that infrastructure can be designed as must be designed with a digital an The design must now for the of transmission and into the physical A is a is a with and that a its A water is a is a with and that can in This the of work for civil and engineers, to in the design of cyber-physical They must design not only for physical performance but also for digital a shift that a significant evolution in engineering and professional The in of Urban theoretical framework of the Urban its in the complex of large-scale landmark urban redevelopment is to the challenges and that from the of and engineering. These the of existing urban cores and the creation of new cities serve as living for the principles of integrated design. They how are leveraging and collaborative to engineering Hudson New City: Engineering Hudson Yards development on the of as a monumental to modern engineering is a city” built of the in the a that civil and structural engineering The project serves as a of engineering driven by physical primary civil and structural challenge to a of and the new and then to the of this and into the ground This a complex system involving a of into the and existing structural solution for the for in New the and the of the role of surveying engineering critical to this process. a be surveying to create an and digital map of the complex including the of tunnels, and This high-fidelity ”hardware the essential blueprint that structural engineers to the new through the of existing each engineering The design of and systems to be with the unique structural demands of on a The entire by digital A sequential construction analysis the of to ensure that the existing structures at every of for that the of and structural the Hudson Yards of where complexity a level of interdisciplinary collaboration and digital that has set a new for urban King’s and redevelopment of King’s Cross in a model of by and the of The project a of and into a vibrant new construction with the of significant civil and surveying challenge new buildings and to King’s Cross ensure the safety of this critical infrastructure, a system This system surveying with to of the and during This the construction team to with with set to work movement civil engineering the design of a energy the development and built the infrastructure to be The and and to the future capacity of the site, with to for to existing engineering in the design of this energy of the and Engineers to to these modern to a of from new to structures like the Building and the This often the design of and other to the new system with the of the entire energy system is by a and system, a form of that the performance of the energy centers on and environmental King’s Cross an on and the of modern systems with Masdar City and the Urban City in the and the in a of designed from their to be exemplars of and As cities built from a they the of the Urban City as a city in the of the design is a in holistic integration. Architectural engineering principles are in the design by traditional and that and the for These are by systems, as that power engineering provides the including a water system that and an for The entire by work of the city’s for its Masdar is a physical of an designed for environmental by is an designed for digital The entire built on is by a digital backbone of networks and pervasive of the city’s core water, and from a central a for the Urban with of the core Architectural engineering is in the of systems which energy in every and are to the Civil engineering systems, as the collection and water are also integrated into this central The foundational ”hardware for this entire digital through comprehensive surveying and from the the power of a on creating a for urban and diverse in their and primary illustrate the of engineering integration. Hudson Yards driven by structural King’s Cross by the demands of and Masdar and by to sustainability and digital in each success on the seamless collaboration of and engineers, by shared digital The provides a comparative synthesis of these the distinct of each discipline and the that their integration. ER - TY - JOUR TI - Urban Emergency Delivery Resilience: The Potential Role of Unmanned Aerial Vehicles PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5227086 UR - https://doi.org/10.2139/ssrn.5227086 DA - 2025-01-01 AU - Peng, Jinhan C1 - South China University of Technology C1 - South China University of Technology, Wushan, Guangzhou, AR 510640, China AU - Mao, Jingyi C1 - South China University of Technology C1 - South China University of Technology, Wushan, Guangzhou, AR 510640, China AU - Lin, Peiqun C1 - South China University of Technology C1 - South China University of Technology, Wushan, Guangzhou, AR 510640, China AU - Li, Qijie C1 - South China University of Technology C1 - South China University of Technology, Wushan, Guangzhou, AR 510640, China LA - en KW - Resilience (materials science) KW - Emergency response KW - Business KW - Emergency management KW - Urban resilience KW - Drone KW - Aeronautics KW - Transport engineering KW - Urban planning KW - Engineering KW - Medical emergency KW - Civil engineering KW - Political science KW - Medicine ER - TY - GEN TI - Radar-On-Chip laboratory characterization for UAM applications PY - 2023 DO - 10.1109/metroaerospace57412.2023.10190006 UR - https://doi.org/10.1109/metroaerospace57412.2023.10190006 DA - 2023-06-19 AU - Menichino, Aniello C1 - Italian Aerospace Research Center (CIRA),Capua,CE,Italy,81043 AU - Di Vito, Vittorio C1 - Italian Aerospace Research Center (CIRA),Capua,CE,Italy,81043 AU - Ariante, Gennaro C1 - University of Naples “Parthenope”,Department of Science and Technology,Naples,Italy,80133 AU - Del Core, Giuseppe C1 - University of Naples “Parthenope”,Department of Science and Technology,Naples,Italy,80133 LA - en KW - Drone KW - Radar KW - Computer science KW - Automotive industry KW - Dimension (graph theory) KW - Aeronautics KW - Class (philosophy) KW - Systems engineering KW - Embedded system KW - Engineering KW - Aerospace engineering KW - Telecommunications KW - Artificial intelligence SP - 296 EP - 301 ER - TY - THES TI - Towards Reliable and Efficient Safety-Critical Systems: A DO-178C Framework for UAV Development PY - 2024 PB - Polytechnique Montréal T2 - PolyPublie (École Polytechnique de Montréal) DA - 2024-05-01 AU - Amaral Misson, Henrique LA - en KW - Context (archaeology) KW - Christian ministry KW - Initial training ER - TY - JOUR TI - Finite State Machine-Based Multi-Landing Autonomous UAV Navigation Using the Waypoint Follower Algorithm on ROS2 PY - 2026 SN - 0216-4221 T2 - Informatik Jurnal Ilmu Komputer DO - 10.52958/iftk.v22i1.12680 UR - https://doi.org/10.52958/iftk.v22i1.12680 DA - 2026-04-10 AU - Anggraeni, Pipit C1 - Politeknik Manufaktur Bandung AU - Khoirunnisa, Hilda C1 - Politeknik Manufaktur Bandung AU - Utama, Alif C1 - Politeknik Manufaktur Bandung KW - Waypoint KW - Position (finance) KW - State (computer science) KW - Supervisor KW - Navigation system KW - Controller (irrigation) KW - Robot KW - Odometer KW - Control (management) VL - 22 IS - 1 SP - 36 EP - 44 AB - Challenges in medical logistics, such as geographical limitations, urban congestion, and ground transport risks, necessitate efficient and safe delivery solutions. Unmanned Aerial Vehicles (UAVs) offer a promising alternative. This research aims to develop and implement an autonomous UAV navigation system capable of complex multi-landing missions, validated for medical delivery scenarios. The system employs a hierarchical control architecture, integrating the Robot Operating System 2 (ROS2) as the high-level mission supervisor with the PX4 flight controller for low-level control. Mission logic, including navigation using the Waypoint Follower algorithm and landing sequences, is formally governed by a Finite State Machine (FSM) implemented in ROS2. Communication between ROS2 and PX4 is facilitated by Micro XRCE-DDS. System validation was performed via Software-in-the-Loop (SITL) simulation using Gazebo, featuring a mission scenario with four waypoints and one intermediate landing. Simulation results demonstrated that the system successfully completed the entire mission autonomously (100% success rate). Quantitative analysis of position accuracy revealed total position errors () between 1.35 m and 1.57 m during navigation phases, and an error of 0.84 m at the intermediate landing. Errors were found to be consistently dominant on the Y-axis. In conclusion, the proposed architecture, which separates mission logic (FSM in ROS2) from flight control (PX4), provides a robust and functional approach for autonomous UAV applications requiring complex mission interactions. ER - TY - JOUR TI - Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Prediction PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2512.24324 UR - https://doi.org/10.48550/arxiv.2512.24324 DA - 2025-12-30 AU - Li, Haojin AU - Zhang, Anbang AU - Sun, Chen AU - Feng, Chenyuan AU - Qu, Kaiqian AU - Quek, Tony AU - Zhang, Haijun KW - Weighting KW - Robustness (evolution) KW - Discriminative model KW - Modal KW - Baseline (sea) KW - Representation (politics) KW - Modalities KW - Geospatial analysis KW - Computation AB - The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the "multi-source representation beam semantics" associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods. ER - TY - JOUR TI - Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Prediction PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DA - 2025-12-30 AU - Li, Haojin AU - Zhang, Anbang AU - Sun, Chen AU - Feng, Chenyuan AU - Qu, Kaiqian AU - Quek, Tony AU - Zhang, Haijun KW - Weighting KW - Robustness (evolution) KW - Discriminative model KW - Modal KW - Baseline (sea) KW - Representation (politics) KW - Modalities KW - Geospatial analysis KW - Computation AB - The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the "multi-source representation beam semantics" associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods. ER - TY - THES TI - Unobstructed Constructed: Developing a supporting tool for municipalities to stimulate the implementation of smart solutions in construction logistics in civil structure projects PY - 2018 PB - Delft University of Technology T2 - Research Repository (Delft University of Technology) DA - 2018-01-01 AU - Hoekstra, Eke LA - en KW - Work (physics) KW - Urbanization KW - Brownfield KW - Business KW - Population KW - Smart city KW - Sustainable development KW - Environmental planning KW - Scale (ratio) KW - Transport engineering KW - Civil engineering KW - Economic growth KW - Engineering KW - Computer science KW - Redevelopment KW - Geography KW - Political science KW - Computer security KW - Internet of Things KW - Economics ER - TY - GEN TI - Research on Vehicle Routing Problem in Urban Distribution Using Unmanned Aerial Vehicles PY - 2020 T2 - CICTP 2020 DO - 10.1061/9780784483053.408 UR - https://doi.org/10.1061/9780784483053.408 DA - 2020-12-09 AU - Li, Tao C1 - Research Institute of Highway Ministry of Transport C1 - Transport Planning and Research Institute, Ministry of Transport C1 - Research Institute of Highway Ministry of Transport, Beijing, China C1 - Transport Planning and Research Institute, Ministry of Transport, Beijing, China AU - Yang, Wen-Yin C1 - Research Institute of Highway Ministry of Transport C1 - Transport Planning and Research Institute, Ministry of Transport C1 - Research Institute of Highway Ministry of Transport, Beijing, China C1 - Transport Planning and Research Institute, Ministry of Transport, Beijing, China AU - Shen, Meng-Qing C1 - Research Institute of Highway Ministry of Transport C1 - Transport Planning and Research Institute, Ministry of Transport C1 - Research Institute of Highway Ministry of Transport, Beijing, China C1 - Transport Planning and Research Institute, Ministry of Transport, Beijing, China AU - Liu, Hang C1 - Research Institute of Highway Ministry of Transport C1 - Transport Planning and Research Institute, Ministry of Transport C1 - Research Institute of Highway Ministry of Transport, Beijing, China C1 - Transport Planning and Research Institute, Ministry of Transport, Beijing, China LA - en KW - Vehicle routing problem KW - Heuristic KW - Computer science KW - Routing (electronic design automation) KW - Traffic congestion KW - Distribution (mathematics) KW - City logistics KW - Drone KW - Transport engineering KW - Real-time computing KW - Automotive engineering KW - Engineering KW - Artificial intelligence KW - Embedded system SP - 4903 EP - 4914 ER - TY - JOUR TI - DVA-ACLF: Designated Verifier Asymmetric Anonymous Credential Scheme for Low-Altitude Flight PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2327-4697 T2 - IEEE Transactions on Network Science and Engineering DO - 10.1109/tnse.2025.3649720 UR - https://doi.org/10.1109/tnse.2025.3649720 DA - 2025-12-31 AU - Chen, Biwen C1 - College of Computer Science, Chongqing University, Chongqing, China AU - Guan, Qiuling C1 - College of Computer Science, Chongqing University, Chongqing, China AU - Li, Jia C1 - College of Computer Science, Chongqing University, Chongqing, China AU - Wang, Jing C1 - School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China AU - He, Jialing C1 - College of Computer Science, Chongqing University, Chongqing, China AU - He, Debiao C1 - School of Cyber Science and Engineering, Wuhan University, Wuhan, China AU - Xiang, Tao C1 - College of Computer Science, Chongqing University, Chongqing, China KW - Credential KW - Authentication (law) KW - Scheme (mathematics) KW - Replay attack KW - Revocation KW - Mathematical proof KW - Identity (music) VL - 13 SP - 5752 EP - 5767 ER - TY - JOUR TI - Enhancing Urban Air Mobility Integration in Cargo Transportation through Tiltrotor Technology PY - 2023 T2 - Preprints.org DO - 10.20944/preprints202310.0413.v1 UR - https://doi.org/10.20944/preprints202310.0413.v1 DA - 2023-10-08 AU - Adebimpe, Saheed C1 - Kwara state university , malete , Kwara state , Nigeria; C1 - Kwara state university, malete, Kwara state, Nigeria; LA - en KW - Payload (computing) KW - Engineering KW - Transport engineering KW - Computer science KW - Computer security AB - The burgeoning field of Urban Air Mobility (UAM) promises transformative solutions to urban transportation challenges, with the potential to revolutionize cargo logistics in metropolitan areas. This research investigates the role of tiltrotor technology as a pivotal enabler of seamless UAM integration for efficient cargo transportation within urban environments. The study delves into the existing challenges within UAM, such as infrastructure limitations and the need for adaptable aerial vehicles that can navigate confined spaces while maintaining payload capacity. Tiltrotor Unmanned Aerial Vehicles (UAVs) are examined as a solution to these challenges due to their unique capabilities, combining vertical takeoff and landing (VTOL) with efficient forward flight. This research evaluates the potential of tiltrotor UAVs for urban cargo transportation, focusing on their operational efficiency, safety, and economic viability. Furthermore, the study explores the potential impact of tiltrotor technology on urban cargo logistics, considering factors such as reduced congestion, shorter delivery times, and environmentally sustainable operations. Real-world case studies and practical implementation strategies are discussed, shedding light on the importance of integration of tiltrotor UAVs into existing cargo transportation networks.The findings of this research underscore the transformative potential of tiltrotor technology in enhancing UAM for cargo transportation within urban areas. The study contributes valuable insights to the ongoing discourse on UAM integration and lays the groundwork for future developments in autonomous aerial cargo delivery systems. ER - TY - DATA TI - "Energy-Efficient Trajectory Planning for Multirotor UAVs With Time-Varying Mass: Mass-Augmented A* and Hybrid DP\u2013NLP Speed Optim" PY - 2025 T2 - IEEE DataPort DO - 10.21227/tbrx-vy83 UR - https://doi.org/10.21227/tbrx-vy83 DA - 2025-12-22 AU - Hua, Zhaoyuan KW - Multirotor KW - Payload (computing) KW - Trajectory KW - Control theory (sociology) KW - Energy consumption KW - Trajectory optimization KW - Nonlinear programming KW - Optimal control KW - Energy (signal processing) KW - Motion planning AB - "The large-scale deployment of multirotor delivery UAVs is fundamentally constrained by limited battery endurance. In practical logistics missions, discrete payload changes during parcel delivery result in time-varying vehicle mass, which in turn affects both vehicle dynamics and power consumption. However, most existing planners assume constant mass, leading to trajectories that are energy-inefficient and difficult for low-level controllers to track accurately. This paper presents a payload-aware trajectory planning and energy optimization framework for multirotor UAVs. The proposed method employs a hierarchical architecture: the front-end utilizes a mass-augmented A* algorithm to search a four-dimensional position\u2013mass state space, using energy consumption as the edge cost to obtain a globally optimal geometric path. The back-end features a hybrid dynamic programming (DP) and nonlinear programming (NLP) trajectory generator that optimizes the speed profile along the path under strict thrust-sphere constraints. This formulation accounts for gravity, inertial forces, and aerodynamic drag, producing smooth and dynamically feasible space\u2013time trajectories. High-fidelity software-in-the-loop (SITL) simulations based on PX4 and RflySim demonstrate that, compared to conventional geometric shortest-path methods, the proposed framework reduces total mission energy by 9.4%\u20139.6% in urban and mountainous delivery scenarios. Additionally, it reduces the mean tracking error to approximately 0.8 m (sub-meter level), showing significant improvements in both energy efficiency and operational safety for UAV logistics operations." ER - TY - JOUR TI - Enhancing Urban Air Mobility Integration in Cargo Transportation through Tiltrotor Technology PY - 2023 DO - 10.31224/3275 UR - https://doi.org/10.31224/3275 DA - 2023-10-09 AU - Adebimpe, Saheed C1 - kwara state university LA - en KW - Business KW - Transport engineering KW - Environmental planning KW - Environmental science KW - Engineering AB - The burgeoning field of Urban Air Mobility (UAM) promises transformative solutions to urban transportation challenges, with the potential to revolutionize cargo logistics in metropolitan areas.This research investigates the role of tiltrotor technology as a pivotal enabler of seamless UAM integration for efficient cargo transportation within urban environments.The study delves into the existing challenges within UAM, such as infrastructure limitations and the need for adaptable aerial vehicles that can navigate confined spaces while maintaining payload capacity.Tiltrotor Unmanned Aerial Vehicles (UAVs) are examined as a solution to these challenges due to their unique capabilities, combining vertical takeoff and landing (VTOL) with efficient forward flight.This research evaluates the potential of tiltrotor UAVs for urban cargo transportation, focusing on their operational efficiency, safety, and economic viability.Furthermore, the study explores the potential impact of tiltrotor technology on urban cargo logistics, considering factors such as reduced congestion, shorter delivery times, and environmentally sustainable operations.Realworld case studies and practical implementation strategies are discussed, shedding light on the importance of integration of tiltrotor UAVs into existing cargo transportation networks.The findings of this research underscore the transformative potential of tiltrotor technology in enhancing UAM for cargo transportation within urban areas.The study contributes valuable insights to the ongoing discourse on UAM integration and lays the groundwork for future developments in autonomous aerial cargo delivery systems. ER - TY - GEN TI - Shipment delivery challenges using unmanned aerial vehicles PY - 2022 DO - 10.59478/atct.2022.22 UR - https://doi.org/10.59478/atct.2022.22 DA - 2022-05-01 AU - Kosovac, Amel C1 - Faculty of Traffic and Communications , University of Sarajevo , Zmaja od Bosne 89 , Sarajevo 71000 , Bosnia and Herzegovina C1 - Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 89, Sarajevo 71000, Bosnia and Herzegovina AU - Šabić, Muharem C1 - Faculty of Traffic and Communications , University of Sarajevo , Zmaja od Bosne 89 , Sarajevo 71000 , Bosnia and Herzegovina C1 - Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 89, Sarajevo 71000, Bosnia and Herzegovina AU - Muharemović, Ermin C1 - Faculty of Traffic and Communications , University of Sarajevo , Zmaja od Bosne 89 , Sarajevo 71000 , Bosnia and Herzegovina C1 - Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 89, Sarajevo 71000, Bosnia and Herzegovina AU - Šimić, Edvin C1 - Faculty of Traffic and Communications , University of Sarajevo , Zmaja od Bosne 89 , Sarajevo 71000 , Bosnia and Herzegovina C1 - Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 89, Sarajevo 71000, Bosnia and Herzegovina LA - en KW - Drone KW - Plan (archaeology) KW - Service (business) KW - Aeronautics KW - Independence (probability theory) KW - Computer science KW - Transport engineering KW - Engineering KW - Business SP - 161 EP - 166 AB - A UAV (Unmanned Aerial Vehicle) is an unmanned aerial vehicle or aircraft that can be monitored remotely or flown independently using a pre-programmed flight plan. Simply, a drone is a selfpropelled vehicle heavier than air. From their beginnings and first forms to modern drones as we know them today, people have discovered many areas for their application. As the power and carrying capacity of drones has grown, so does the possibility of using them for different purposes such as logistics and postal services. The main advantage of drones in urban areas is the independence from road infrastructure and fast delivery, but several challenges need to be addressed for this type of delivery to be competitive with other modes. Many projects have dealt with this topic, but none of them has widespread use to this day. In this paper, we will present the basic challenges that arise in the use of drones for shipment delivery. The paper will present activities that should be prioritized to make a competitive type of service. The paper will present also the advantages over other types of shipment delivery and the current limitation with a future possible research area. ER - TY - JOUR TI - A Bayesian trust-based hybrid truck-drone coalition framework for resilient and sustainable last-mile delivery PY - 2026 PB - Springer Science+Business Media SN - 0254-5330 T2 - Annals of Operations Research DO - 10.1007/s10479-026-07243-5 UR - https://doi.org/10.1007/s10479-026-07243-5 DA - 2026-05-05 AU - Mahmoodi, Armin C1 - Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada AU - Sajadi, Seyed C1 - Operations and Service Management Department, Aston Business School, Aston University, Birmingham, UK AU - Easa, Said C1 - Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada LA - en KW - Control reconfiguration KW - Resilience (materials science) KW - Sorting KW - Cooperative game theory KW - Service (business) KW - Reliability (semiconductor) KW - Markov decision process KW - Pooling KW - Service provider KW - Game theory AB - Abstract This study proposes a novel hybrid Truck-Drone-Coalition (TDC) framework to enhance the resilience and sustainability of last-mile urban delivery systems. The model optimizes four conflicting objectives simultaneously: total delivery cost, service time, operational risk, and unmanned aerial vehicles (UAVs, also called drones) energy consumption. To address behavioral uncertainty among coalition partners, we integrate a Bayesian trust update mechanism with a Markov transition process, enabling dynamic adjustment of cooperation levels and automatic activation of fallback outsourcing when trust thresholds are breached. The solution combines an exact Mixed-Integer Linear Programming (MILP) core with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) metaheuristic to ensure feasible routing, coalition stability, and robust Pareto-optimal trade-offs. Validated using real-world urban scenarios (Solomon benchmarks and commercial UAV specifications), the framework demonstrates: (a) 18% fewer delivery disruptions compared to static trust models, (b) 94% on-time delivery rate under high uncertainty (an 11% improvement over the base case), and (c) 9% lower costs and 12% higher energy efficiency through adaptive coalition reconfiguration. Simulation results demonstrate that the Bayesian trust mechanism effectively detects misbehavior, reallocates tasks via third-party outsourcing, and sustains service reliability under coalition instability or UAV breakdowns. Additionally, dynamic Shapley cost-sharing ensures fair cost allocation among coalition members. By integrating trust modeling, cooperative game theory, and risk-aware controls, this research bridges a critical gap in hybrid truck-drone logistics. The findings provide actionable insights for logistics managers seeking cost-effective, flexible, and resilient last-mile solutions. Future work will focus on real-time trust evaluation and dynamic coalition reconfiguration using live operational data. ER - TY - JOUR TI - Distributed formation control of unmanned aerial vehicles via a decoupled linear-quadratic differential game PY - 2026 PB - Elsevier BV SN - 0967-0661 T2 - Control Engineering Practice DO - 10.1016/j.conengprac.2026.106876 UR - https://doi.org/10.1016/j.conengprac.2026.106876 DA - 2026-03-02 AU - Hu, Jinghe C1 - School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China AU - Xian, Bin C1 - School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China AU - Shao, Peng C1 - NXP Semiconductors (Tianjin) Ltd, Tianjin, 300385, China LA - en KW - Control theory (sociology) KW - Robustness (evolution) KW - Bounded function KW - Controller (irrigation) KW - Heuristics KW - Collision avoidance KW - Optimal control KW - Stability (learning theory) KW - Vehicle dynamics VL - 172 SP - 106876 EP - 106876 ER - TY - JOUR TI - Enhancing Information Resources for Urban Freight Management through Image Processing and Analysis PY - 2026 PB - De Gruyter Open SN - 1407-6160 T2 - Transport and Telecommunication Journal DO - 10.2478/ttj-2026-0016 UR - https://doi.org/10.2478/ttj-2026-0016 DA - 2026-04-01 AU - Kujawski, Artur C1 - Faculty of Engineering and Economics of Transport , Maritime University of Szczecin Szczecin , Poland , Wały Chrobrego 1-2, 70-500 AU - Dud, Tomasz C1 - Faculty of Engineering and Economics of Transport , Maritime University of Szczecin Szczecin , Poland , Wały Chrobrego 1-2, 70-500 AU - Nürnberg, Mariusz C1 - Faculty of Engineering and Economics of Transport , Maritime University of Szczecin Szczecin , Poland , Wały Chrobrego 1-2, 70-500 LA - en KW - Image processing KW - Traffic management KW - Data processing KW - Tracking (education) KW - Public transport KW - Information processing KW - Traffic flow (computer networking) KW - Reduction (mathematics) KW - Volume (thermodynamics) VL - 27 IS - 2 SP - 214 EP - 225 AB - Abstract The issue of incomplete data on the volume and types of freight transport in urban areas is well known, leading to difficulties in managing transport processes and hindering the development of strategic approaches for modern cities. To improve the management of freight flows, it is essential to distinguish freight vehicles from private and public transport. This paper investigates and develops methods for vehicle detection and tracking using cameras mounted on unmanned aerial vehicles (UAVs), aiming to enhance urban logistics data through image processing and analysis algorithms. A multi-camera vehicle detection and tracking algorithm was developed to support the analysis and evaluation of traffic flow efficiency in urban logistics, ultimately contributing to improved transportation management aligned with emission reduction principles. ER - TY - THES TI - Uso de drones en logística para entrega de mercancías PY - 2015 DA - 2015-11-30 AU - Campos, Romero AU - Andrés, Jairo LA - es KW - Drone KW - Aeronautics KW - Relevance (law) KW - Amazon rainforest KW - Engineering KW - Computer science KW - Political science ER - TY - GEN TI - XGBoost-XAILOG: Interpretable Machine Learning for UAV Delivery Time Prediction in the Low-Altitude Economy via Dual-Layer SHAP and LIME Explanation PY - 2026 DO - 10.1145/3813822.3814188 UR - https://doi.org/10.1145/3813822.3814188 DA - 2026-03-20 AU - Ke, Chao C1 - Kashi University, Kashi, Xinjiang, China AU - Xie, Xin C1 - Zhangzhou Institute of Technology, Zhangzhou, Fujian, China AU - Abudukeremu, Abudureyimu C1 - Kashi University, Kashi, Xinjiang, China KW - Scheduling (production processes) KW - Train KW - Boosting (machine learning) KW - Predictive modelling KW - Key (lock) KW - Gradient boosting KW - Heuristic KW - Nonlinear system SP - 2297 EP - 2302 AB - Accurate and interpretable delivery time prediction is a critical enabler for operational efficiency and dispatcher decision-making in low-altitude economy UAV logistics systems. However, existing approaches suffer from three key limitations: (1) conventional scheduling methods rely on heuristic rules that fail to capture the nonlinear interactions among flight distance, cargo weight, weather, and urban congestion; (2) data-driven models such as gradient boosting ensembles improve predictive accuracy but remain opaque black boxes, providing no actionable insight to logistics operators; and (3) current explainability techniques are applied in isolation, offering either global population-level attribution or local sample-level justification, but rarely both in a unified framework. To address these challenges, we propose XGBoost-XAILOG—an interpretable machine learning framework for UAV delivery time prediction in low-altitude urban airspace. Our framework introduces three tightly integrated components: (1) a regularised XGBoost regression model with second-order split-gain optimisation for robust nonlinear delivery time estimation; (2) a global SHAP attribution layer based on the exact TreeSHAP algorithm for population-wide feature importance ranking; and (3) a local LIME explanation layer using proximity-weighted perturbation sampling for per-order decision transparency. Experiments on a 2,000-sample real-world UAV logistics dataset demonstrate that XGBoost-XAILOG achieves strong predictive accuracy while revealing that flight distance dominates delivery delay, with instance-level sign reversals of hub proximity and weather validated across diverse operational scenarios. ER - TY - GEN TI - Optimization of UAV Distribution Route under Carbon Tax Constraints--An Empirical Study of Hankou North Based on Improved Grey Wolf Algorithm PY - 2025 DO - 10.1145/3772726.3772827 UR - https://doi.org/10.1145/3772726.3772827 DA - 2025-08-22 AU - Li, Silan C1 - Center for Service Science and Engineering, Wuhan University of Science & Technology, Wuhan, Hubei, China C1 - School of Management, Wuhan University of Science & Technology, Wuhan, Hubei, China AU - Qin, Wei C1 - School of Management, Wuhan University of Science & Technology, Wuhan, Hubei, China KW - Convergence (economics) KW - Carbon tax KW - Empirical research KW - Distribution (mathematics) KW - Optimization algorithm KW - Transformation (genetics) KW - Order (exchange) KW - Resource (disambiguation) SP - 644 EP - 650 AB - Green intelligent logistics is becoming the direction of urban logistics system transformation under the guidance of the goal of "carbon peak, carbon neutral". In order to achieve low carbon and efficient terminal distribution goal, this paper focuses on the optimization of multi-model UAV collaborative distribution route under carbon tax constraints. Specifically, this paper constructs a multi-objective mathematical model, which takes the minimum total cost as the core objective.Besides, an improved grey wolf optimization algorithm (MP-GWO) is proposed in this paper. In order to verify the validity of MP-GWO algorithm and model, this paper selects Hankou North Business Circle as the research object, and carries out several sets of simulation experiments. Experimental results show that MP-GWO algorithm performs better than traditional algorithm in convergence speed and cost control. Moreover, the optimization results are better than single model distribution scheme in terms of total cost, carbon emission and resource utilization. ER - TY - JOUR TI - INTELLIGENT DRONE ROUTE PLANNING IN URBAN ENVIRONMENTS: OPTIMIZATION AND SAFETY PY - 2026 SN - 2707-9031 T2 - Scientific Journal of Astana IT University DO - 10.37943/25onqv4873 UR - https://doi.org/10.37943/25onqv4873 DA - 2026-03-30 AU - Seksembayeva, Manara AU - Amangaliyev, Zhaksylyk AU - Anuarbekov, Miras LA - en KW - Geospatial analysis KW - Drone KW - Context (archaeology) KW - Route planning KW - Routing (electronic design automation) KW - Obstacle KW - Solver KW - Trajectory AB - This study develops and evaluates an intelligent route-planning model for unmanned aerial vehicles operating in urban environments with the objective of minimizing total flight time while maintaining flight safety. The proposed approach addresses a practical last-mile delivery scenario in a smart-city context and is implemented using Google OR-Tools within a constrained Traveling Salesman Problem framework. Open geospatial data from OpenStreetMap are used to obtain building geometries, street topology, and available height-related attributes, while meteorological data from OpenWeatherMap are used to account for wind conditions in flight-time estimation. Safe cruising altitude is determined from the maximum surrounding obstacle height with an additional safety margin. If explicit building-height data are unavailable, height is approximated from the number of building levels. A prototype system was developed to visualize routes, estimate route distance, flight time, and delivery cost, and export missions in MAVLink/QGroundControl-compatible format. Experimental scenarios for real delivery points in the Astana metropolitan area, including Astana, Kosshy, and Koyandy demonstrate the practical feasibility of the proposed open-data-driven routing approach and show improvements over a baseline sequential routing strategy. The scientific contribution of the study lies in the integration of open geospatial building data, weather-aware flight-time estimation, and safety-oriented altitude selection into a reproducible urban UAV route-planning framework. The proposed method can support the development of safe and cost-efficient drone delivery services in dense urban environments. Experimental scenarios with increasing numbers of delivery points demonstrated that the proposed method remains computationally efficient, with sub-second solver runtime for the tested cases for practical smart-city logistics applications. ER - TY - JOUR TI - AeroMedic Pro:An Autonomous UAV Platform for Emergency Medical Delivery Using Machine Learning-Based Navigation and Obstacle Avoidance PY - 2025 PB - Lectito Journals SN - 2468-4376 T2 - Journal of Information Systems Engineering & Management DO - 10.52783/jisem.v10i51s.10408 UR - https://doi.org/10.52783/jisem.v10i51s.10408 DA - 2025-05-30 AU - Patankar, Sanika C1 - Vishwakarma Institute of Technology Pune , Maharashtra , India LA - en KW - Obstacle avoidance KW - Obstacle KW - Computer science KW - Collision avoidance KW - Artificial intelligence KW - Human–computer interaction KW - Aeronautics KW - Real-time computing KW - Engineering KW - Computer security KW - Robot KW - Mobile robot KW - Geography VL - 10 IS - 51s SP - 426 EP - 433 AB - Introduction: In many areas, the emergency medical response networks are harmed by obstructions like congestion, harsh terrains, or stocks failures in particular after natural calamities or in remote areas. Daily operations mean that traditional approaches are usually adequate, but in emergency situations when time is critical, they tend to be quite lacking as they are often too slow. Hence, the immediate need is the development of intelligent self-ruling systems intended to support quick medical assistance in emergencies. The planning for AeroMedic Pro strives to fill the gap, providing a bright solution of smart UAV delivery for rapid transport of critical medical supplies in urban and even remote places. In this paper, we document the design, construction, and live implementation of this groundbreaking platform. Objectives: One of the central goals of the AeroMedic Pro project was to develop autonomous aerial vehicles that would greatly improve emergency healthcare. The objective was to design a lightweight, high-performing UAV with advanced navigation systems that function to some considerable extent in varying and mostly challenging environments. The mission was to establish a delivery system which significantly increases a general speed of transport and effectively functions in places where typical means of transport can’t access it, mainly within GPS challenged or highly populated urban air-space. Among the most important priority goals of the project were delivery speed improvement of more than 50%, superior obstacle detection and avoidance, as well as preservation of the safety of fragile cargo such as blood, vaccines, and high value pharmaceutical products. Methods: In order to achieve its great goals for the future, the AeroMedic Pro system has been developed with state-of-the-art technological elements. The bottom of the system involves a navigation module featuring machine learning, to fuse real-time sensor and reinforcement learning algorithms. LiDAR and IMUs collect spatial data that the system transforms in real-time to assist presentation for the UAV’s intelligent navigation. Consequently, the system is able to respond in real time to the changes in the environment and overcome obstacles; it is particularly useful in the environments that are unpredictable or GPS-inaccessible. The system uses a Convolutional Neural Network (CNN) for object detection and so helps the UAV to detect and avoid any obstacles in flight. Rectangular wings and fuselage were shaped for strength while keeping weight to a minimum with high stability, making use of advanced composites to increase payload by as much as 400 percent. Several operational scenarios have been employed to examine the adaptability of the system to different environmental conditions, obstacle levels and limitations on the mission. Results: AeroMedic Pro’s reliability was confirmed by rigorous testing in real life conditions. The UAV could convey the medical supplies to the challenging environment and heavy traffic destinations 60% faster than the “mainstream” delivery methods behind-based systems are. The UAV achieved a success rate of 98.3% in obstacle-intensive test environments at real-time detection and avoidance of static and dynamic obstacles. Furthermore, in places where GPS signals were unavailable, the system proved to demonstrate extraordinary abilities to navigate with inbuilt intelligence to avoid position and course loss. While in transit, the medical payload was excellently protected without interfering with the quality of medical service delivered and the UAV showed robust behavior in terms of taking-off, cruising and landing performances under a wide spectrum of environmental conditions including wind, rain and varied terrain. High throughput levels were observed through out thus making it easy to adapt the framework to coordinated deployment of multiple drones in future. Conclusions:AeroMedic Pro is an evolution of great steps for autonomous modes of medical transport. Through integrating state-of-the-art machine learning algorithms, data-driven sensing and optimized aerodynamic design, AeroMedic Pro provides reliable and effective supply of critical medical equipment on emergencies. Apart from speeding up and ensuring a more accurate delivery of products, the UAV addresses major problems regarding access to healthcare in severely resourced-poor regions. The success of the system in operationalizing in GPS blocked areas has demonstrated its merits towards supporting disaster relief in urban settings and necessary healthcare in rural territories. The team intends to upgrade the system to promote fleet coordination, use blockchain for secure payload monitoring and develop regulatory framework to encourage public usage of airspace. AeroMedic Pro is bound to bring one radical change to the global emergency healthcare logistics with continuous improvements. ER - TY - JOUR TI - Energy Management for a Fuel Cell Hybrid-Powered Unmanned Aerial Vehicle Based on Optimal Path Planning PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 1996-1073 T2 - Energies DO - 10.3390/en19081854 UR - https://doi.org/10.3390/en19081854 DA - 2026-04-09 AU - Ji, Yong C1 - School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China AU - Ling, Xingpeng C1 - School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China AU - Wu, Xiaojuan C1 - School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China AU - Hu, Jiangping C1 - School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China LA - en KW - Energy management KW - Energy consumption KW - Path (computing) KW - Power (physics) KW - Motion planning KW - Wind power KW - Ant colony optimization algorithms KW - Energy (signal processing) KW - Fuel efficiency VL - 19 IS - 8 SP - 1854 EP - 1854 AB - Unmanned Aerial Vehicles (UAVs) present a promising solution for urban logistics, where an effective energy management strategy guided by optimal path planning is crucial for reducing operational costs and extending system lifespan. This study begins by analyzing the wind field distribution in a specific urban area of Chengdu using Computational Fluid Dynamics, and establishes a data-driven power prediction model to evaluate UAV energy consumption. A hybrid wind-field-aware A* with Ant Colony Optimization algorithm is subsequently proposed to compute the optimal flight path that balances energy consumption and distance, generating corresponding power demand profiles for the ensuing energy management strategy. Finally, a Deep Q-Learning (DQN)-based energy management strategy is implemented to regulate power distribution between the fuel cell and the battery, aiming to minimize hydrogen consumption and stabilize the power output of the primary source. Experimental results demonstrate that the proposed path planning method can effectively reduce energy consumption across different scenarios while causing only a marginal increase in travel distance. In addition, the DQN-based strategy significantly suppresses fuel cell power fluctuations at the cost of only a slight increase in hydrogen consumption, thereby demonstrating the effectiveness of the path-planning-informed energy management strategy. ER - TY - JOUR TI - Analysis of multi-modal energy supply and power systems adaptability for eVTOL and UAV applications in the low-altitude economy PY - 2026 PB - Elsevier BV SN - 2590-1168 T2 - eTransportation DO - 10.1016/j.etran.2026.100626 UR - https://doi.org/10.1016/j.etran.2026.100626 DA - 2026-07-28 AU - Fan, Peixiao C1 - Department of Electrical and Electronic Engineering, Research Centre for Grid Modernization, The Hong Kong Polytechnic University, Hong Kong SAR, China AU - Bu, Siqi C1 - Department of Electrical and Electronic Engineering, Research Centre for Grid Modernization, The Hong Kong Polytechnic University, Hong Kong SAR, China AU - Zhu, Mingshuo C1 - Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China AU - Wen, Yuxin C1 - Southern Power Grid Research Institute Co., Ltd, Guangzhou, China AU - Li, Shuangqi C1 - Department of Electrical and Electronic Engineering, Research Centre for Grid Modernization, The Hong Kong Polytechnic University, Hong Kong SAR, China AU - Zhang, Guoqing C1 - Department of Electrical and Electronic Engineering, Research Centre for Grid Modernization, The Hong Kong Polytechnic University, Hong Kong SAR, China AU - Zhang, Chenxuan C1 - Department of Electrical and Electronic Engineering, Research Centre for Grid Modernization, The Hong Kong Polytechnic University, Hong Kong SAR, China LA - en KW - Adaptability KW - Energy supply KW - Software deployment KW - Key (lock) KW - Energy consumption KW - Takeoff KW - Reliability (semiconductor) KW - Grid KW - Electricity generation VL - 29 SP - 100626 EP - 100626 ER - TY - JOUR TI - Drag-aware path planning for unmanned aerial vehicles in dynamic urban environments PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6072486 UR - https://doi.org/10.2139/ssrn.6072486 DA - 2026-01-01 AU - Lee, Hojin C1 - Inha University C1 - Inha University, 253 Yonghyun-dong, Nam-gu Incheon 402-751, Korea, Republic of (South Korea) AU - Puri, Rishabh C1 - Karlsruhe Institute of Technology AU - Sarma, Rakesh C1 - Independent - affiliation not provided to SSRN AU - Lintermann, Andreas C1 - Independent - affiliation not provided to SSRN AU - Lee, Sangseung C1 - Inha University C1 - Inha University, 253 Yonghyun-dong, Nam-gu Incheon 402-751, Korea, Republic of (South Korea) AU - Rüttgers, Mario C1 - RWTH Aachen University C1 - RWTH Aachen University, Templergraben 55, 52056 Aachen, 52056, Germany KW - Motion planning KW - Path (computing) KW - Trajectory KW - Aerodynamics KW - Work (physics) KW - Graph KW - Flow (mathematics) KW - Field (mathematics) KW - Urban planning ER - TY - JOUR TI - Unmanned Aerial Vehicles in Logistics: Efficiency Gains and\n Communication Performance of Hybrid Combinations of Ground and Aerial\n Vehicles PY - 2019 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.1910.10451 UR - https://doi.org/10.48550/arxiv.1910.10451 DA - 2019-10-23 AU - Patchou, Manuel AU - Sliwa, Benjamin AU - Wietfeld, Christian LA - en KW - Drone KW - Truck KW - Base station KW - Computer science KW - Context (archaeology) KW - Popularity KW - Wireless KW - Computer network KW - Intelligent transportation system KW - Routing (electronic design automation) KW - Transport engineering KW - Telecommunications KW - Engineering KW - Automotive engineering AB - Unmanned Aerial Vehicles (UAVs) have drastically gained popularity in various\nIntelligent Transportation System (ITS) applications to improve the safety and\nefficiency of transportation systems. In this context, the combination of\nground vehicles, such as delivery trucks, with drones to assist in the last\nmile pick-up and delivery of the parcels has been recently proposed. While\naerial vehicles promise increased efficiency based on flexible routes and\nparallelized operation, highly reliable wireless communication is also required\nfor the control and coordination of potentially many drones acting in a\nself-organized way. In this paper, we analyze the improvements procured by\ndrone usage in parcel delivery compared to traditional delivery and propose a\nsimulation framework to further quantify the efficiency gains of the parcel\ndelivery logistics and to analyze the performance of different wireless\ncommunications options. To this end, we consider a heterogeneous vehicle\nrouting problem with various constraints. We consider two approaches regarding\nthe dispatching and recovery of drones and evaluate their benefits as opposed\nto parcel delivery with a classic truck only. Furthermore, we compare two\nnetworking technologies for enabling coordination of the self-organizing teams\nof drones with a realistically modeled environment: one approach relying on\nbase station oriented Long Term Evolution (LTE) vs. a more decentralized\nCellular Vehicle-to-Everything (C-V2X) solution. The results show time savings\nof nearly 40% can be achieved through drone usage and that the negative impact\nof urban shadowing on network communications in the base station oriented LTE\napproach can be compensated by leveraging decentralized C-V2X communications\n ER - TY - JOUR TI - A Cooperative UAV-Truck Cyber-Physical System for Risk-Aware ContainerLogistics: A V2X-Enabled Bi-Level Optimization Framework PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6161433 UR - https://doi.org/10.2139/ssrn.6161433 DA - 2026-01-01 AU - Ndikuriyo, Yves C1 - Central South University C1 - Central South University, Jiangwan Building, Changsha, Hunan, China AU - Zhang, Yinggui C1 - Central South University C1 - Central South University, Changsha, 410083, China AU - Fom, Dung KW - Drone KW - Resilience (materials science) KW - Component (thermodynamics) KW - Truck KW - Latency (audio) KW - Service (business) KW - Systems architecture KW - Cellular network KW - Routing (electronic design automation) ER - TY - JOUR TI - High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements PY - 2024 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2408.04205 UR - https://doi.org/10.48550/arxiv.2408.04205 DA - 2024-08-08 AU - Chen, Xinwei AU - Zhong, Xiaofeng AU - Zhang, Zijian AU - Dai, Linglong AU - Zhou, Shidong LA - en KW - Estimation KW - Computer science KW - Engineering AB - Recent widespread applications for unmanned aerial vehicles (UAVs) -- from infrastructure inspection to urban logistics -- have prompted an urgent need for high-accuracy three-dimensional (3D) radio maps. However, existing methods designed for two-dimensional radio maps face challenges of high measurement costs and limited data availability when extended to 3D scenarios. To tackle these challenges, we first build a real-world large-scale 3D radio map dataset, covering over 4.2 million m^3 and over 4 thousand data points in complex urban environments. We propose a Gaussian Process Regression-based scheme for 3D radio map estimation, allowing us to realize more accurate map recovery with a lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance data efficiency, we propose two methods for training point selection, including an offline clustering-based method and an online maximum a posterior (MAP)-based method. Extensive experiments demonstrate that the proposed scheme not only achieves full-map recovery with only 2% of UAV measurements, but also sheds light on future studies on 3D radio maps. ER - TY - JOUR TI - Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions PY - 2025 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2504.21583 UR - https://doi.org/10.48550/arxiv.2504.21583 DA - 2025-04-30 AU - Wang, Yixian AU - Sun, Geng AU - Sun, Zemin AU - Wang, Jiacheng AU - Li, Jiahui AU - Zhao, Changyuan AU - Jing, Wu AU - Liang, Shuang AU - Yin, Minghao AU - Wang, Pengfei AU - Niyato, Dusit AU - Sun, Sumei AU - Kim, Dong LA - en KW - Key (lock) KW - Takeoff KW - Vulnerability (computing) KW - Core (optical fiber) KW - Emerging technologies KW - Sustainability KW - Drone AB - The rise of the low-altitude economy (LAE) is propelling urban development and emerging industries by integrating advanced technologies to enhance efficiency, safety, and sustainability in low-altitude operations. The widespread adoption of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft plays a crucial role in enabling key applications within LAE, such as urban logistics, emergency rescue, and aerial mobility. However, unlike traditional UAV networks, LAE networks encounter increased airspace management demands due to dense flying nodes and potential interference with ground communication systems. In addition, there are heightened and extended security risks in real-time operations, particularly the vulnerability of low-altitude aircraft to cyberattacks from ground-based threats. To address these, this paper first explores related standards and core architecture that support the development of LAE networks. Subsequently, we highlight the integration of technologies such as communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management. This synergy of multi-technology drives the advancement of real-world LAE applications, particularly in improving operational efficiency, optimizing airspace usage, and ensuring safety. Finally, we outline future research directions for LAE networks, such as intelligent and adaptive optimization, security and privacy protection, sustainable energy and power management, quantum-driven coordination, generative governance, and three-dimensional (3D) airspace coverage, which collectively underscore the potential of collaborative technologies to advance LAE networks. ER - TY - JOUR TI - An Empirical Study of Drone Medical Logistics Transportation in a Multi-Campus Model of Chinese Public Hospitals: Real-World Data-Driven Validation of Timeliness and Application Effects PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5190068 UR - https://doi.org/10.2139/ssrn.5190068 DA - 2025-01-01 AU - Long, Xingbo AU - Huang, Min AU - Xie, Xiankai AU - Wang, Yanxin AU - Yu, Xiaojiang AU - Zhou, Wei AU - Hu, Bo LA - en KW - Drone KW - Business KW - Empirical research KW - Transport engineering KW - Computer science KW - Engineering ER - TY - GEN TI - Urban Wind Field Simulation for Flight Safety and Unmanned Aerial Vehicle Flight Path Planning PY - 2026 PB - Springer Science+Business Media SN - 1876-1100 T2 - Lecture notes in electrical engineering DO - 10.1007/978-981-95-6813-0_36 UR - https://doi.org/10.1007/978-981-95-6813-0_36 DA - 2026-01-01 AU - Gao, Xiaochao C1 - School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, 518107, Guangdong, China AU - Xu, Chunguang C1 - School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, 518107, Guangdong, China AU - Wang, Guangxue C1 - School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen, 518107, Guangdong, China AU - Wang, Aihua C1 - MAKER Technology Co., LTD., Mianyang, 621000, Sichuan, China LA - en KW - Flight planning KW - Motion planning KW - Field (mathematics) KW - Path (computing) KW - Trajectory SP - 360 EP - 368 ER - TY - GEN TI - Urban Roads and Aerial Autonomy: Are Drones Safe Above Busy Roads? PY - 2025 DO - 10.1109/mass66014.2025.00124 UR - https://doi.org/10.1109/mass66014.2025.00124 DA - 2025-10-06 AU - Das, Papiya C1 - Univ. of Florence,Dept. of CS and Math.,Italy AU - Sorbelli, Francesco C1 - Univ. of Perugia,Dept. of CS and Math.,Italy AU - Chatterjee, Punyasha C1 - Jadavpur University,School of Mobile Comp. and Comm.,India AU - Pinotti, Cristina C1 - Univ. of Perugia,Dept. of CS and Math.,Italy KW - Drone KW - Aviation safety KW - Work (physics) KW - Aviation KW - Poison control KW - Control (management) SP - 759 EP - 764 ER - TY - GEN TI - Assessment and deployment of bird-carried meteorological sensors for microclimate measurements in urban terrain PY - 2018 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.7741665 UR - https://doi.org/10.5281/zenodo.7741665 DA - 2018-04-08 AU - Thomas, Rick C1 - University of Birmingham, UK C1 - Geography AU - Cropley, Ford C1 - University of Birmingham, UK C1 - Geography AU - MacKenzie, Angus C1 - University of Birmingham, UK C1 - Earth and Environmental Sciences AU - Reynolds, S. C1 - University of Birmingham, UK C1 - Biosciences AU - Sadler, Jon C1 - University of Birmingham, UK C1 - Geography AU - Chapman, Lee C1 - University of Birmingham, UK AU - Quinn, Andrew C1 - University of Birmingham, UK AU - Zhong, Jian C1 - University of Birmingham, UK AU - Schellenberg, Ben C1 - University of Bristol, UK AU - Cai, Xiaoming C1 - University of Birmingham, UK LA - en KW - Microclimate KW - Terrain KW - Software deployment KW - Environmental science KW - Meteorology KW - Remote sensing KW - Geography KW - Cartography KW - Computer science AB - The Cityflocks project has been developing and deploying lightweight, fast-response meteorological sensors to be carried by bird species in urban areas to address the paucity of measurements in the region a few hundred metres above the urban rooftops. Accurate and fast routine temperature measurements in this region will help further our understanding of the above-canopy internal boundary layer structure and the spatial variability of Urban Boundary Layers (Barlow 2014). Data implications include improving forecasting of Urban Heat Island events and urban weather, improving air pollution modelling and informing sustainable urban planning. Other methods of making such measurements routinely, such as using manned aircraft or Unmanned Aerial Vehicles (UAVs), can be prohibitively expensive or require onerous permissions and logistics to achieve. A first-iteration prototype was carried initially by a trained White-tailed Eagle (Haliaeetus albicilla) and an Imperial Eagle (Aquila adalberti) to assess probe-mounting locations (back or tail mounted temperature sensor), other met sensors and the potential for data contamination due to heat generation by birds (Thomas et al. 2017). Other characterisation tests included car-mounted solar radiation shield tests, UAV-mounted tests for altimeter testing and field tests of temperature responses to solar-heated surface elements. We present results demonstrating that packages meet their specified target of measuring at least a 0.5 degree celcius change in temperature over each hundred metres of flight track, as well as accurately recording their location and orientation using on-board GPS, 3-axis gyroscopes and accelerometers, and a pressure altitude sensor. The package has been miniaturised and carried by captive homing pigeons (Columba livia) for initial tests prior to more widespread deployment later in 2018. ER - TY - JOUR TI - Evaluating 5G Networks for U-Space Applications: Insights from Dense Urban Measurement Campaign PY - 2024 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2411.09666 UR - https://doi.org/10.48550/arxiv.2411.09666 DA - 2024-11-14 AU - Ricardo, Barrios-Munoz AU - Matteo, Bernabe AU - López‐Pérez, David AU - David, Gomez-Barquero AU - Israel, Quintanilla-Garcia LA - en KW - Space (punctuation) KW - Computer science KW - Telecommunications AB - Following the burgeoning interest in unmanned aerial vehicles (UAVs) utilization within human-inhabited spaces, critical challenges arise in ensuring reliable, low-latency communication-particularly important given the safety-critical nature of such operations in densely populated urban environments. Therefore, adequate cellular communication capabilities are essential to enable safe and effective operations within the so-called U-Spaces. In this context, this paper investigates the communication performance of cellular-connected UAVs in dense urban environments. In particular, the analysis is based on a comprehensive measurement campaign conducted in the city of Benidorm, Spain-an urban area well known for its high concentration of tall buildings and overall urban density. More specifically, we evaluated key performance indicators (KPIs) related to received signal strength and quality, data rate, and latency across various altitudes, mobile network operators, access technologies, and frequency bands, using multiple types of measurement equipment. The results highlight significant challenges, primarily due to the lack of dedicated planning for aerial coverage and interference management, revealing that current cellular networks may fall short in supporting reliable and ubiquitous UAVs communication. Thus, this paper calls for improved network solutions to ensure the reliability of UAV operations in urban airspace, thereby contributing to the integration of UAVs into urban logistics and mobility. ER - TY - JOUR TI - Toward a Safer Low-Altitude Airspace in Urban Environments: Risk and Airspace Availability in Central Wuhan PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6174722 UR - https://doi.org/10.2139/ssrn.6174722 DA - 2026-01-01 AU - Peng, Chong C1 - Huazhong University of Science and Technology C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China AU - Ding, Yuchen C1 - Huazhong University of Science and Technology C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China AU - Ming, Tingzhen C1 - Wuhan University of Technology C1 - Wuhan University of Technology, Wuhan, Wuhan 430063, China AU - Zhai, Wei C1 - University of Texas at Arlington C1 - University of Texas at Arlington, 415 S West St Apt no 205, Arlington, TX 76019, United States AU - Du, Kejun C1 - Huazhong University of Science and Technology C1 - Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, 430074, China KW - SAFER KW - Visibility KW - Population KW - Service (business) KW - Corporate governance KW - Spatial planning KW - Conceptual model KW - Resilience (materials science) ER - TY - JOUR TI - Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System PY - 2026 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DO - 10.48550/arxiv.2602.12913 UR - https://doi.org/10.48550/arxiv.2602.12913 DA - 2026-02-13 AU - Lei, Songxin AU - Ma, Chunming AU - Wen, Haomin AU - Li, Yexin AU - Chen, Lizhenghe AU - Yang, Qianyu AU - Tsung, Fugee AU - Chen, Lei AU - Ruan, Sijie AU - Liang, Yuxuan KW - Bottleneck KW - Reinforcement learning KW - Scalability KW - ENCODE KW - Action (physics) KW - Component (thermodynamics) KW - Hierarchical control system AB - Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments on two real-world datasets and an evaluation platform demonstrate that HRL4AG significantly outperforms state-of-the-art baselines, improving the delivery success rate by up to 26% while achieving an 80-fold increase in computational efficiency. ER - TY - JOUR TI - Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System PY - 2026 PB - Cornell University SN - 2331-8422 T2 - arXiv (Cornell University) DA - 2026-02-13 AU - Lei, Songxin C1 - The Hong Kong University of Science and Technology (Guangzhou) Guangzhou , China AU - Ma, Chunming C1 - The Hong Kong University of Science and Technology (Guangzhou) Guangzhou , China AU - Wen, Haomin C1 - Carnegie Mellon University Pittsburgh , Pennsylvania USA AU - Li, Yexin C1 - Beijing Institute for General Artificial Intelligence Beijing , China AU - Chen, Lizhenghe C1 - The Hong Kong University of Science and Technology (Guangzhou) Guangzhou , China AU - Yang, Qianyu C1 - Beijing Institute of Technology Beijing , China AU - Tsung, Fugee C1 - The Hong Kong University of Science and Technology Hong Kong SAR , China AU - Chen, Lei C1 - The Hong Kong University of Science and Technology (Guangzhou) Guangzhou , China AU - Ruan, Sijie C1 - Beijing Institute of Technology Beijing , China AU - Liang, Yuxuan C1 - The Hong Kong University of Science and Technology (Guangzhou) Guangzhou , China KW - Bottleneck KW - Reinforcement learning KW - Scalability KW - ENCODE KW - Action (physics) KW - Component (thermodynamics) KW - Hierarchical control system AB - Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments on two real-world datasets and an evaluation platform demonstrate that HRL4AG significantly outperforms state-of-the-art baselines, improving the delivery success rate by up to 26% while achieving an 80-fold increase in computational efficiency. ER - TY - GEN TI - Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System PY - 2026 DO - 10.1145/3770855.3818964 UR - https://doi.org/10.1145/3770855.3818964 DA - 2026-08-06 AU - Lei, Songxin C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China AU - Ma, Chunming C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China AU - Wen, Haomin C1 - Carnegie Mellon University, Pittsburgh, Pennsylvania, USA AU - Li, Yexin C1 - Beijing Institute for General Artificial Intelligence, Beijing, China AU - Chen, Lizhenghe C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China AU - Yang, Qianyu C1 - Beijing Institute of Technology, Beijing, China AU - Tsung, Fugee C1 - The Hong Kong University of Science and Technology, Hong Kong SAR, China AU - Chen, Lei C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China AU - Ruan, Sijie C1 - Beijing Institute of Technology, Beijing, China AU - Liang, Yuxuan C1 - The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China KW - Bottleneck KW - Reinforcement learning KW - Scalability KW - ENCODE KW - Hierarchical control system KW - Action (physics) KW - Component (thermodynamics) KW - Key (lock) SP - 11295 EP - 11306 AB - Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments on two real-world datasets and an evaluation platform demonstrate that HRL4AG significantly outperforms state-of-the-art baselines, improving the delivery success rate by up to 26% while achieving an 80-fold increase in computational efficiency. Our source codes are available at https://github.com/thunderlrr/Cooperative-Air-Ground-Delivery. ER - TY - JOUR TI - Using a deep learning neural network to predict flight path PY - 2025 PB - National Aerospace University – Kharkiv Aviation Institute SN - 1814-4225 T2 - RADIOELECTRONIC AND COMPUTER SYSTEMS DO - 10.32620/reks.2025.1.05 UR - https://doi.org/10.32620/reks.2025.1.05 DA - 2025-02-20 AU - Bezsonov, Oleksandr AU - Liashenko, Serhii AU - Rudenko, Oleg AU - Rutska, Sofiia AU - Vashchenko, Kateryna LA - en KW - Artificial neural network KW - Artificial intelligence KW - Path (computing) KW - Computer science KW - Deep learning KW - Machine learning KW - Computer network VL - 2025 IS - 1 SP - 71 EP - 84 AB - The subject of this paper is a new approach using a deep learning neural network designed for predicting the flight path of an unmanned aerial vehicle (UAV). The purpose of this study was to improve the accuracy of drone flight path prediction by developing a deep learning-based trajectory forecasting model. The task was to collect and prepare a dataset of video and photo materials for training the neural network, develop and implement a deep learning model for trajectory prediction, and enhance UAV flight trajectory forecasting through model optimization and validation. Methods used included the creation of a synthetic dataset using the 3D modeling tool Blender, which enabled the generation of animations representing various drone flight scenarios. These scenarios include different environmental conditions and urban landscapes, providing a robust training ground for the neural network. To further improve and test the model’s predictive capabilities, real-world data, including eyewitness videos, were used. The architecture of the neural network includes long short-term memory (LSTM) units that can process sequential data, making them ideal for predicting dynamic UAV trajectories. The training process involved several stages, starting with pre-training on general visual features and then fine-tuning to UAV-specific motion patterns. The results of this study show that the neural network achieved high accuracy in trajectory prediction, with the model showing better performance in real-world scenarios compared to traditional trajectory prediction methods. The integration of LSTM enabled efficient learning and generalization of temporal data, capturing complex motion patterns and interactions with the environment. This research not only demonstrates the feasibility of using deep learning to predict UAV trajectories but also offers potential applications in civilian security or delivery logistics, where real-time trajectory prediction can significantly improve the efficiency and speed of decision-making. Conclusions. The scientific novelty of the obtained results lies in the development and training of deep learning models specifically designed for predicting drone flight paths. This study demonstrated the effectiveness of the proposed approach by demonstrating its ability to enhance the accuracy of UAV trajectory forecasting. ER - TY - GEN TI - Integrated Sensing, Communications, and Computation Services in Low-Altitude Economy Networks: A Comprehensive Survey PY - 2025 DO - 10.1109/ciotsc67482.2025.11413091 UR - https://doi.org/10.1109/ciotsc67482.2025.11413091 DA - 2025-11-07 AU - Liang, Zhiyuan C1 - Xi’an Jiaotong-Liverpool University,School of Robotics,Suzhou,China AU - Hu, Bintao C1 - Xi’an Jiaotong-Liverpool University,School of Internet of Things & Institute for Low Altitude Innovation,Suzhou,China AU - Yang, Songjiang C1 - Northeastern University,College of Information Science and Engineering,Shenyang AU - Du, Jianbo C1 - Xi’an University of Posts and Telecommunications,School of Communications and Information Engineering,Xi’an,China AU - Pei, Rui C1 - Xi’an Jiaotong-Liverpool University,School of Advanced Technology & Institute for Low Altitude Innovation,Suzhou,China AU - Yin, Yue C1 - Keio University,Faculty of Science and Technology,Yokohama,Japan KW - Scalability KW - Drone KW - Software deployment KW - Key (lock) KW - Resource (disambiguation) KW - Open research KW - Resource allocation KW - Service (business) SP - 1 EP - 6 ER - TY - JOUR TI - Urban low-altitude airspace resources: Integrating risk and accessibility through fine-grained analysis PY - 2026 PB - Elsevier BV SN - 0264-2751 T2 - Cities DO - 10.1016/j.cities.2026.107190 UR - https://doi.org/10.1016/j.cities.2026.107190 DA - 2026-05-19 AU - He, Hongbo C1 - Key Laboratory of Low Altitude Geographic information and Air Route, Civil Aviation Administration of China, Beijing, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China C1 - University of Chinese Academy of Sciences, Beijing, China AU - Ye, Huping C1 - Key Laboratory of Low Altitude Geographic information and Air Route, Civil Aviation Administration of China, Beijing, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China C1 - University of Chinese Academy of Sciences, Beijing, China AU - Xu, Chenchen C1 - Key Laboratory of Low Altitude Geographic information and Air Route, Civil Aviation Administration of China, Beijing, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China AU - Yue, Huanyin C1 - Key Laboratory of Low Altitude Geographic information and Air Route, Civil Aviation Administration of China, Beijing, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China C1 - University of Chinese Academy of Sciences, Beijing, China AU - Liao, Xiaohan C1 - Key Laboratory of Low Altitude Geographic information and Air Route, Civil Aviation Administration of China, Beijing, China C1 - State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China C1 - University of Chinese Academy of Sciences, Beijing, China LA - en KW - Geospatial analysis KW - Pace KW - Grid KW - Population KW - Resource (disambiguation) KW - Urban planning KW - Resource distribution VL - 175 SP - 107190 EP - 107190 AB - With the rapid pace of urbanization, low-altitude airspace is increasingly recognized as a critical resource for smart and emerging urban systems, supporting applications such as UAV-based logistics and urban air mobility. However, conventional airspace planning methods have not adequately captured the complexity and dynamic variability of urban morphology. In response, this study proposes an innovative, data-driven framework for the fine delineation of low-altitude airspace. By integrating multiple high-precision geospatial datasets and employing the H3 hexagonal grid system as the spatial discretization framework, the framework quantitatively assesses airspace risk and accessibility. Using a case study of airspace below 120 m in Shenzhen, the analysis reveals distinct variations in airspace attributes across different flight altitudes and time periods. Results indicate that risk exhibits a pronounced bimodal distribution with significant differences among functional zones, while, after accounting for uncertainties in UAV performance and flight conditions, the feasible airspace (measured as true altitude) stabilizes above 60 m with a deceleration in growth. Furthermore, the integration of built-up area and local climate zones (LCZ) analyses effectively elucidates differences in risk and accessibility, providing unprecedented data granularity and a computational platform for characterizing the spatiotemporal evolution and attributes of low-altitude airspace. This study offers theoretical support for the sustainable development of the low-altitude economy and introduces new perspectives for the scientific planning and precise management of low-altitude airspace. • Comprehensive analysis of low-altitude airspace risk and accessibility across the urban area. • Spatiotemporal characterization of population exposure risk from UAV operations. • Identification of significant altitude-dependent variations in airspace accessibility. • Exploration of the feasibility of fine-scale airspace management using the LCZ framework. • Recommendations for sustainable urban low-altitude economy development and precise airspace management. ER - TY - JOUR TI - Optimization of UAV–UGV Last-Mile Collaborative Delivery under Dynamic Carbon Footprint Constraints: Visual Simulation and Scenario Analysis of Coordinated Air–Ground Unmanned Delivery System PY - 2026 SN - 2737-5374 T2 - Global Humanities and Social Sciences DO - 10.61360/bonighss26202052040801 UR - https://doi.org/10.61360/bonighss26202052040801 DA - 2026-08-18 AU - Qiao, Yu C1 - Guangzhou Huali College , P.R. China AU - Deng, Xinwen C1 - Guangzhou Huali College , P.R. China AU - Huang, Xiaoying C1 - Guangzhou Zhixin Middle School Zengcheng Experimental School, P.R. China AU - Chen, Wanting C1 - Guangzhou Huali College , P.R. China AU - Lin, Yimeng C1 - Guangzhou Huali College , P.R. China AU - Luo, Zuqiang C1 - Guangzhou Huali College , P.R. China AU - Du, Yuchen C1 - Guangzhou Huali College , P.R. China AU - Li, Luyao C1 - Guangzhou Huali College , P.R. China AU - Shen, Meijun C1 - Guangzhou Huali College , P.R. China AU - Chen, Xuanxuan C1 - Guangzhou Huali College , P.R. China KW - Carbon footprint KW - Truck KW - Obstacle KW - Sensitivity (control systems) KW - Scenario analysis KW - Footprint KW - System dynamics VL - 7 IS - 4 SP - 287 EP - 306 AB - To address the challenges of operational cost control, safety assurance, and dynamic carbon footprint management in urban last-mile logistics, this study proposes a UAV–UGV air–ground collaborative delivery framework and develops a multi-objective optimization model considering efficiency, safety, and low-carbon performance. A visual simulation platform is constructed using HTML5 Canvas and Three.js, integrating obstacle avoidance, low-altitude UAV delivery, random scenario generation, and dynamic route re-planning modules. Simulation results show that, under an order density of 20 orders/100 km², the proposed strategy reduces total operational time by 33.9% compared with traditional truck delivery and decreases travel distance by 24.3% compared with UAV-only delivery. The unit carbon emission intensity is reduced by 96.4% and 24.3% compared with truck-based and UAV-only delivery modes, respectively. The obstacle avoidance algorithm achieves a 100% success rate in 50 random scenarios, with a path length variation coefficient of 0.132, demonstrating strong robustness. Sensitivity analysis confirms that the model remains stable under ±20% variations in carbon emission factors and carbon trading prices. This study provides a quantitative decision-support framework for unmanned delivery optimization and low-carbon logistics equipment selection. At the same time, it provides a reference for nurturing potential talent for the future. ER - TY - JOUR TI - A SYSTEMS APPROACH TO URBAN OPERATIONS FOR THE PROTECTION OF CRITICAL INFRASTRUCTURE AND THE MANAGEMENT OF CIVILIAN RISKS PY - 2025 PB - National Technical University "Kharkiv Polytechnic Institute" T2 - eNTUKhPIIR Repository (Kharkiv Polytechnic Institute) DO - 10.13140/rg.2.2.23481.84321 UR - https://doi.org/10.13140/rg.2.2.23481.84321 DA - 2025-01-01 AU - Rustamov, Asad AU - Akhundov, Ramil AU - Hashimov, Elshan AU - TALIBOV, Aziz LA - en KW - Critical infrastructure KW - Risk management KW - Government (linguistics) KW - Vulnerability (computing) KW - Key (lock) AB - This article proposes a unified operational-methodological framework for optimizing the intelligence-surveillance-assessment chain in the context of increasing armed confrontations within urban space, integrating it in advance with a ruleset and managing outcomes on the basis of measurable indicators.The scientific basis of the problem is that fragmentation of information across diverse sources, delayed formation of a common operational picture, and the deferral of legal vetting to later stages prolong the OODA cycle, increase civilian risks, and weaken the resilience of critical infrastructure.The aim of the study is to demonstrate the coordination, within a single system, of urban flow formalization, vertical stratification of sensors, proceduralizing of a scenario-driven ruleset decision tree, and a KPI-based management mechanism.The methodological sequence consists of four interlinked stages.In the first stage, the urban environment is modeled not as a simple collection of buildings but as a network of parallel flows.Energy, water supply, information, logistics, and population movement are mapped in a GIS environment as overlapping layers.Cadastre data, street networks, utility infrastructure, and population density are synchronized.Restriction zones and humanitarian transit routes in the area of operations are identified in advance.In the second stage, the information resilience of the system is strengthened.High-altitude platforms and satellite observation provide a synoptic view of the area of operations, while tactical UAVs and ground robots perform micro-scale identification.Observation and patrol reports support the mapping of OSINT social signals.Time stamps and geolocation rules are standardized.In cases of inter-source inconsistencies, a human-in-the-loop correction mechanism is activated.Through this integration, a unified operational picture is formed.In the third stage, legal and ethical compliance is institutionalized at the level of operational procedures.The scenariodriven decision tree is based on criteria such as the probability of civilian presence, proximity to a critical facility, and the availability of alternative means.When civilian risk is confirmed, non-lethal means or maneuver are prioritized.When critical proximity is detected, high-precision munitions or standoff methods are selected.Every firing decision is formalized through a dual-confirmation mechanism, and poststrike assessment results are immediately fed back into the cycle.In the fourth stage, measurability and management are ensured.Core indicators include the event-based average value of civilian casualties, the share of damage to critical facilities, the duration of the intelligence-decision-execution cycle, the accuracy ER - TY - JOUR TI - OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 1939-1404 T2 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing DO - 10.1109/jstars.2025.3579740 UR - https://doi.org/10.1109/jstars.2025.3579740 DA - 2025-01-01 AU - Hu, Boni C1 - School of Aeronautics, National Key Laboratory of Aircraft Configuration Design, Northwestern Polytechnical University, Xi’an, China C1 - School of Aeronautics, Northwestern Polytechnical University, Xi'an, China AU - Li, Haowei C1 - School of Aeronautics, National Key Laboratory of Aircraft Configuration Design, Northwestern Polytechnical University, Xi’an, China C1 - School of Aeronautics, Northwestern Polytechnical University, Xi'an, China AU - Bu, Shuhui C1 - School of Aeronautics, National Key Laboratory of Aircraft Configuration Design, Northwestern Polytechnical University, Xi’an, China C1 - School of Aeronautics, Northwestern Polytechnical University, Xi'an, China AU - Chen, Lin C1 - School of Aeronautics, National Key Laboratory of Aircraft Configuration Design, Northwestern Polytechnical University, Xi’an, China C1 - School of Aeronautics, Northwestern Polytechnical University, Xi'an, China AU - Han, Pengcheng C1 - School of Aeronautics, National Key Laboratory of Aircraft Configuration Design, Northwestern Polytechnical University, Xi’an, China C1 - School of Aeronautics, Northwestern Polytechnical University, Xi'an, China LA - en KW - Orientation (vector space) KW - Computer science KW - Optics KW - Computer vision KW - Physics KW - Geometry KW - Mathematics VL - 18 SP - 15508 EP - 15522 AB - Cross-view image-based geolocalization can provide accurate, drift-free navigation in the absence of external positioning signals, which is particularly crucial in domains such as UAV delivery logistics and disaster relief. While existing research predominantly focuses on matching ground panoramic images with known orientations, real-world scenarios often present images with unknown orientations. Additionally, acquiring panoramic images with a full$360^\circ$field of view (FoV) is impractical, creating a disconnect between research and practical applications. In this study, we introduce an innovative cross-view geolocalization method that integrates sophisticated orientation estimation. Our goal is to improve geolocalization accuracy in limited FoV and arbitrary orientation scenarios. To achieve this, we design an efficient hard sample training strategy employing a dual-weighted soft-margin triplet loss function to extract discriminative features. Concurrently, we develop an orientation estimation module that leverages a convolution-based sliding window to assess the similarity with satellite-view embeddings and query embeddings. Thus, our method is namedOriLoc. We validated the approach's unique outperformance on three challenging cross-view geolocalization datasets, including data from commercial, residential, urban landscapes, and suburban areas across two continents, aligning with the demands of real-world scenarios. Our results demonstrate that a straightforward hard sample mining strategy, when combined with an appropriate learning objective, significantly enhances geolocalization performance for limited FoV and orientation-free cross-view images, as shown in Figure 1. Furthermore, our orientation estimation module, which incorporates classical convolutions, exhibits remarkable accuracy in estimating orientations when integrated with attention embeddings prior to polar transformation. Code and trained models are publicly available onhttps://github.com/boni-hu/OriLoc. ER - TY - CHAP TI - The Next Form of Human Settlement: A Design for Future Yilong City PY - 2020 PB - Springer International Publishing SN - 2212-5450 T2 - Strategies for sustainability DO - 10.1007/978-3-030-49618-0_9 UR - https://doi.org/10.1007/978-3-030-49618-0_9 DA - 2020-08-13 AU - Long, Ying C1 - School of Architecture and Hang Lung Center for Real Estate, Key Laboratory of Eco Planning & Green Building by Ministry of Education, Tsinghua University, Beijing, China AU - Zhang, Enjia C1 - School of Architecture, Tsinghua University, Beijing, China LA - en KW - Human settlement KW - Settlement (finance) KW - Geography KW - Architectural engineering KW - Augmented reality KW - Tourism KW - Engineering KW - Computer science KW - World Wide Web KW - Artificial intelligence KW - Archaeology SP - 143 EP - 160 ER - TY - JOUR TI - Governance Architecture and Urban Performance: Low-Altitude Economy Infrastructure, Coordination Capacity, and Innovation Outcomes in Vietnam PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.6547037 UR - https://doi.org/10.2139/ssrn.6547037 DA - 2026-01-01 AU - Tran, Thi C1 - Vietnam National University (VNU) - VNU University of Economics and Business C1 - Vietnam National University (VNU) - VNU University of Economics and Business, E4 Building, 144 Xuan Thuy - Cau Giay - Hanoi, Hanoi, Vietnam AU - Pham, Thi C1 - Vietnam National University AU - Vu, Thi C1 - Vietnam National University (VNU) - VNU University of Economics and Business C1 - Vietnam National University (VNU) - VNU University of Economics and Business, E4 Building, 144 Xuan Thuy - Cau Giay - Hanoi, Hanoi, Vietnam AU - Tran, An C1 - Posts and Telecommunications Institute of Technology C1 - Posts and Telecommunications Institute of Technology, Vietnam AU - Le, Khanh C1 - Vietnam National University (VNU) - VNU University of Economics and Business C1 - Vietnam National University (VNU) - VNU University of Economics and Business, E4 Building, 144 Xuan Thuy - Cau Giay - Hanoi, Hanoi, Vietnam AU - Nguyen, Minh C1 - FPT University C1 - FPT University, Ho Chi Minh City, Vietnam KW - Corporate governance KW - Productivity KW - Mediation KW - Economies of scale KW - Urban economics KW - Inefficiency KW - Digital economy KW - Welfare KW - Panel data ER - TY - JOUR TI - Optimizing Unmanned Aerial Vehicle Path finding in Complex Urban Airspaces: A Hybrid Genetic Algorithm with Bidirectional and Greedy Initialization PY - 2026 PB - Springer Science+Business Media SN - 0365-7442 T2 - Aerotecnica Missili & Spazio DO - 10.1007/s42496-026-00314-7 UR - https://doi.org/10.1007/s42496-026-00314-7 DA - 2026-04-09 AU - Balakrishnan, Hari C1 - APJ Abdul Kalam Technological University, Thiruvananthapuram, 695016, Kerala, India C1 - Department of Mechanical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, 695016, Kerala, India AU - Suryan, Abhilash C1 - APJ Abdul Kalam Technological University, Thiruvananthapuram, 695016, Kerala, India C1 - Department of Mechanical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, 695016, Kerala, India AU - Alex, Anu C1 - APJ Abdul Kalam Technological University, Thiruvananthapuram, 695016, Kerala, India C1 - Department of Civil Engineering, College of Engineering Trivandrum, Thiruvananthapuram, 695016, Kerala, India AU - Jisha, V. C1 - APJ Abdul Kalam Technological University, Thiruvananthapuram, 695016, Kerala, India C1 - Department of Electrical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, 695016, Kerala, India AU - Raj, Sonia C1 - APJ Abdul Kalam Technological University, Thiruvananthapuram, 695016, Kerala, India C1 - Department of Mechanical Engineering, Marian Engineering College, Thiruvananthapuram, 695582, Kerala, India AU - Zhang, Guang C1 - Zhejiang Key Laboratory of Multiflow and Fluid Machinery, Zhejiang Sci-Tech University, Hangzhou, 310018, China LA - en KW - Initialization KW - Motion planning KW - Heuristics KW - Genetic algorithm KW - Path (computing) KW - Population KW - Convergence (economics) KW - Heuristic VL - 105 IS - 5 SP - 833 EP - 851 ER - TY - JOUR TI - AchiFL: Efficient Asynchronous Hierarchical Federated Learning via Adaptive Edge Aggregation PY - 2025 PB - Institute of Electrical and Electronics Engineers SN - 2332-7731 T2 - IEEE Transactions on Cognitive Communications and Networking DO - 10.1109/tccn.2025.3641522 UR - https://doi.org/10.1109/tccn.2025.3641522 DA - 2025-12-08 AU - Tang, Xiangyun C1 - Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE and the School of Information Engineering, Minzu University of China, Beijing, China C1 - the School of Information Engineering, Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China AU - Peng, R C1 - Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE and the School of Information Engineering, Minzu University of China, Beijing, China C1 - the School of Information Engineering, Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China AU - Li, Hao C1 - Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE and the School of Information Engineering, Minzu University of China, Beijing, China C1 - the School of Information Engineering, Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China AU - Zhang, Tao C1 - School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China AU - Hou, Xiangwang C1 - Department of Electronic Engineering, Tsinghua University, Beijing, China AU - Liu, Xiangzhi C1 - Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China C1 - Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Key Laboratory of Computing Power Network and Information Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China KW - Server KW - Asynchronous communication KW - Upload KW - Enhanced Data Rates for GSM Evolution KW - Edge device KW - Edge computing KW - Robustness (evolution) KW - Key (lock) VL - 12 SP - 4746 EP - 4761 ER - TY - JOUR TI - A Spatio-Temporal Collaborative Improved Multi-Strategy Dung Beetle Optimization Algorithm for 3D Path Planning of Multiple Unmanned Aerial Vehicles in Urban Environments PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2226-4310 T2 - Aerospace DO - 10.3390/aerospace13060506 UR - https://doi.org/10.3390/aerospace13060506 DA - 2026-05-29 AU - Yu, Yaowei C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China AU - Le, Meilong C1 - College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China LA - en KW - Obstacle KW - Motion planning KW - Path (computing) KW - Population KW - Dung beetle KW - Swarm behaviour KW - Obstacle avoidance KW - Optimization algorithm VL - 13 IS - 6 SP - 506 EP - 506 AB - Collaborative 3D path planning for multiple unmanned aerial vehicles (UAVs) in dense urban airspace is difficult, which does not come from one factor alone. Buildings, flight restrictions, moving obstacles, and inter-UAV coupling all act together, and the search space grows quickly as the scene becomes more crowded. In such cases, a standard swarm optimizer may still find a path, but it often struggles with early feasibility, later-stage refinement, and local replanning after the environment changes. To deal with these issues, this paper develops a spatio-temporal collaborative improved multi-strategy dung beetle optimization algorithm, called STC-IMSDBO, for urban multi-UAV path planning. The framework combines five linked components: feasible-airspace population initialization, spatio-temporal variable-step search, multi-factor adaptive weighting, local game-based conflict handling, and rolling-horizon replanning. A normalized multi-objective cost is used to balance flight efficiency, smoothness, obstacle avoidance, airspace compliance, and cooperative safety. The method is tested in four simulated urban scenarios and compared with six representative methods. In the tested cases, the STC-IMSDBO generates shorter feasible routes, uses less energy, converges in fewer iterations, and maintains better cooperative safety than the comparison methods. These results suggest that the method is a useful planning option for dense urban missions such as logistics, inspection, and emergency response. That said, larger-swarm runtime tests and field validation are still needed. ER - TY - JOUR TI - From Smuggling to Armed Drones: The Evolution of Unmanned Technologies in the Arsenal of Latin American Organized Crime PY - 2025 PB - Aurora Group s.r.o. SN - 2409-8965 T2 - Конфликтология / nota bene DO - 10.7256/2454-0617.2025.3.75585 UR - https://doi.org/10.7256/2454-0617.2025.3.75585 DA - 2025-03-01 AU - Tsimashchenia, Viachaslau LA - en KW - Drone KW - Latin Americans KW - Criminology KW - Computer security KW - Aeronautics KW - Political science KW - Computer science KW - Engineering KW - Law KW - Biology KW - Sociology IS - 3 SP - 164 EP - 189 AB - The subject of research is the use of unmanned aerial vehicles by organized criminal groups in Latin America and the Caribbean Basin countries during 2010–2025. The transformation of drones from tools for drug smuggling across the US-Mexico border to full-fledged combat platforms capable of delivering precision strikes with explosive devices is analyzed. The evolution of tactical UAV application by major cartels (CJNG, Sinaloa, Nueva Familia Michoacana) and insurgent groups (ELN, FARC) is examined. Technical modification of commercial drones into improvised bombers with thermal imaging, FPV systems, and autonomous navigation is investigated. The geographical spread of the threat from Mexican border states to urban slums of Brazil, prisons of Ecuador, and gang territories in Haiti is studied. State countermeasures, international cooperation, and legal regulation of UAV circulation in thirteen regional countries are analyzed. The methodological foundation includes open source intelligence (OSINT) data analysis with incident verification through geolocation of video materials, content analysis of official documents from US and Latin American law enforcement agencies, and reports from international organizations (UNODC, INTERPOL, OAS). A comparative case-study approach is applied to identify regional patterns and functional classification of drone application methods. Verification is conducted through cross-checking independent sources and comparison with official statistics from thirteen regional countries. The research presents a comprehensive study of the drone threat from organized crime in the LAC region, filling a gap in security studies. A functional typology of UAV application (surveillance → logistics → violence) and taxonomy of criminal platforms has been developed. Concepts of "adaptive criminality" in technological context, "technological spillover" between sectors, and a model of "drone arms race" have been introduced. Critical turning points in threat development (2020, 2022-2023) have been identified and connections established between the Ukrainian conflict and the spread of combat drone use among Latin American cartels. Threat mapping has been conducted with correlation between drug trafficking geography and state institutional weakness. Prognostic scenarios for 2025-2030 and recommendations for improving international countermeasures against criminal use of unmanned technologies, applicable to other world regions as well, have been formulated. ER - TY - JOUR TI - Spatio-temporal economic planning weighted routing model for agricultural land-use management in peri-urban zones: a case study of the Kyiv agglomeration PY - 2026 PB - National University of Life and Environmental Sciences of Ukraine SN - 2306-1677 T2 - Zemleustrìj kadastr ì monìtorìng zemelʹ DO - 10.31548/zemleustriy2026.02.05 UR - https://doi.org/10.31548/zemleustriy2026.02.05 DA - 2026-06-11 AU - Nazarenko, V. C1 - National University of Life and Environmental Sciences of Ukraine LA - en KW - Service (business) KW - Routing (electronic design automation) KW - Agriculture KW - Baseline (sea) KW - Time horizon KW - Profitability index KW - Economic impact analysis KW - Middleware (distributed applications) AB - Ukraine’s agricultural sector faces a compound set of economic and spatial pressures: peri-urban land-use transformation, disrupted logistics, volatile fuel costs, and restricted mobility in wartime conditions. These challenges are especially evident in the Kyiv agglomeration, where agricultural operations increasingly depend on rapid re-planning under changing constraints. This study presents and evaluates a weights-based real-time routing platform for digital agriculture in Ukraine, integrated into a FAIR/OGC-aligned middleware architecture that consolidates multi-source data streams (satellite EO, UAV imagery when permitted, IoT telemetry, road and congestion layers, and administrative–economic registers). The platform operationalizes routing as a continuous decision-support service by allowing users to tune explicit weights across four criteria: time, direct cost, CO₂e, and operational risk, while enforcing land-use and safety restrictions through hard spatial masks and soft penalty layers.Empirical testing was conducted over four pilot weeks in the peri-urban belt of Kyiv using a controlled-scenario design with three weekly templates: Baseline (A), Stress (B: high fuel prices and higher transport risk), and Sustainability-adjusted (C). The middleware maintained continuous advisory generation under intermittent UAV availability and short-lived network outages by employing store-and-forward edge buffers, asynchronous refresh, and lineage capture; exported route advisories were reproducible as CSV/GeoJSON with full provenance. Scenario results show strong sensitivity of weekly logistics costs to fuel-driven cost bands: compared with Scenario A, Scenario B increased direct logistics costs by 12.44%. In comparison, Scenario C increased costs by 6.22%. Tactical re-weighting protected time performance (approximately −1.15% total travel time in B and C). In comparison, feasibility remained high (≥95% of jobs served within service windows), and restricted-edge violations remained zero due to enforced masks. CO₂e totals remained stable across scenarios under uniform emission factors, highlighting the need for differentiated low-carbon corridors or fleet classes in future pilots.The results indicate that a standards-based middleware platform combined with weight-based routing can serve as a practical tool for land-use governance and agricultural economics by linking spatial constraints, cost dynamics, and auditable sustainability indicators within a single operational workflow.Received: 27.03.2026;Accepted:07.04.2026; ER - TY - GEN TI - UFPR CampusMap: a laboratory for a Smart City developments PY - 2019 SN - 2570-2106 T2 - Abstracts of the ICA DO - 10.5194/ica-abs-1-57-2019 UR - https://doi.org/10.5194/ica-abs-1-57-2019 DA - 2019-07-15 AU - Delazari, Luciene C1 - Geodetic Sciences Graduate Program, Federal University of Paraná (UFPR), Curitiba, Brazil AU - Filho, Leonardo C1 - Geomatic Department, Federal University of Paraná (UFPR), Curitiba, Brazil AU - Skroch, Ana C1 - Cartographic Engineering Undergraduate Course, Federal University of Paraná (UFPR), Curitiba, Brazil LA - en KW - Computer science VL - 1 SP - 1 EP - 2 AB - Abstract. A Smart City is based on intelligent exchanges of information that flow between its many different subsystems. This flow of information is analyzed and translated into citizen and commercial services. The city will act on this information flow to make its wider ecosystem more resource-efficient and sustainable. The information exchange is based on a smart governance operating framework designed to make cities sustainable.The public administration needs updated and reliable geospatial data which depicts the urban environment. These data can be obtained through smart devices (smartphones, e.g.), human agents (collaborative mapping) and remote sensing technologies, such as UAV (Unnamed Aerial Vehicles). According to some authors, there are four dimensions in a Smart City. The first dimension concerns the application of a wide range of electronic and digital technologies to create a cyber, digital, wired, informational or knowledge-based city; the second is the use of information technology to transform life and work; the third is to embed ICT (Information and Communication Technology) in the city infrastructure; the fourth is to bring ICT and people together to enhance innovation, learning, and knowledge. Analyzing these dimensions, it is possible to say that in all of them the geospatial information is crucial, otherwise, none of them are possible. Considering these aspects, this research intends to use the Smart City concept as a methodological approach using the UFPR (Federal University of Parana) as a target to develop a case study.The UFPR has 26 campus in different cities of the Paraná State, south of Brazil. Its structure has 14 institutes. It comprises 11 million square meters of area, 500,000 square meters of constructed area and 316 buildings. There are more than 6,300 employees (staff and administration), 50,000 undergraduate students and 10,000 graduate students. Besides these figures, there are external people who need access to the UFPR facilities, such as deliveries, service providers and the community in general.The lack of knowledge about the space and its characteristics has a direct impact on issues such as resources management (human and material), campi infrastructure (outside and inside of the buildings), security and other activities which can be supported using an updated geospatial database. In 2014, the UFPR CampusMap project was started with the indoor mapping as the main goal. However, the base map of the campus was needed in order to support the indoor mapping, the available one was produced in 2000. Thereafter, the campus Centro Politécnico (located in the city of Curitiba) is being used as a case study to develop methodologies to create a geospatial database which will allows to different users the knowledge and management of the space.According to Gruen (2013), a Smart City must have spatial intelligence. Moreover, it is necessary the establishment of a database, in particular, a geospatial database. The knowledge of the space where the events happen is a key element in this context. This author also states that to achieve this objective are necessary the following items: Automatic or semi-automated Digital Surface Models (DSM) generation from satellite, aerial and terrestrialimages and/or LiDAR data;Further development of the semi-automated techniques onto a higher level of automation; Integrated automated and semi-automated processing of LiDAR point clouds and images, both from aerial andterrestrial platforms; Streamlining the processing pipeline for UAV image data projects; Set-up of GIS with 3D/4D capabilities; Change detection and databases updating; Handling of dynamic and semantic aspects of city modeling and simulation. This leads to 4D city models; LBS (Location Based Services) system investigations (PDAs, mobiles); and Establishment of a powerful visualization and interaction platform.Some of these aspects are being addressed in this research. The first one is the integration of indoor/outdoor data to helps the space management and provides a tool for navigation between the spaces. The base map was updated through a stereo mapping compilation from images collected using a UAV Phantom 4 from DJI (https://www.dji.com/phantom-4). The use of this technology for data acquisition is not only faster but also cheaper compared to the traditional photogrammetric method. Besides the quality of the images (in this case a GSD – Ground Sample Distance – of 2,5 cm), it can be use in urban areas as a rapid response in emergency situations. To georreferencing the image block, it was used 50 control points collected by GNSS (Global Navigation Satellite System) and the software Agisoft Photoscan (http://www.agisoft.com/) to perform the bundle block adjustment with self-calibration. After the processing, the exterior orientation parameters of image block and the tridimensional coordinates of each tie point were calculated simultaneously with the determination of the interior orientation parameters: focal length (f), principal point coordinates (x0, y0), radial symmetric (k1, k2, k3) and decentering distortion coefficients (p1, p2). In the mapping production step, the features were extracted through stereo mapping compilation accordingly the standards defined by the Brazilian Mapping Agency. The several layers were edited in GIS software (QGIS) and then the topology was built. Afterward, it was created a spatial database using Postgre/PostGIS. Also, the dense point cloud was generated using SfM (Structure from Motion) algorithms to allow to generate the digital surface model and orthomosaics. Meanwhile, a website using HTML5+CSS3® and JavaScript® technologies was developed to publish the results and the first applications. (www.campusmap.ufpr.br). The architecture of this application uses JavaScript®, LeafLet, PgRouting library (to calculate the routes between interest points), files in GeoJson format and custom applications. The indoor database comprises the data about the interior of the buildings and provides to the user some functionalities such as: search for rooms, laboratories, and buildings; routes between points (inside and outside the buildings), floor change. Also, some web applications were developed in order to demonstrate the capabilities of the use of geospatial information in an environment very similar to a city and its problems, e.g. parking management, security, logistics, resources inventory, among others. It was developed a mobile application to provide the indoor user positioning through Wi-Fi (Wireless Fidelity) networks. This, combined with the indoor mapping, will allow the users to navigate in real time inside the buildings. Using the data from the point cloud and the CityGML standard it was developed a 3D model of some buildings. An application to inform crime occurrences (such as robbery, assaults) was also developed so these occurrences can be mapped, and the administration can increase the security of the campus. Design an interface with functionalities to integrate all applications which are being presented in individual Webpages;Develop a visualization tool for 3D models using CityGML;Evaluate the potential of UAV images for different applications in urban scenarios;Develop an interface for collaborative database update.Expand the database to other campus of UFPR and develop new functionalities to different users; The “smart city” concept allows to develop an optimized system that use geospatial data to understand the complexity of the urban environments. The use of the geospatial data can improve efficiency and security to manage urban aspects like infrastructure, building and public spaces, natural environment, urban services, health and education. Also, this concept can give a support to the city management agents during the design, realization and evaluation of the urban projects.In the present project, we believe these are the first steps to build a connected environment and apply the “smart city” concept into the university administration to make the sustainable use of resources and could suit as an example to some existing problems in public administrations. ER - TY - JOUR TI - Physical and Mental Restorative Benefits and Spatial Intervention Mechanisms of Urban Green Spaces Under Low-Altitude 3D Noise Disturbance PY - 2026 PB - Science Press SN - 1673-1530 T2 - Landscape Architecture DO - 10.3724/j.fjyl.la20260118 UR - https://doi.org/10.3724/j.fjyl.la20260118 DA - 2026-07-01 AU - SHAO, Yuhan AU - CAI, Qingzhou AU - Ma, Ding LA - en KW - Disturbance (geology) KW - Intervention (counseling) KW - Noise (video) KW - Noise pollution KW - Mental health VL - 33 IS - 7 SP - 47 EP - 56 AB - Objective Low-altitude cargo drones are now flying over peri-urban green spaces, and their noise profile differs markedly from conventional traffic sound. Unlike road noise, the acoustic energy of multi-rotor UAVs arrives from above, creating a three-dimensional intrusion that existing park design standards have not addressed. Current health assessments of urban green infrastructure still rely primarily on two-dimensional visual metrics such as the green view index (GVI), tacitly assuming that visible greenery reduces stress in a roughly linear manner. This assumption becomes problematic when overhead mechanical noise enters the scene, because the interaction between visual and auditory stimuli in three-dimensional space is not well understood. Although prior research has separately documented the restorative effects of vegetation views and the annoyance caused by drone noise, few studies have examined their joint influence in real spatial settings. We therefore conducted an empirical study to investigate how micro-scale spatial form, visual greenness, and acoustic exposure together shape autonomic physiology and subjective states during drone overflight. Our broader purpose was to test whether conventional nature-based health services (NBHS) planning remains valid when low-altitude noise is present. Methods Fieldwork was carried out in Tanzhou, Zhongshan, a peri-urban corridor adjacent to Zhuhai’s low-altitude logistics pilot zone. Twelve micro-scale green spaces were selected to represent a realistic gradient of vegetation density and built-form enclosure, ranging from open lawn to dense canopy. A DJI FlyCart 30 logistics drone served as the noise source, hovering at 40 m, 30 m, and 20 m relative altitude above each measurement point; a no-drone baseline was also recorded. Spatial parameters including distance to the nearest building façade, distance to the nearest road, and building coverage ratio were derived from ArcGIS Pro and high-resolution satellite imagery. We later removed building coverage from the final statistical model because variance inflation factor diagnostics showed unacceptable collinearity with visual greenness. Visual data consisted of 1.6 m eye-level panoramic photographs processed through a ComfyUI workflow with the OneFormer semantic-segmentation model to extract pixel-level GVI. Acoustic metrics—LAeq and acoustic kurtosis—were recorded simultaneously with a Class 1 sound level meter at 48 kHz sampling. Thirty healthy volunteers were exposed to these audio-visual scenes under immersive laboratory conditions. Continuous electrocardiogram (ECG) was monitored with a Polar system, and a retrospective time-window method aligned physiological data with specific scene exposures. Root mean square of successive RR interval differences (RMSSD), mean heart rate, and Baevsky stress index were extracted with the NeuroKit2 framework. Subjective ratings of interest, tension, focused attention, and fear were collected via post-scene questionnaires. All data were analyzed with multilevel linear modeling using restricted maximum likelihood estimation. Results The analysis revealed that environmental factors shape health outcomes in ways that are not always intuitive. Drone altitude mattered sharply: the initial appearance at 40 m already raised psychological load above the control level, and as the drone descended to 20 m, tension and fear reached their peak while focused attention and recreational interest steadily dropped. Spatial context produced a telling disconnect between what participants felt and what their bodies showed. Greater distance from urban roads lowered self-reported tension, yet it also raised mean heart rate and Stress Index while depressing RMSSD. This indicates that without the masking blanket of continuous traffic noise, impulsive drone intrusions become more acoustically salient and provoke stronger sympathetic activation. Distance from nearby buildings, by contrast, improved physiological relaxation. On the acoustic front, LAeq emerged as the dominant stressor, eclipsing the more transient role of acoustic kurtosis, which failed to register a significant main effect in the final regression. Higher LAeq consistently eroded recreational interest and attention while amplifying tension and fear. Vegetation provided tangible buffering: a higher GVI was linked to lower mean heart rate, confirming that visual greenness can partly offset physiological stress even under noise. Notably, dense forest canopies reduced subjective tension but produced autonomic co-activation—RMSSD rose alongside a peak Baevsky Stress Index—and these enclosed spaces also diminished cognitive attention, probably because overhead noise in confined settings heightens a sense of oppressiveness. Open grassy areas with high GVI delivered the best overall physiological resilience. Conclusion Relying on visual metrics alone falls short when low-altitude drone noise enters the picture. Our results show that perceived calmness and actual physiological relaxation do not necessarily align; quiet green pockets deprived of continuous background noise to mask sudden intrusions can harbour hidden physiological stress. Planning urban NBHS sites therefore calls for integrating soundscape design with spatial layout. We argue for extending the classic prospect-refuge theory into three-dimensional settings: landscape configurations should secure visual openness through adequate building setbacks, giving people the capacity to visually register overhead threats without feeling enclosed, while tree canopies supply physical shelter yet retain enough vertical permeability to avoid a boxed-in sensation. Introducing positive natural masking sounds offers a practical way to make up for the loss of traffic-noise masking in deeper green areas and thus safeguard their health benefits. Follow-up work would benefit from testing additional drone models and incorporating further psychoacoustic metrics to refine these design guidelines. ER - TY - JOUR TI - Explainable federated learning-based fault diagnosis framework for safety assurance in multi-agent autonomous systems PY - 2026 PB - Emerald Publishing Limited SN - 2633-6596 T2 - Journal of Intelligent Manufacturing and Special Equipment DO - 10.1108/jimse-09-2025-0022 UR - https://doi.org/10.1108/jimse-09-2025-0022 DA - 2026-01-15 AU - Rahmati, Milad C1 - Independent Researcher , , , AU - Rahmati, Nima C1 - Independent Researcher , , LA - en KW - Scalability KW - Edge device KW - Leverage (statistics) KW - Artificial neural network KW - Feature (linguistics) KW - Low latency (capital markets) KW - Inference KW - Enhanced Data Rates for GSM Evolution KW - Latency (audio) KW - Deep learning VL - 7 IS - 1 SP - 59 EP - 74 AB - Purpose This study introduces an explainable federated learning (XFL) framework for fault diagnosis in multi-agent autonomous systems, such as UAVs and self-driving vehicles. It addresses limitations of centralized approaches, including scalability issues, communication delays, data privacy concerns and lack of transparency in safety-critical environments. By enabling collaborative model training without central data pooling, the framework ensures real-time fault detection, interpretability, and regulatory compliance, contributing to safer operations in intelligent mobility, drone logistics, and urban robotics. Design/methodology/approach The framework employs Federated Averaging (FedAvg) for distributed learning across autonomous agents, with local models featuring a lightweight neural network including attention mechanisms and softmax output. SHAP (Shapley Additive Explanations) is integrated for local interpretability, with aggregated global profiles for feature relevance. Simulations mimic multi-agent environments with non-IID data, injecting faults such as sensor drift, dropout and adversarial perturbations. Evaluation uses metrics such as accuracy, convergence speed and explanation fidelity, benchmarked against FedAvg, centralized and local-only methods on edge hardware such as Jetson Nano. Findings The XFL framework achieved 92.6% classification accuracy after 20 rounds, outperforming FedAvg (88.7%) and centralized baselines (86.9%), with faster convergence (12 vs. 18 rounds). It demonstrated robustness, retaining 89.4% accuracy under faults such as Gaussian drift and FGSM perturbations. SHAP explanations showed consistent feature importance (e.g. gyroscope delta and latency spike) with Jaccard indices averaging 0.705 across clients. Real-time inference latency was under 60 ms on edge devices, validating practicality for deployment. Ablation studies confirmed contributions from SHAP reweighting and attention layers. Originality/value This work pioneers the integration of explainable AI (SHAP) into federated learning for fault diagnosis in multi-agent autonomous systems, bridging gaps in privacy-preserving, interpretable and robust safety solutions. Unlike prior studies focused on accuracy alone, it embeds transparency during training, enabling traceable decisions for regulatory compliance and public trust. The framework's entropy-based reweighting enhances convergence and resilience, offering scalable value for safety-critical applications such as UAV swarms and vehicle platoons, advancing intelligent manufacturing and special equipment reliability. ER - TY - JOUR TI - Development of a Low-Noise and High-Efficiency Propeller Using Divergent Riblet Surface Pattern PY - 2023 PB - American Institute of Aeronautics and Astronautics SN - 0001-1452 T2 - AIAA Journal DO - 10.2514/1.j062602 UR - https://doi.org/10.2514/1.j062602 DA - 2023-02-27 AU - Zheng, Chuntai C1 - The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China AU - Chen, Wangqiao C1 - The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China AU - Wu, Han C1 - The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China AU - Zhou, Peng C1 - The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China AU - Zhong, Siyang C1 - The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China AU - Zhang, Xin C1 - The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China LA - en KW - China KW - Aerospace KW - Bay KW - Engineering KW - Science and engineering KW - Management KW - History KW - Civil engineering KW - Archaeology KW - Engineering ethics KW - Aerospace engineering VL - 61 IS - 4 SP - 1876 EP - 1880 ER - TY - GEN TI - Drone Delivery Route Planning for the Low-Altitude Economy: An Improved Gray Wolf Optimization Algorithm Based on Niche Technology PY - 2026 DO - 10.1109/icaace69793.2026.11509210 UR - https://doi.org/10.1109/icaace69793.2026.11509210 DA - 2026-03-20 AU - Yan, Xiaoya C1 - Business School, Wuhan Huaxia University of Technology,Wuhan,China AU - Duan, Wenxing C1 - Business School, Wuhan Huaxia University of Technology,Wuhan,China AU - Ma, Jiangwei C1 - Business School, Wuhan Huaxia University of Technology,Wuhan,China AU - JIANG, Changbin C1 - School of Management, Wuhan University of Technology,Wuhan,China AU - Cai, Kui C1 - Business School, Wuhan Huaxia University of Technology,Wuhan,China KW - Drone KW - Route planning KW - Niche KW - Gray (unit) KW - Optimization algorithm KW - Optimization problem SP - 1537 EP - 1540 ER - TY - JOUR TI - Special issue on “drones as enablers of novel services: operational and technology challenges” PY - 2021 PB - Springer Science+Business Media SN - 1617-4909 T2 - Personal and Ubiquitous Computing DO - 10.1007/s00779-021-01535-2 UR - https://doi.org/10.1007/s00779-021-01535-2 DA - 2021-03-14 AU - Bernardos, Ana C1 - Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense 30, 28040, Madrid, Spain AU - Besada, Juan C1 - Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense 30, 28040, Madrid, Spain AU - Garcı́a, Jesús C1 - Universidad Carlos III de Madrid, Campus de Colmenarejo, Avd. de Gregorio Peces-Barba Martínez, 22, 28270, Colmenarejo, Madrid, Spain AU - Saitô, Hideo C1 - Keio University, Faculty of Science and Technology, Yagami Campus, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan AU - Marti, Patrizia C1 - University of Siena, Via Roma 56, 53100, Siena, Italy LA - en KW - Drone KW - Computer science VL - 26 IS - 4 SP - 1083 EP - 1084 ER - TY - JOUR TI - A Standardized Protocol for Medical Specimen Delivery by Uncrewed Aerial Vehicles in Multi-Campus Hospitals PY - 2026 PB - MyJOVE SN - 1940-087X T2 - Journal of Visualized Experiments DO - 10.3791/72147 UR - https://doi.org/10.3791/72147 DA - 2026-07-17 AU - Dai, Xiaoyan C1 - Logistics Support Department, Zigong First People's Hospital AU - Zeng, Xiaoling C1 - Logistics Support Department, Zigong First People's Hospital AU - Zou, Ying C1 - Office of the Vice President, Zigong First People's Hospital AU - Qiu, Tao C1 - Department of Neurology, Zigong First People's Hospital; qiutao_zgyyy@163.com LA - en KW - Protocol (science) KW - MEDLINE KW - Medical device KW - Medical equipment IS - 233 ER - TY - JOUR TI - Digital agronomy: smart decision-support workflow for climate‑resilient farming in the Kyiv agglomeration PY - 2025 PB - National University of Life and Environmental Sciences of Ukraine SN - 2306-1677 T2 - Zemleustrìj kadastr ì monìtorìng zemelʹ DO - 10.31548/zemleustriy2025.03.04 UR - https://doi.org/10.31548/zemleustriy2025.03.04 DA - 2025-09-30 AU - Назаренко, В. C1 - National University of Life and Environmental Sciences of Ukraine LA - en KW - Workflow KW - Baseline (sea) KW - Usability KW - Agriculture KW - Process (computing) KW - Economic rent KW - Land use KW - Metropolitan area AB - Rapid metropolitan growth is reshaping agrarian viability in peri-urban regions. Using the Kyiv agglomeration as a data-rich testbed, this study couples a modular UAV–AI decision-support workflow with empirical constraints emerging from land use, labour markets, logistics, and land rents. Regional evidence shows agricultural land has been squeezed to 0.21% of the area (≈0.18 k ha), against >54% green zones; wage differentials (16,500 UAH food processing vs 14,000 UAH agriculture) and transport costs of 20–250 UAH/km undermine farm margins and labour retention, while prime‑zone rents up to 25 million UAH/ha/year intensify conversion pressure (Kyiv & oblast baseline tables and figures). These structural frictions motivate digital agronomy that is explicitly policy‑ and cost-aware. We therefore prototype a decision-support workflow that fuses UAV/satellite imagery, in-field IoT, and historical climate/crop data with administrative‑economic layers (rents, wage gradients, haulage costs). The system translates multisource inputs into actionable stress detection, irrigation timing, and input allocation recommendations. At the same time, a logistics module evaluates route/vehicle choices under peri-urban cost profiles—a stakeholder‑co-design process (farmers, processors, and planners) anchors usability and transferability. We report (1) the peri-urban baseline for Kyiv (land, wages, logistics, enterprise distribution), (2) the architecture of the UAV–AI workflow and integration points with farm CRMs and public agri‑data, and (3) an evaluation framework linking agronomic KPIs to spatial‑economic constraints for resilient adoption. The approach is designed for cross-border replication (Ukraine - Germany) and to inform respectable policy outputs on digital land management and peri-urban agrifood resilience. ER - TY - THES TI - Digitization of Wind Tunnel Experiments: An AI-Based Approach to Wind Field Reconstruction and Visualization PY - 2026 PB - Concordia University T2 - Spectrum Research Repository (Concordia University) DA - 2026-01-01 AU - Geng, Dingyang LA - en KW - Visualization KW - Wind tunnel KW - Airflow KW - Terrain KW - Flow (mathematics) KW - Field (mathematics) KW - Turbulence KW - Wind engineering KW - Wind direction ER - TY - JOUR TI - After the Earthquake in the Low-Altitude Economy, Drones Deliver Relief Supplies PY - 2025 SN - 2688-8645 T2 - Scientific Journal of Technology DO - 10.54691/nqqjqd60 UR - https://doi.org/10.54691/nqqjqd60 DA - 2025-09-21 AU - Zhu, Xinyu C1 - North China University of Science and Technology , Tangshan Hebei , 063210 , China AU - Chu, Jianyu C1 - North China University of Science and Technology , Tangshan Hebei , 063210 , China LA - en KW - Drone KW - Aviation KW - Variety (cybernetics) KW - Search and rescue KW - Emergency management KW - Order (exchange) VL - 7 IS - 9 SP - 6 EP - 17 AB - The low-altitude economy is developing rapidly, and UAV post-earthquake rescue is helpful to improve the emergency response ability of post-earthquake rescue. Due to road damage, complex terrain, and uncertainty of meteorological conditions, ground rescue in the post-earthquake disaster area often cannot carry out full coverage of the earthquake-stricken area with high efficiency, while UAV has the characteristics of flexibility, dynamic real-time information, and intelligence, which is conducive to the application of post-earthquake rescue. The low-altitude economy has become a worldwide development trend, among which the development of low-altitude economy including UAM (UrbanAirMobility, urban air transportation), regional aviation logistics, and drones has become an important part of the global trend, and a variety of UAV application services and application scenarios such as logistics, environmental monitoring, and emergency rescue have been developed. Based on the background of low-altitude economic development, this paper discusses the application of UAV post-earthquake rescue, and analyzes the technical problems such as UAV post-earthquake rescue application technology, rescue material aerial throwing application, UAV route modeling airspace management application, etc., in order to play a positive role in post-earthquake rescue emergency response. ER - TY - DATA TI - A Standardized Protocol for Medical Specimen Delivery by Uncrewed Aerial Vehicles in Multi-Campus Hospitals PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20609945 UR - https://doi.org/10.5281/zenodo.20609945 DA - 2026-06-09 AU - tao, qiu KW - Protocol (science) KW - Container (type theory) KW - General partnership KW - Emergency medical services KW - Health care KW - Traffic congestion AB - Efficient transportation of medical specimens across multi-campus hospital networks is frequently hindered by urban traffic congestion and traditional vehicle delays. This protocol outlines a comprehensive methodology for establishing an uncrewed aerial vehicle (UAV)-based medical logistics system to achieve rapid, safe, and cost-aware specimen delivery. The procedure details the end-to-end implementation framework, beginning with quantitative demand assessment, stakeholder consultation, tender preparation, and strategic partnership modeling. Subsequent critical steps include flight route planning, regulatory airspace approval, UAV model selection, container validation, base station construction, and standardized personnel training. Furthermore, the protocol covers trial operations, system calibration, routine maintenance, and emergency response mechanisms prior to formal deployment. Application of this protocol at a tertiary hospital successfully established a three-campus UAV network. Representative results demonstrate that this method reduced average transport time by nearly two-thirds and decreased direct operational costs by 62%, while maintaining a 100% specimen integrity rate in the reported operational period. By providing a multidimensional analysis of technical, operational, regulatory, and managerial procedures, this standardized framework enables healthcare institutions to evaluate, replicate, and scale low-altitude smart medical logistics networks in complex urban environments. ER - TY - GEN TI - Applications and Assessments of Uncrewed Aerial Vehicles for Dual-Use Public Good and Military Missions PY - 2026 DO - 10.4050/tm-2026-vlada-5184 UR - https://doi.org/10.4050/tm-2026-vlada-5184 DA - 2026-01-27 AU - Scott, Robert C1 - VFS Member AU - Conley, Sarah C1 - NASA Ames Research Center KW - Drone KW - Aerial survey KW - Search and rescue KW - Public transport KW - Emergency response KW - Emergency management SP - 1 EP - 13 ER - TY - DATA TI - A Standardized Protocol for Medical Specimen Delivery by Uncrewed Aerial Vehicles in Multi-Campus Hospitals PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20609944 UR - https://doi.org/10.5281/zenodo.20609944 DA - 2026-06-09 AU - tao, qiu KW - Protocol (science) KW - Container (type theory) KW - General partnership KW - Emergency medical services KW - Health care KW - Traffic congestion AB - Efficient transportation of medical specimens across multi-campus hospital networks is frequently hindered by urban traffic congestion and traditional vehicle delays. This protocol outlines a comprehensive methodology for establishing an uncrewed aerial vehicle (UAV)-based medical logistics system to achieve rapid, safe, and cost-aware specimen delivery. The procedure details the end-to-end implementation framework, beginning with quantitative demand assessment, stakeholder consultation, tender preparation, and strategic partnership modeling. Subsequent critical steps include flight route planning, regulatory airspace approval, UAV model selection, container validation, base station construction, and standardized personnel training. Furthermore, the protocol covers trial operations, system calibration, routine maintenance, and emergency response mechanisms prior to formal deployment. Application of this protocol at a tertiary hospital successfully established a three-campus UAV network. Representative results demonstrate that this method reduced average transport time by nearly two-thirds and decreased direct operational costs by 62%, while maintaining a 100% specimen integrity rate in the reported operational period. By providing a multidimensional analysis of technical, operational, regulatory, and managerial procedures, this standardized framework enables healthcare institutions to evaluate, replicate, and scale low-altitude smart medical logistics networks in complex urban environments. ER - TY - GEN TI - Low-altitude economy: institutional, infrastructural, and innovative development aspects PY - 2025 DO - 10.47813/mip.6.2025.3004 UR - https://doi.org/10.47813/mip.6.2025.3004 DA - 2025-01-01 AU - Podoplelova, Valeria C1 - Krasnoyarsk State Agrarian University , Krasnoyarsk , Russia C1 - Sochi State University , Sochi , Russia AU - Pokusaev, D.S. C1 - Federal Research Center Krasnoyarsk Scientific Center of the C1 - Федеральный исследовательский центр Красноярский научный центр Сибирского отделения Российской академии наук , Красноярск , Россия AU - Kuzmik, S.R. C1 - Federal Research Center Krasnoyarsk Scientific Center of the C1 - Федеральный исследовательский центр Красноярский научный центр Сибирского отделения Российской академии наук , Красноярск , Россия LA - en KW - Identification (biology) KW - Key (lock) KW - Cluster analysis KW - Service (business) KW - Development (topology) KW - Work (physics) SP - 135 EP - 145 AB - The article examines the institutional, infrastructural, and innovative aspects of the development of the low-altitude economy, driven by the extensive integration of unmanned aerial vehicles in agriculture, construction, logistics, energy, tourism, and environmental protection. Systematic analysis of recent publications and statistical data demonstrates that UAV implementation accelerates digitalization, optimizes logistics, enhances efficiency, and enables new service models in urban and regional contexts. Key growth drivers, institutional challenges, and development prospects of the sector are identified, along with scenarios for clustering and regulatory support. The result of the study is a structured assessment of the sector's transformation and identification of further research directions. ER - TY - JOUR TI - Optimisation analysis of unmanned coordination path considering line crossing and various disturbance factors PY - 2026 PB - Inderscience Publishers SN - 1466-6642 T2 - International Journal of Information and Communication Technology DO - 10.1504/ijict.2026.154348 UR - https://doi.org/10.1504/ijict.2026.154348 DA - 2026-01-01 AU - Wei, Zongchao C1 - School of Business and Management , Yellow River Conservancy Technical University , Kaifeng , 475004 , China AU - Wen, Jing LA - en KW - Energy consumption KW - Simulated annealing KW - Path (computing) KW - Disturbance (geology) KW - Energy (signal processing) KW - Stability (learning theory) KW - Line (geometry) VL - 27 IS - 68 SP - 1 EP - 26 AB - This study addresses the limitations of existing UAV logistics research, which often neglects communication instability and multiple disturbances in real-time path planning.The objective is to optimise coordinated UAV paths by considering line crossing and multifactorial disruptions to improve delivery efficiency and system robustness.The methodology enhances a simulated annealing algorithm with a sub-path local search operator, integrated with a communication sensing strategy and energy consumption model.Results from six test cases show the improved algorithm reduces average distance by 3.13-5.41%,computation time by 13.6-17.6%,and boosts path quality by 11.5% in communication-hostile settings.The algorithm effectively manages disturbances like wind, obstacles, and demand changes, enhancing stability and adaptability.This research offers a novel co-optimisation method for UAV coordination, balancing communication and energy efficiency, with significant practical value for urban delivery systems. ER - TY - THES TI - AI-driven 5G networks for autonomous positioning system platform PY - 2025 PB - Cranfield University T2 - CERES (Cranfield University) DA - 2025-05-01 AU - Mousa, Morad LA - en KW - Hybrid positioning system KW - Non-line-of-sight propagation KW - Multipath propagation KW - GNSS applications KW - Global Positioning System KW - Positioning system KW - Kalman filter KW - Wireless sensor network KW - Key (lock) ER - TY - JOUR TI - Comparative Analysis of Precise Positioning Methods for Unmanned Aerial Vehicles PY - 2025 SN - 2413-2527 T2 - Intellectual Technologies on Transport DO - 10.20295/2413-2527-2025-141-27-36 UR - https://doi.org/10.20295/2413-2527-2025-141-27-36 DA - 2025-03-24 AU - Lipanov, Il'ya C1 - Emperor Alexander I St. Petersburg State Transport University AU - Хомоненко, А. C1 - Emperor Alexander I St. Petersburg State Transport University C1 - Mozhaisky Military Aerospace Academy AU - Molodkin, Igor C1 - Emperor Alexander I St. Petersburg State Transport University LA - en KW - Aeronautics KW - Remote sensing KW - Computer science KW - Artificial intelligence KW - Aerospace engineering KW - Computer vision KW - Engineering KW - Geography VL - 0 IS - 1 SP - 27 EP - 36 AB - With the advancement of unmanned aerial vehicles (UAVs), ensuring an object’s precise positioning has become one of the key challenges especially in environments with limited or no satellite signals. Purpose: to conduct a comparative analysis of Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) methods to evaluate their effectiveness under various UAV operational conditions. Results: the study revealed that RTK provides high real-time accuracy when a base station is available making it suitable for tasks requiring rapid analytics, such as in urban agglomerations. In contrast, PPP enables autonomous positioning and is better suited for remote areas, though it requires an initialization period to achieve high accuracy. The comparison showed that both methods have unique advantages and limitations that define their applicability in different usage scenarios. Practical significance: the study’s findings can be used to optimize the selection and implementation of positioning systems in various industries, such as cartography, logistics, and environmental monitoring enhancing the efficiency and reliability of UAV operations. Discussion: further refinement of these methods is recommended to improve their effectiveness and expand their applications including integration with modern UAV control systems. ER - TY - JOUR TI - Review of Autonomous Flight Control Technology for Unmanned Aerial Vehicles in Complex Environments PY - 2026 PB - Ivyspring International Publisher SN - 2664-9640 T2 - Scientific Journal of Intelligent Systems Research DO - 10.54691/63cn9j94 UR - https://doi.org/10.54691/63cn9j94 DA - 2026-05-14 AU - Hou, Aolin KW - Obstacle KW - Obstacle avoidance KW - Context (archaeology) KW - Reinforcement learning KW - Key (lock) KW - Drone KW - Field (mathematics) KW - Automation VL - 8 IS - 4 SP - 43 EP - 50 AB - With the rapid advancement of Unmanned Aerial Vehicles (UAVs) technology, its application value and penetration have been continuously enhanced in various fields, especially in surveying and mapping exploration, emergency rescue, logistics and transportation, and military fields. However, in some complex environments such as urban building clusters, indoor spaces, mountainous terrain, and areas with dynamic obstacles, achieving safe and efficient autonomous flight of UAVs still faces numerous difficulties and challenges. This paper systematically reviews the autonomous flight control technology of UAVs in these complex environments, elaborates on the core technical framework of this field, and introduces the latest research achievements and future development trends at home and abroad. It focuses on four key modules: environmental perception, path planning, obstacle avoidance, and robust control systems. Research shows that multi-sensor fusion technology for perception, intelligent path planning based on deep reinforcement learning, multi-UAV cooperative obstacle avoidance technology, and robust control technology driven by reinforcement learning have become the main research trends in this field. By comprehensively sorting out relevant research results at home and abroad, this paper constructs the context of technological development, hoping to provide certain reference for future research in the field of UAV autonomous flight control. ER - TY - JOUR TI - A Spatio-Temporal Collaborative Improved Multi-Strategy Dung Beetle Optimization Algorithm for 3D Path Planning of Multiple Unmanned Aerial Vehicles in Cities PY - 2026 T2 - Preprints.org DO - 10.20944/preprints202603.1669.v1 UR - https://doi.org/10.20944/preprints202603.1669.v1 DA - 2026-03-23 AU - Yu, Yaowei C1 - College of Civil Aviation , Nanjing University of Aeronautics and Astronautics , Nanjing 211106 , China AU - Le, Meilong C1 - College of Civil Aviation , Nanjing University of Aeronautics and Astronautics , Nanjing 211106 , China KW - Motion planning KW - Obstacle avoidance KW - Population KW - Benchmark (surveying) KW - Obstacle KW - Path (computing) KW - Convergence (economics) KW - Nash equilibrium KW - Particle swarm optimization AB - Three-dimensional collaborative path planning for multiple unmanned aerial vehicles (UAVs) in low-altitude urban environments with dense buildings constitutes a typical high-dimensional NP-hard problem. Conventional swarm intelligence algorithms suffer from critical limitations such as poor initial population quality, imbalance between global exploration and local exploitation, inadequate collaborative performance, susceptibility to local optima, and weak adaptability to dynamic environments. To overcome these challenges, this paper proposes a Spatio-Temporal Cooperative Improved Multi-Strategy Dung Beetle Optimization (STC-IMSDBO) algorithm tailored for 3D collaborative path planning of multi-UAV in urban area. The algorithm integrates five core enhancement strategies: first, an airspace-constrained sampling strategy enhances the uniformity and validity of initial population distribution; second, a spatio-temporal coupled iteration strategy achieves adaptive balancing between individual obstacle avoidance and multi-UAV coordination; third, a cooperative adaptive weight strategy optimizes iterative stability; fourth, a game mechanism theoretically guarantees collision-free Nash equilibrium for multi-UAV paths; and finally, a receding horizon strategy enables real-time path replanning in dynamic environments. Additionally, a multi-objective optimization function encompassing both individual flight costs and multi-UAV collaborative costs is formulated, comprehensively addressing core requirements including energy consumption, trajectory smoothness, obstacle avoidance, airspace compliance, and collision prevention. STC-IMSDBO is benchmarked against six mainstream path planning algorithms using test functions and four urban scenarios of varying complexity. Experimental results demonstrate that STC-IMSDBO exhibits superior stability, higher solution accuracy, and faster convergence in most benchmark functions. In path planning experiments, the algorithm achieves 6.12%–36.24% shorter mean path lengths, 14.1%–39.47% faster convergence, zero collision rate, 100% dynamic obstacle avoidance success, along with improved path smoothness and reduced energy consumption. Ablation studies, theoretical convergence proofs, nonparametric statistical tests, and real-world urban scenario simulations further validate the effectiveness of individual strategies and the algorithm’s global optimality. The proposed method demonstrates broad applicability in urban multi-UAV operations such as logistics delivery, emergency inspection, and traffic management. ER - TY - CHAP TI - Future of Drones PY - 2026 T2 - River Publishers eBooks DO - 10.1201/9788743812418-9 UR - https://doi.org/10.1201/9788743812418-9 DA - 2026-05-22 AU - Kumar, Kaushik AU - ŞENARAS, Arzu AU - Ranjan, Chikesh AU - Şenaras, Onur LA - en KW - Drone KW - Swarm behaviour KW - Propulsion KW - Key (lock) SP - 289 EP - 336 ER - TY - THES TI - Optimizing Last-Mile Deliveries with New Technology and Strategies PY - 2024 T2 - Theseus (Ammattikorkeakoulujen) DA - 2024-01-01 AU - Huopaniemi, Iivo LA - en KW - Last mile (transportation) KW - Popularity KW - Context (archaeology) KW - Emerging technologies KW - Mile KW - Process (computing) AB - The popularity of online shopping and e-commerce has skyrocketed in the recent years. This change in consumer behavior has brought forward numerous new challenges, in the context of the final mile of logistics. These challenges come especially in the form of the three dimensions of sustainability: environment, social, and economic sustainability. In order to combat these challenges, logistics companies now look for new ways of optimizing their last mile delivery (LMD) systems. This process of optimization involves new technologies and strategies, which companies are scrambling to consider, test, and implement. Newly surfaced technologies such as electric vehicles (EV), unmanned aerial vehicles (UAV), and autonomous ground vehicles (AGV), continue to show impressive potential for last mile delivery applications, while newly surfaced strategies, such as underground logistics systems (ULS), pickup points, and crowdshipping show potential for usage of new technologies in them. Together, the new technologies and strategies aim to make last mile deliveries sustainable, improving the environmental, social, and economic conditions of consumers and companies in urban areas alike. ER - TY - JOUR TI - Simulation-Based Validation of Delivery Drone Energy Consumption PY - 2025 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5252367 UR - https://doi.org/10.2139/ssrn.5252367 DA - 2025-01-01 AU - Choi, SungHyuk C1 - Queen Mary University of London C1 - Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom LA - en KW - Drone KW - Energy consumption KW - Computer science KW - Consumption (sociology) KW - Energy (signal processing) KW - Engineering KW - Statistics KW - Mathematics KW - Electrical engineering KW - Sociology ER - TY - JOUR TI - Autonomous Drone Delivery Systems PY - 2026 SN - 2582-3930 T2 - INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT DO - 10.55041/ijsrem58101 UR - https://doi.org/10.55041/ijsrem58101 DA - 2026-03-23 AU - Suresh, S. C1 - Department of Electrical and Electronics Engineering, Vimal Jyothi Engineering College AU - Francis, Tinu C1 - Department of Electrical and Electronics Engineering, Vimal Jyothi Engineering College AU - Raj, Aravind C1 - Department of Electrical and Electronics Engineering, Vimal Jyothi Engineering College AU - Paleri, Devadathan C1 - Department of Electrical and Electronics Engineering, Vimal Jyothi Engineering College AU - cherukot, Karthik C1 - Department of Electrical and Electronics Engineering, Vimal Jyothi Engineering College KW - Drone KW - Global Positioning System KW - Plan (archaeology) KW - Flight plan KW - Assisted GPS KW - Robot VL - 10 IS - 03 SP - 1 EP - 9 ER - TY - THES TI - Research on the Sustainable Development Path of Unmanned Aerial Vehicle Delivery in China's E-commerce PY - 2025 T2 - Theseus (Ammattikorkeakoulujen) DA - 2025-01-01 AU - Chen, Junhua LA - en KW - Sustainable development KW - Obstacle KW - Government (linguistics) KW - Operational efficiency KW - Drone KW - Key (lock) ER - TY - JOUR TI - Reducing rural health inequity through drone-based delivery: a spatial multi-criteria decision framework prioritizing epidemiological vulnerability in hub location PY - 2026 PB - Frontiers Media SN - 2296-2565 T2 - Frontiers in Public Health DO - 10.3389/fpubh.2026.1897414 UR - https://doi.org/10.3389/fpubh.2026.1897414 DA - 2026-09-14 AU - Zhong, Jingling C1 - Research Center for the Smart Health and Elderly Care Industry in the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou Huashang College C1 - School of Management, Guangzhou Huashang College AU - Zhao, Yan C1 - School of Management, Guangzhou Huashang College LA - en KW - Baseline (sea) KW - Vulnerability (computing) KW - Spatial epidemiology KW - Rural area KW - Epidemiology KW - Catchment area KW - Population KW - Decision support system VL - 14 AB - Objective To alleviate spatial health inequities in rural areas by optimizing unmanned aerial vehicle (UAV) medical hub location based on an epidemiological vulnerability-driven approach, correcting for the tendency of current logistics-centric models to overlook high-need rural populations. Methods A spatial multi-criteria decision-making (MCDM) framework was developed for a simulated rural–urban fringe experiencing severe demographic aging. We defined a novel Epidemiological Vulnerability Index (EVI) incorporating three core dimensions: demographic aging, chronic disease burden (e.g., cardiovascular risks), and baseline healthcare deprivation (ground EMS delay). The framework integrates Geographic Information Systems (GIS) with the enhanced two-step floating catchment area (E2SFCA) method, Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank and allocate highly-prioritized UAV hub sites across 400 grid cells. All results are derived from a calibrated simulation environment and should be interpreted as model-based projections rather than empirical observations. Results Simulation results indicated that the “Digital Inverse Care Law” emerged naturally under traditional logistics models, which allocated 85% of hubs to low-vulnerability urban centers. In contrast, the EVI-driven framework strategically anchored 65% of hubs in deep rural pockets. Under the baseline parameter assumptions, this reallocation of UAV hubs reduced the simulated average rural emergency response time by 74.8% (from 45.2 to 11.4 min) in the model. The 11.4-min figure represents UAV delivery time to the incident location, not complete emergency intervention time. This modeled improvement brought an estimated 88.4% of the highly vulnerable aging population into the “Golden 15-Minute” survival window within the simulation. Conclusion By embedding epidemiological metrics into spatial planning, this framework provides a decision-support tool that is traceable and reproducible for health policymakers, based on model outputs rather than empirical field validation. It illustrates how targeted digital health infrastructure deployment could, under the model assumptions, contribute to narrowing the rural–urban emergency health divide. These findings are contingent on the specific model assumptions and require field validation and formal health-economic evaluation before real-world implementation. ER - TY - THES TI - fifth generation PY - None T2 - University of Klagenfurt AU - Mijić, Ivan LA - en KW - Artificial neural network KW - Process (computing) KW - Regression KW - Regression analysis KW - Focus (optics) KW - Kriging KW - Gaussian process KW - Support vector machine KW - Power (physics) ER - TY - JOUR TI - AERONAUTICAL DRONES, PART 2: CURRENT APPLICATIONS AND TRENDS PY - 2026 SN - 2247-3173 T2 - SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE DO - 10.19062/2247-3173.2026.27.22 UR - https://doi.org/10.19062/2247-3173.2026.27.22 DA - 2026-07-13 AU - BENCZE, Andrei C1 - “Transilvania” University of Brasov, Romania KW - Drone KW - Software deployment KW - Propulsion KW - Aviation KW - Principal (computer security) KW - System integration KW - Emerging technologies VL - 27 SP - 217 EP - 231 AB - This paper examines the current applications and emerging development trends of unmanned aerial vehicles (UAVs), with emphasis on the technological and operational evolution of contemporary drone systems across civil and military domains. The study reviews the expanding use of UAVs in precision agriculture, infrastructure inspection, logistics, emergency response, urban air mobility, intelligence, surveillance and reconnaissance (ISR), electronic warfare and tactical operations, highlighting the operational advantages offered by unmanned platforms in diverse mission environments. The principal challenges affecting future UAV deployment are also analyzed, including battery endurance, cybersecurity, communication reliability, regulatory compliance, airspace integration and environmental constraints. Attention is given also to the increasing role of artificial intelligence, autonomous mission execution, swarm coordination, advanced propulsion technologies and digital airspace management in shaping the next generation of unmanned aircraft systems. Current research directions indicate a transition from individually operated drones toward interconnected, intelligent and highly autonomous aerial systems capable of cooperative decision- making and large-scale deployment. The analysis shows that future progress in unmanned aviation will depend not only on improvements in aircraft performance but also on advances in systems integration, secure communications, resilient autonomy and regulatory frameworks that enable the safe and efficient operation of increasingly complex UAV ecosystems. ER - TY - JOUR TI - A COMPREHENSIVE REVIEW OF UNMANNED AERIAL VEHICLE TECHNOLOGY: CURRENT STATE AND FUTURE POSSIBILITIES PY - 2026 PB - IJEAST SN - 2455-2143 T2 - International Journal of Engineering Applied Sciences and Technology DO - 10.33564/ijeast.2026.v11i01.008 UR - https://doi.org/10.33564/ijeast.2026.v11i01.008 DA - 2026-05-01 AU - Henry, Rabinder C1 - Professor, ATLAS SkillTech University, Mumbai, India, AU - Zade, Nilima C1 - Faculty, Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University), Pune, India, KW - Drone KW - State (computer science) KW - Biomimetics KW - Space exploration KW - Architecture KW - Situation awareness KW - Convergence (economics) KW - Anthropocene VL - 11 IS - 01 SP - 51 EP - 62 AB - : Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have evolved from rudimentary military tools into sophisticated, AI-enabled autonomous platforms. They have been constantly transforming civilian, commercial, and defence ecosystems across the world. This paper presents a comprehensive review of UAV technology, tracing its development from the earliest radio-controlled aircraft of the 1930’s through the Cold War surveillance era. It also emphasizes the emergence of weaponised platforms and the contemporary explosion of civilian applications. A structured classification framework is presented, followed by an analysis of the seven-subsystem functional architecture common to modern UAVs. The current state of the art is surveyed across ten application domainsincluding precision agriculture, logistics and urban air mobility, infrastructure inspection, disaster response, environmental monitoring, archaeological survey, defence, molecular nano-drone medicine, space exploration, and construction. It also enumerates a comprehensive review of the innovative applications emerging from recent research. The paper concludes with a forward-looking synthesis of future possibilities including autonomous swarm intelligence, bio-inspired and morphologically adaptive designs, quantumenhanced sensing and communication, cognitive AI and neuromorphic autonomy, and planetary drone systems. Throughout, the convergence of artificial intelligence, advanced materials, 5G/6G communications, aerodynamics, and biomimetics are identified as the main drivers of next-generation UAV capabilities. ER - TY - JOUR TI - Improving Unmanned Aerial Vehicle Security as a Factor in Sustainable Development of Smart City Infrastructure: Automatic Dependent Surveillance–Broadcast (ADS-B) Data Protection PY - None PB - Federal Reserve Bank of St. Louis T2 - RePEc: Research Papers in Economics AU - Semenov, Serhii AU - Krupska-Klimczak, Magdalena AU - Mazurek, Patryk AU - Zhang, Minjian AU - Chernikh, Olena KW - Smart city KW - Robustness (evolution) KW - Sustainable development KW - Resilience (materials science) KW - Key (lock) KW - Drone KW - Sustainability KW - Warning system KW - Identification (biology) ER - TY - JOUR TI - オリジナル VTOL 固定翼ドローン研究の紹介 PY - 2025 PB - National Institute of Informatics T2 - Institutional Repositories DataBase (IRDB) DA - 2025-05-01 AU - 野田, 晋二 C1 - Department of Aeronautical Engineering , Daiichi Institute of Technology LA - ja KW - Drone KW - Airframe KW - Process (computing) KW - Range (aeronautics) IS - 第37号 SP - 1 EP - 6 AB - In recent years, unmanned aerial vehicle (UAV) technology has undergone rapid advancements.Among these developments, fixed-wing drones equipped with vertical take-off and landing (VTOL) capabilities have attracted significant attention.VTOL drones combine the ability to take off and land vertically, akin to helicopters, with the efficient flight enabled by fixed wings, similar to conventional aircraft.This hybrid functionality renders them highly promising for a wide range of applications, including urban logistics, disaster response, and agricultural monitoring.This paper presents research conducted since fiscal year 2020, focusing on the airframe design of VTOL fixed-wing drones.It introduces the development process and technical considerations involved in creating an original VTOL fixed-wing drone. ER - TY - GEN TI - Deployments of Aerial Aided Edge Computing in Real‐World Scenarios PY - 2026 DO - 10.1002/9781394336326.ch8 UR - https://doi.org/10.1002/9781394336326.ch8 DA - 2026-02-26 AU - Durai, R. C1 - Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India AU - Dhivyashree, M. C1 - Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India AU - Akshya, J. C1 - Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India AU - Sundarrajan, M. C1 - Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India AU - Dhanaraj, Rajesh C1 - Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India LA - en KW - Key (lock) KW - Enhanced Data Rates for GSM Evolution KW - Drone KW - Edge detection KW - Noise (video) SP - 217 EP - 244 ER - TY - GEN TI - A study on two-layer spatiotemporal corridor planning based on SVO PY - 2026 PB - IOP Publishing SN - 1742-6588 T2 - Journal of Physics Conference Series DO - 10.1088/1742-6596/3314/1/012014 UR - https://doi.org/10.1088/1742-6596/3314/1/012014 DA - 2026-09-01 AU - Wang, Yuan C1 - Beihang University AU - Jiang, Chengpeng C1 - Beihang Hangzhou Innovation Institute AU - Luo, Xiling C1 - Beihang University KW - Scalability KW - Trajectory KW - Voxel KW - Path (computing) KW - Key (lock) KW - Motion planning KW - Urban planning VL - 3314 IS - 1 SP - 012014 EP - 012014 AB - Abstract High-density urban Unmanned Aerial Vehicle (UAV) logistics demand a sophisticated balance between limited airspace resources and dynamic multi-path coupling. This paper proposes a hierarchical spatiotemporal planning framework grounded in a “capacity-driven trajectory decoupling” mechanism leveraging Sparse Voxel Octrees (SVO). By transforming unstructured 3D space into a multi-resolution topological network, we quantify the traffic-bearing limits of heterogeneous voxels through a physical scale-based capacity mapping. The framework synergizes an upper-layer capacity-aware A * algorithm with an improved lower-layer Windowed Hierarchical Cooperative A * (WHCA*) algorithm, effectively mitigating localized congestion and ensuring deterministic intra-node conflict resolution. Experimental results in complex urban scenarios demonstrate that the SVO-based model achieves a 10.4x spatial compression ratio and a 98.5% mission success rate, significantly outperforming traditional Conflict-Based Search (CBS) and WHCA* baselines in scalability and global throughput. ER - TY - JOUR TI - VLF-SPS: a visual localization framework based on salient position selection and cross-source image matching PY - 2025 PB - IOP Publishing SN - 0957-0233 T2 - Measurement Science and Technology DO - 10.1088/1361-6501/ae2e1f UR - https://doi.org/10.1088/1361-6501/ae2e1f DA - 2025-12-17 AU - Yong, Chengyou C1 - Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing City, Jiangsu Province, Nanjing, 210016, CHINA AU - Yuan, Cheng C1 - Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing City, Jiangsu Province, Nanjing, Jiangsu, 210016, CHINA AU - Lai, Jizhou C1 - Nanjing University of Aeronautics and Astronautics, General Road Campus of Nanjing University of Aeronautics and Astronautics, Moling Street, Jiangning District, Nanjing, Jiangsu Province, China, Nanjing, Jiangsu, 210016, CHINA AU - Ding, Churong C1 - Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing City, Jiangsu Province, Nanjing, Jiangsu, 210016, CHINA AU - Xia, Zhongqing C1 - Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing City, Jiangsu Province, Nanjing, Jiangsu, 210016, CHINA AU - Lyu, Pin C1 - Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning District, Nanjing City, Jiangsu Province, Nanjing, 210016, CHINA KW - Salient KW - Discriminative model KW - Position (finance) KW - Motion blur KW - Feature (linguistics) KW - Matching (statistics) KW - Feature extraction KW - Orientation (vector space) KW - Aerial image VL - 37 IS - 4 SP - 045109 EP - 045109 ER - TY - JOUR TI - Anomaly Management in Unmanned Aerial Vehicles: A Systematic Literature Review PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2026.3716292 UR - https://doi.org/10.1109/access.2026.3716292 DA - 2026-01-01 AU - Tan, Ivan C1 - Singapore Management University AU - Poskitt, Christopher C1 - Singapore Management University AU - Jiang, Lingxiao C1 - Singapore Management University AU - Shar, Lwin C1 - Singapore Management University LA - en KW - Systematic review KW - Systematic error KW - Anomaly (physics) KW - Radar tracker KW - Anomaly detection VL - 14 SP - 117991 EP - 118010 AB - Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, our review integrates operational and regulatory perspectives, explicitly linking technical anomalies to policy enforcement and compliance requirements. We systematically compare detection techniques, datasets, simulators, and evaluation practices, revealing significant fragmentation in datasets, limited real-world validation, and a lack of standardized real-time benchmarks. Our synthesis highlights key research challenges, including the sim-to-real gap, limited interpretability of learning-based detectors, and the scarcity of policy-aware anomaly management frameworks. Based on these findings, we outline emerging research opportunities for adaptive, explainable, and benchmark-driven anomaly management systems that support safe, transparent, and reliable UAV operations. We release our metadata for all the papers reviewed, as well as filters for easy sorting. ER - TY - JOUR TI - Anomaly management in unmanned aerial vehicles: A systematic literature review PY - 2026 PB - Singapore Management University T2 - Singapore Management University Institutional Knowledge (InK) (Singapore Management University) DA - 2026-07-01 AU - HAN, IVAN C1 - Singapore Management University AU - Poskitt, Christopher C1 - Singapore Management University AU - Jiang, Lingxiao C1 - Singapore Management University AU - Shar, Lwin C1 - Singapore Management University LA - en KW - Interpretability KW - Systematic review KW - Metadata KW - Anomaly detection KW - Software deployment KW - Enforcement KW - Software KW - Fragmentation (computing) KW - Drone AB - Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, our review integrates operational and regulatory perspectives, explicitly linking technical anomalies to policy enforcement and compliance requirements. We systematically compare detection techniques, datasets, simulators, and evaluation practices, revealing significant fragmentation in datasets, limited real-world validation, and a lack of standardized real-time benchmarks. Our synthesis highlights key research challenges, including the sim-to-real gap, limited interpretability of learning-based detectors, and the scarcity of policy-aware anomaly management frameworks. Based on these findings, we outline emerging research opportunities for adaptive, explainable, and benchmark-driven anomaly management systems that support safe, transparent, and reliable UAV operations. We release our metadata for all the papers reviewed, as well as filters for easy sorting at [1]. ER - TY - JOUR TI - Tracking of Moving Targets Through Asynchronous Measures PY - 2025 PB - Multidisciplinary Digital Publishing Institute SN - 2624-6120 T2 - Signals DO - 10.3390/signals6020019 UR - https://doi.org/10.3390/signals6020019 DA - 2025-04-10 AU - Facheris, Alberto C1 - Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy AU - Reggiani, Luca C1 - Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy LA - en KW - Asynchronous communication KW - Tracking (education) KW - Computer science KW - Artificial intelligence KW - Computer vision KW - Psychology KW - Telecommunications VL - 6 IS - 2 SP - 19 EP - 19 AB - Unmanned Aerial Vehicles (UAVs) have progressively gained interest in recent years due to the wide range of related applications, from aerial communications and autonomous flight to agriculture and logistics. However, accurate 3D localization is crucial for enabling these kinds of applications, and commonly used tracking algorithms are often performing unsatisfactorily in critical scenarios like urban canyons and environments, characterized by dense multipath and line of sight obstruction. In this work we derive a novel 3D tracking algorithm which, despite its mathematical simplicity, can efficiently track moving targets handling asynchronous arrival of the anchor measurements or obstructions of line-of-sight links and outperforming commonly used algorithms like the Extended Kalman Filter (EKF) and the Particle Filter (PF). The proposed algorithm tracks the 3D position, velocity, and acceleration of a moving target through the combination of range measurements, between the target and different anchors, which become available in numbers and time instants not necessarily ordered as usually assumed in these applications. We denote this condition as asynchronous measurements, meaning that the ranging measurements are not available from all the anchors and they refer to different positions of the UAV during the tracking. We also show that our estimator is optimal among the linear ones, meaning that within this class, it minimizes the estimation error variance. Finally, we explore the accuracy that can be achieved in simulated scenarios defined by realistic UAV altitudes, velocities, and trajectories, as well as typical ranging errors of wideband localization systems. ER - TY - JOUR TI - Optimizing Drone-to-Ground Coordination in Humanitarian Supply Chains Using Deep Reinforcement Learning PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20790140 UR - https://doi.org/10.5281/zenodo.20790140 DA - 2026-01-01 AU - Alfaro, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Monterrosa, Jorge C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) LA - en KW - Reinforcement learning KW - Payload (computing) KW - Aerospace KW - Software deployment KW - Drone KW - Adaptation (eye) KW - Key (lock) KW - Communications system KW - Synchronization (alternating current) KW - Heuristic AB - ABSTRACTThis research presents a novel framework for optimizing drone-to-ground coordination in humanitarian supply chains through the integration of aerospace telemetry systems and deep reinforcement learning (DRL). Developed within the CATERO Advanced Aerospace Engineering Division at the El Salvador Aerospace Institute (ESAI), the study addresses inefficiencies in disaster-response logistics characterized by uncertainty, infrastructure disruption, and time-sensitive decision-making. Traditional logistics frameworks rely on static routing and heuristic models that perform poorly in dynamic environments. This work introduces an adaptive system capable of real-time optimization under uncertainty. The foundation of the framework is a high-reliability aerospace telemetry architecture designed to maintain continuous communication between unmanned aerial vehicles (UAVs) and ground coordination nodes. Mathematical models are developed to characterize aerodynamic stability, signal propagation, and communication integrity under varying atmospheric conditions. These models are embedded into a simulation platform that evaluates performance baselines and defines optimal operational thresholds for UAV deployment in complex environments. The telemetry system ensures synchronization among distributed agents, enabling coordinated decision-making and mission execution. At the core of the system is a deep reinforcement learning model structured in a multi-agent configuration. Each drone operates as an autonomous agent capable of learning optimal behaviors through interaction with its environment. The state space is defined by telemetry-driven variables, including spatial positioning, velocity, payload status, battery levels, and communication signal strength. A multi-objective reward function is designed to balance competing priorities such as minimizing delivery time, maximizing energy efficiency, maintaining communication stability, and ensuring payload accuracy. This enables continuous adaptation in highly variable contexts. A key innovation of this research is the integration of real-time telemetry feedback into the DRL decision-making loop. This allows the system to dynamically respond to unexpected disruptions such as environmental obstacles, communication degradation, and shifting demand points. Additionally, predictive modeling techniques are incorporated to estimate future system states, improving resilience, and proactive decision-making. This significantly reduces operational uncertainty and enhances mission reliability. Simulation experiments compare the proposed framework against conventional methods such as shortest-path algorithms and static scheduling models. Results demonstrate significant improvements in delivery latency, energy efficiency, and operational resilience. The system achieves faster response times and extended UAV endurance while maintaining coordination stability. Furthermore, the multi-agent structure enhances fault tolerance, ensuring continued operation despite localized system failures. Overall, this research demonstrates the potential of integrating aerospace engineering with artificial intelligence to transform humanitarian logistics systems. The proposed approach improves efficiency, reliability, and adaptability in critical environments, ensuring the timely delivery of essential resources such as food, water, and medical supplies. The framework is scalable and applicable to broader domains including urban air mobility and defense logistics, establishing a foundation for next-generation autonomous supply chain systems. ER - TY - JOUR TI - Optimizing Drone-to-Ground Coordination in Humanitarian Supply Chains Using Deep Reinforcement Learning PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.20790141 UR - https://doi.org/10.5281/zenodo.20790141 DA - 2026-01-01 AU - Alfaro, Luis C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) AU - Monterrosa, Jorge C1 - El Salvador Aerospace Institute (ESAI) C1 - Advanced Technology Center for Education, Training and Official Resources (CATERO) LA - en KW - Reinforcement learning KW - Payload (computing) KW - Aerospace KW - Software deployment KW - Drone KW - Adaptation (eye) KW - Key (lock) KW - Communications system KW - Synchronization (alternating current) KW - Heuristic AB - ABSTRACTThis research presents a novel framework for optimizing drone-to-ground coordination in humanitarian supply chains through the integration of aerospace telemetry systems and deep reinforcement learning (DRL). Developed within the CATERO Advanced Aerospace Engineering Division at the El Salvador Aerospace Institute (ESAI), the study addresses inefficiencies in disaster-response logistics characterized by uncertainty, infrastructure disruption, and time-sensitive decision-making. Traditional logistics frameworks rely on static routing and heuristic models that perform poorly in dynamic environments. This work introduces an adaptive system capable of real-time optimization under uncertainty. The foundation of the framework is a high-reliability aerospace telemetry architecture designed to maintain continuous communication between unmanned aerial vehicles (UAVs) and ground coordination nodes. Mathematical models are developed to characterize aerodynamic stability, signal propagation, and communication integrity under varying atmospheric conditions. These models are embedded into a simulation platform that evaluates performance baselines and defines optimal operational thresholds for UAV deployment in complex environments. The telemetry system ensures synchronization among distributed agents, enabling coordinated decision-making and mission execution. At the core of the system is a deep reinforcement learning model structured in a multi-agent configuration. Each drone operates as an autonomous agent capable of learning optimal behaviors through interaction with its environment. The state space is defined by telemetry-driven variables, including spatial positioning, velocity, payload status, battery levels, and communication signal strength. A multi-objective reward function is designed to balance competing priorities such as minimizing delivery time, maximizing energy efficiency, maintaining communication stability, and ensuring payload accuracy. This enables continuous adaptation in highly variable contexts. A key innovation of this research is the integration of real-time telemetry feedback into the DRL decision-making loop. This allows the system to dynamically respond to unexpected disruptions such as environmental obstacles, communication degradation, and shifting demand points. Additionally, predictive modeling techniques are incorporated to estimate future system states, improving resilience, and proactive decision-making. This significantly reduces operational uncertainty and enhances mission reliability. Simulation experiments compare the proposed framework against conventional methods such as shortest-path algorithms and static scheduling models. Results demonstrate significant improvements in delivery latency, energy efficiency, and operational resilience. The system achieves faster response times and extended UAV endurance while maintaining coordination stability. Furthermore, the multi-agent structure enhances fault tolerance, ensuring continued operation despite localized system failures. Overall, this research demonstrates the potential of integrating aerospace engineering with artificial intelligence to transform humanitarian logistics systems. The proposed approach improves efficiency, reliability, and adaptability in critical environments, ensuring the timely delivery of essential resources such as food, water, and medical supplies. The framework is scalable and applicable to broader domains including urban air mobility and defense logistics, establishing a foundation for next-generation autonomous supply chain systems. ER - TY - JOUR TI - A Four-Dimensional Planning Framework for Drone-Enabled Mobility Systems: Integrating Goods, Information, Sensing, and Human Mobility PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2504-446X T2 - Drones DO - 10.3390/drones10090654 UR - https://doi.org/10.3390/drones10090654 DA - 2026-08-27 AU - Brocchini, Lorenzo C1 - Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy C1 - University Centre of Logistic Systems, University of Pisa, Via dei Pensieri 60, 57128 Livorno, Italy AU - Wang, Chenxi C1 - Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy C1 - University Centre of Logistic Systems, University of Pisa, Via dei Pensieri 60, 57128 Livorno, Italy AU - Pratelli, Antonio C1 - Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy AU - Conte, Dorothée C1 - University Centre of Logistic Systems, University of Pisa, Via dei Pensieri 60, 57128 Livorno, Italy AU - Farina, Alessandro C1 - Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy LA - en KW - Drone KW - Conceptual framework KW - Mobility model KW - Emergency management KW - Public transport KW - Conceptual model KW - Component (thermodynamics) KW - Mobile device VL - 10 IS - 9 SP - 654 EP - 654 AB - Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. ER - TY - GEN TI - Adaptive Coordination in Multi-agent Systems Inspired by Reinforcement Learning and Couzin-Leader Models PY - 2026 PB - Springer Science+Business Media SN - 0302-9743 T2 - Lecture notes in computer science DO - 10.1007/978-981-92-2888-1_6 UR - https://doi.org/10.1007/978-981-92-2888-1_6 DA - 2026-07-18 AU - Fu, Yu C1 - College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China AU - Ning, Tengfei C1 - College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China AU - Yu, Bin C1 - College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China LA - en KW - Reinforcement learning KW - Scalability KW - Resilience (materials science) KW - Embedding KW - Cohesion (chemistry) KW - Adaptive system KW - Fragmentation (computing) SP - 66 EP - 79 ER - TY - JOUR TI - Autonomous Drone for the Point-to-Point Navigation PY - 2026 SN - 2664-2042 T2 - Pakistan Journal of Engineering and Technology DO - 10.51846/vol8iss4pp33-39 UR - https://doi.org/10.51846/vol8iss4pp33-39 DA - 2026-03-31 AU - Islam, Moin C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan AU - Arshad, Asad C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan AU - Kamal, Asim C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan AU - Khan, Usama C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan AU - Raza, Hamza C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan AU - Taj, Abdul C1 - Department of Electrical Engineering, National University of Technology, Islamabad-04403, Pakistan KW - Obstacle KW - Drone KW - Obstacle avoidance KW - Path (computing) KW - Lidar KW - Controller (irrigation) KW - Object detection KW - Navigation system VL - 8 IS - 4 SP - 33 EP - 39 AB - Unmanned Aerial Vehicles (UAVs) are transforming modern logistics by enabling autonomous systems. However, safe navigation in urban and rural environments is still a challenge due to obstacles such as trees, buildings, houses and transmission wires. This research presents the development of an autonomous drone from point-to-point navigation, integrating real time obstacle avoidance. The system integrates a Pixhawk 6x as the flight controller and a Jetson Nano as the onboard processing unit. A camera and a Lidar sensor take the input from the real environment, enabling obstacle detection using a deep learning model (Yolov8) and Convolutional Neural Network (CNN). The Jetson Nano processes real time video streams, detects obstacles and sends corrective navigation commands to the Pixhawk flight controller using the MAV Link protocol. Based on the detected obstacles, the drone adjusts its trajectory. It ensures a safe and efficient flight path between pre-defined waypoints. The proposed system is tested in various environments. It shows good obstacle detection and real time flight path correction. The system is tested experimentally and the result shows an improvement in (1) real-time object detection accuracy, (2) obstacle avoidance efficiency and (3) navigation stability. Along with avoiding the obstacles, path correction algorithms reroute around the obstacles with minimal deviation from its original course. This research contributes to advancing autonomous UAV navigation systems, particularly in obstacle avoidance. Our findings show the potential for UAVs to work safely and autonomously in real world conditions. This work can be expanded to integrate LiDAR for enhanced depth sensitivity. It can expand the system’s capabilities for multi-object tracking and decision making in complex environments. ER - TY - JOUR TI - A Review of Technologies Related to the Application of Inertial Navigation on Unmanned Aerial Vehicles PY - 2025 SN - 2755-2721 T2 - Applied and Computational Engineering DO - 10.54254/2755-2721/2026.tj30153 UR - https://doi.org/10.54254/2755-2721/2026.tj30153 DA - 2025-12-03 AU - Zhang, Junlu C1 - Beijing 21st Century School, Beijing, China KW - GNSS applications KW - Swarm behaviour KW - Drone KW - Inertial navigation system KW - Global Positioning System KW - Sensor fusion KW - Scalability KW - GNSS augmentation VL - 207 IS - 1 SP - 186 EP - 191 AB - Swarm collaborative navigation of Unmanned Aerial Vehicles (UAVs) is critical for both civilian (e.g., agricultural machinery fleets, urban drone logistics) and military (e.g., tactical formations, search-and-rescue) applications. However, it faces significant challenges in Global Navigation Satellite System (GNSS)-denied environments: traditional GNSS signals are easily compromised by jamming, spoofing, or non-line-of-sight (NLOS) conditions, while standalone Inertial Navigation Systems (INS) suffer from inevitable error accumulation over time, making long-duration operation impractical. To address these limitations, INS fusion strategies have become the core of reliable swarm navigation. This review focuses on three mainstream INS fusion strategies for UAV swarm collaborative navigation in GNSS-denied scenarios: distributed GNSS/INS-Ultra-Wideband (UWB) fusion, swarm intelligence-optimized INS error modelling, and anti-interference-enhanced fusion architecture. It systematically analyses the underlying principles, advantages, limitations, and optimal application scenarios of each strategy by synthesizing recent experimental data (e.g., centimetre-level positioning accuracy of distributed UWB-INS fusion, 70% error reduction of swarm intelligence-based modelling) and technical insights. The review fills the existing gap of lacking systematic trade-off analysis among robustness, cost, and scalability of these strategies, and provides a practical guide for selecting appropriate fusion schemes based on specific swarm requirements (e.g., GNSS availability, cost budget, communication conditions). This work aims to support the development of more resilient and adaptable UAV swarm navigation systems in complex GNSS-denied environments. ER - TY - JOUR TI - Aplicação do Método GUT (Gravidade, Urgência E Tendência) nos Mapas de Danos das Pontes Duarte Coelho e Maurício de Nassau com o Auxílio de Software PY - 2025 SN - 2317-8604 T2 - Periódico Técnico e Científico Cidades Verdes DO - 10.17271/23178604134120255665 UR - https://doi.org/10.17271/23178604134120255665 DA - 2025-05-18 AU - de Albuquerque, Everson C1 - Graduando, UNICAP AU - da Rosa Teixeira, Igor C1 - Mestrando, UPE AU - Barros, Eduardo C1 - Professor Doutor, Unicap AU - do Nascimento, Carlos C1 - Doutorando, UFPE AU - Monteiro, Eliana C1 - Professora Doutora, UNICAP e Poli/UPE LA - en KW - Humanities KW - Philosophy VL - 13 IS - 41 AB - Bridges play a crucial role in the logistics of the Metropolitan Region of Recife (RMR), directly impacting the economy and urban development. Given their importance, monitoring their condition is essential to ensure safety and functionality. This study investigated the pathological manifestations in the Duarte Coelho and Maurício de Nassau Bridges in Recife/PE, aiming to create damage maps and systematically review the identified issues. Visual inspections were conducted using identification forms, recording the damages through photographs and Unmanned Aerial Vehicle (UAV) images. The damage maps, developed in AutoCAD, highlighted problems such as mold, moisture stains, efflorescence, cracks, concrete spalling, and reinforcement corrosion. The analysis indicated that most of these issues could be prevented with routine maintenance. The bridges were found to be in moderate condition, with corrosion and cracks being the highest-priority anomalies, classified using the GUT method with the assistance of software developed in Visual Studio Code. The study emphasizes the need for immediate repairs and the implementation of a regular maintenance plan, ensuring the preservation of these structures and their significance to the city. ER - TY - JOUR TI - Research on the Governance Challenges and Institutional Pathways of the Low-Altitude Economy: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area PY - 2026 T2 - Research Square DO - 10.21203/rs.3.rs-8005441/v1 UR - https://doi.org/10.21203/rs.3.rs-8005441/v1 DA - 2026-03-10 AU - Li, Yifei C1 - University of Malaya AU - HUANG, XINRUI C1 - University of Malaya KW - Corporate governance KW - Airworthiness KW - Liability KW - Certification KW - Authorization KW - Revocation KW - Obstacle KW - Agency (philosophy) ER - TY - JOUR TI - A Coalition Game for On-Demand Multi-Modal 3d Automated Delivery System PY - 2024 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.5066639 UR - https://doi.org/10.2139/ssrn.5066639 DA - 2024-01-01 AU - Moosavi, Farzan C1 - Toronto Metropolitan University AU - Farooq, Bilal C1 - Toronto Metropolitan University LA - en KW - Modal KW - Computer science KW - On demand KW - Human–computer interaction KW - Multimedia KW - Chemistry ER - TY - JOUR TI - Learning heuristics with Vision Transformers for risk-aware drone last-mile delivery PY - 2026 PB - Elsevier BV SN - 0951-8320 T2 - Reliability Engineering & System Safety DO - 10.1016/j.ress.2026.113054 UR - https://doi.org/10.1016/j.ress.2026.113054 DA - 2026-06-20 AU - Aldao, Enrique AU - Veiga-López, Fernando AU - Chanel, Caroline AU - Watanabe, Yoko AU - González-Jorge, H. LA - en KW - Drone KW - Heuristics KW - Transformer VL - 277 SP - 113054 EP - 113054 AB - for urban parcel transport, offering a sustainable and cost-effective alternative to traditional ground-based logistics. Such operations, however, introduce significant risks, requiring effective mitigation strategies and efficient path-planning tools. Risk-aware path planning in complex urban environments remains computationally demanding, as it involves a highly combinatorial sequential decision-making problem while requiring efficient online replanning to ensure reactivity. Efficient planners often rely on graph-based search methods, in which the heuristic used to guide the search plays a crucial role. However, in risk-aware path planning, heuristic estimation is particularly challenging, as risk evaluation forms part of the cost estimation and differs significantly from classical line-of-sight or Euclidean approaches. To address this limitation, this study presents a novel Deep Learning framework for estimating accurate and admissible heuristic functions in risk-aware UAV path planning. To this end, a comprehensive database of cost-to-go maps was generated using realistic 3D urban scenarios and a graph-based optimisation framework. This dataset was used to train a Vision Transformer (ViT) network capable of providing accurate cost-to-go estimates for various objective-function configurations. The proposed architecture incorporates an admissibility correction mechanism that regulates the trade-off between heuristic accuracy and admissibility, promoting either solution optimality or search efficiency. The method was benchmarked against conventional heuristics in terms of computation time and cost-to-go. Results show speed-ups of up to 20 times compared to traditional approaches while maintaining near-optimal solutions, with marginal increases in average path cost of the order of 0.1%. ER - TY - JOUR TI - Computational approaches to collision prevention in drone delivery: a systematic mapping study PY - 2026 PB - Springer Science+Business Media SN - 2199-4668 T2 - Journal of Reliable Intelligent Environments DO - 10.1007/s40860-026-00271-9 UR - https://doi.org/10.1007/s40860-026-00271-9 DA - 2026-06-01 AU - Correia, Leonel C1 - Federal University of Piauí (UFPI), Picos, Piauí, Brazil AU - Barbosa, Vandirleya C1 - Federal University of Piauí (UFPI), Picos, Piauí, Brazil AU - De Oliveira, Fabíola C1 - Federal University of ABC (UFABC), Santo André, São Paulo, Brazil AU - Bittencourt, Luiz C1 - University of Campinas (UNICAMP), Campinas, São Paulo, Brazil AU - Silva, Francisco C1 - Federal University of Piauí (UFPI), Picos, Piauí, Brazil LA - en KW - Drone KW - Context (archaeology) KW - Reliability (semiconductor) KW - Search and rescue KW - Collision KW - Work (physics) VL - 12 IS - 2 AB - Abstract Unmanned aerial vehicles (UAVs), commonly known as drones, have played a crucial role in applications ranging from environmental monitoring to rescue operations. In recent years, their use in commercial services, especially for deliveries, has become an area of great interest due to the potential to reduce costs, increase transport speed, and expand reach in hard-to-reach locations, including densely populated urban areas. This advance represents a significant development in the evolution of logistics, introducing new operational paradigms that connect technology and efficiency. Yet, the use of drones in urban environments is not without its challenges, particularly in the realm of safety. The high density of obstacles, the presence of multiple aerial devices, and the unpredictable nature of environmental conditions all contribute to an increased risk of collisions. This risk, if left unaddressed, could significantly impact the performance and reliability of operations. It is therefore imperative that we investigate existing approaches and propose strategies that seamlessly integrate technological advances with operational safety. This study presents a systematic literature review to explore the causes of collisions and prevention strategies in the context of drone delivery services. From an initial analysis of 533 papers, 34 primary studies were selected for a detailed assessment. The work classifies the adopted approaches, such as types of delivery, collision types and causes, forms of detour, as well as the papers contributions and adopted evaluation. Based on the presented review, we highlight the research challenges and remaining research gaps. The analysis results in a comprehensive taxonomy and visualizations that help in understanding the state of the art, pointing out future directions to improve the safety and efficiency of drone delivery operations. ER - TY - JOUR TI - Low-Altitude Economy as a System-of-Systems: A Review of Concepts, Enabling Infrastructure, Operational Scenarios, and Governance Pathways PY - 2026 PB - Institute of Electrical and Electronics Engineers SN - 2169-3536 T2 - IEEE Access DO - 10.1109/access.2026.3724656 UR - https://doi.org/10.1109/access.2026.3724656 DA - 2026-01-01 AU - Li, Haodong C1 - Institute of Science and Technology Innovation, Xinjiang Academy of Transportation Sciences Company Ltd AU - Wei, Zhenxun C1 - Institute of Science and Technology Innovation, Xinjiang Academy of Transportation Sciences Company Ltd AU - Cao, Zhijian C1 - Institute of Science and Technology Innovation, Xinjiang Academy of Transportation Sciences Company Ltd AU - Jumaybayi, Aheyelike C1 - Institute of Science and Technology Innovation, Xinjiang Academy of Transportation Sciences Company Ltd AU - Shatar, Rizwangul C1 - Institute of Science and Technology Innovation, Xinjiang Academy of Transportation Sciences Company Ltd LA - en KW - Corporate governance KW - Digital economy KW - Government (linguistics) KW - Information governance KW - Production (economics) VL - 14 SP - 130526 EP - 130547 AB - The low-altitude economy (LAE) is evolving from a policy-driven concept into a multidisciplinary research field spanning low-altitude airspace governance, unmanned aircraft systems, eVTOL platforms, public air routes, vertiports, digital traffic services, industrial applications, and social acceptance. However, existing knowledge remains fragmented across Chinese LAE studies and international urban air mobility, advanced air mobility, drone logistics, and airspace-management literatures. This review adopts a PRISMA-informed structured narrative protocol to synthesize representative studies published between 2020 and March 2026, and argues that LAE should be understood as a system-of-systems rather than as a relabeling of drones, air taxis, or general aviation modernization. The article makes five contributions. First, it clarifies the conceptual boundaries among LAE, general aviation, the UAV economy, and UAM/AAM. Second, it positions LAE against prior ecosystem, socio-technical, and transportation system-of-systems frameworks. Third, it develops and operationalizes a five-layer framework linking airspace resources, vehicles and manufacturing, physical and digital infrastructure, operational scenarios, and governance-based value realization. Fourth, it synthesizes key enabling conditions, including public air-route networks, UTM–ATM convergence, vertiports, cybersecurity, edge intelligence, digital twins, certification, and social legitimacy. Fifth, it compares scenario-specific deployability across mobility, logistics, public-service, industrial, environmental-monitoring, and regional-accessibility applications. The central conclusion is that LAE scalability will not be determined by aircraft innovation alone; it depends on coordinated readiness across infrastructure, regulation, digital observability, operational value, resilience, and public trust. ER - TY - THES TI - Time-optimal motion planning for autonomous flight using spatial reformulation PY - None PB - Hong Kong Polytechnic University T2 - PolyU Institutional Research Archive (Hong Kong Polytechnic University) AU - Chan, Yin LA - en KW - Drone KW - Payload (computing) KW - Motion planning KW - Quadcopter KW - Downwash KW - Key (lock) KW - Traffic congestion ER - TY - GEN TI - MAF-YOLO: An Improved YOLO for Dense Small-Object Detection PY - 2026 PB - Springer Science+Business Media SN - 0302-9743 T2 - Lecture notes in computer science DO - 10.1007/978-981-92-3504-9_24 UR - https://doi.org/10.1007/978-981-92-3504-9_24 DA - 2026-07-13 AU - Zhu, Fokun C1 - School of Computer Science, Guangdong University of Technology, Guangzhou, China AU - Zeng, An C1 - School of Computer Science, Guangdong University of Technology, Guangzhou, China AU - Pan, Dan C1 - School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, China AU - Lei, Yong C1 - School of Education, Guangzhou University, Guangzhou, China LA - en KW - Pyramid (geometry) KW - Feature (linguistics) KW - Feature extraction KW - Object detection KW - Aerial image KW - Pattern recognition (psychology) KW - Scale (ratio) KW - Aerial imagery SP - 291 EP - 302 ER - TY - THES TI - Design-Driven Innovation approach for future-proof air-ground infrastructure : Case Schumacher Quartier PY - 2023 PB - Tampere University of Applied Sciences T2 - Tampere University Institutional Repository (Tampere University) DA - 2023-01-01 AU - Stenroos, Joel LA - en KW - Popularity KW - Limiting KW - Sustainability KW - Service (business) KW - Urban planning KW - Drone ER - TY - JOUR TI - BEZPIECZEŃSTWO PILOTA DRONÓW W WARUNKACH ZAGROŻEŃ FPV NA DRÓGACH: MODEL PRZECIWDZIAŁANIA ORIENTOWANY NA RYZYKO PY - 2025 SN - 2956-333X T2 - Łódzkie Studia Humanistyczne DO - 10.32782/2956-333x/2025-3-7 UR - https://doi.org/10.32782/2956-333x/2025-3-7 DA - 2025-12-11 AU - Garasymyuk, Oleksandr C1 - Lwowski Państwowy Uniwersytet Bezpieczeństwa Życia KW - Jamming KW - Warning system KW - Electric power transmission KW - Parking lot KW - Security controls IS - 3 SP - 65 EP - 72 AB - The article is devoted to the study of aerial reconnaissance safety in conditions of ambush FPV threats on motorways, when strike UAVs wait for a long time on the roadside and strike targets as vehicles approach.It justifies a risk-based approach to convoy escort planning, shifting the focus from radio jamming to managing environmental vulnerabilities, reducing route predictability, and standardised SOPs. The scientific novelty lies in the integration of EMCON/OPSEC, engineering barriers, and close-range counter-FPV.Key factors in the effectiveness of ambush FPVs have been identified: exploitation of electronic warfare dead zones in the ground layer, wired or low-emission control, motion sensors for energy conservation and rapid response, and spatial conditions that facilitate concealment of the carrier, cable, and operator position. The limitations of the ‘jamming anything’ paradigm and the advisability of focusing on preventive risk mapping, movement variability, and minimisation of electromagnetic emissions are demonstrated.The structure of multi-layered countermeasures is investigated, combining organisational solutions (passage windows, time and speed profiling, checklists), engineering and sensor means (‘digital pickets’, roadside and cavity inspections, IR control from a suspension), and tactical escort algorithms with the allocation of close counter-FPV crews and a contour for detecting ambush operators with logistics damage. Effectiveness metrics are proposed for assessing route stability and SOP adaptation.The application of a risk-oriented model in three environments – open plain, forest belt, urban development – is analysed, taking into account lines of sight, multi-beam and relief screening, which form ‘pockets’ of low electronic warfare effectiveness. The practical value of SOPs with the fixation of emission ‘windows’, attack vector breaking algorithms, and incident analysis is substantiated. The proposed approach reduces the risk of a successful UAV operation and increases the survivability of aerial reconnaissance and transport convoys. ER - TY - JOUR TI - Super-Silent Propulsion: How To Reduce Drone Noise with Vortex Tricks PY - 2026 DO - 10.31224/6233 UR - https://doi.org/10.31224/6233 DA - 2026-01-09 AU - P, Mokshith C1 - Independent Research KW - Drone KW - Propulsion KW - Vortex KW - Thrust KW - Wake KW - Aerodynamics KW - Propeller KW - Noise (video) AB - The rapid proliferation of small unmanned aerial vehicles (UAVs) in urban, surveillance, and logistics roles has exposed a fundamental limitation of current propulsion systems: acoustic detectability. While electric propulsion eliminates combustion noise, aerodynamic noise generated by propellers remains dominant, persistent, and socially unacceptable in noise-sensitive environments. This paper proposes a vortex-centric aero-acoustic design philosophy for drone propulsion, arguing that noise suppression must be addressed at the level of vortex physics rather than post-hoc damping or active cancellation. We introduce the concept of Controlled Vortex De-Coherence (CVD)—a passive design framework that intentionally disrupts the spatial and temporal coherence of noise-producing vortical structures without degrading thrust efficiency. By reshaping vortex formation, stretching, diffusion, and wake entropy generation, tonal and broadband noise components can be substantially reduced at the source. The paper synthesizes physical insight from aero-acoustics, wake dynamics, and bio-fluid mechanics to develop practical, manufacturable strategies for low-noise propeller design. Unlike conventional approaches that trade noise for efficiency, the proposed methods reallocate aerodynamic energy from acoustically efficient modes to acoustically inefficient ones. The work reframes silent propulsion not as a materials or electronics problem, but as a flow-architecture problem, opening new pathways for patentable designs and scalable drone startups operating in regulated acoustic environments. ER - TY - GEN TI - Multi-Stage Multi-Agent Reinforcement Learning for on-Demand Food-Delivery Services with a Mixed Fleet of Human Couriers and Drones PY - 2025 DO - 10.1109/itsc60802.2025.11423842 UR - https://doi.org/10.1109/itsc60802.2025.11423842 DA - 2025-11-18 AU - Hu, Yulong C1 - The Hong Kong University of Science and Technology,Department of Civil and Environmental Engineering,Hong Kong SAR,China AU - Du, Yali C1 - King's College London,Department of Informatics,London,United Kingdom AU - Li, Sen C1 - The Hong Kong University of Science and Technology,Department of Civil and Environmental Engineering,Hong Kong SAR,China LA - en KW - Drone KW - Reinforcement learning KW - Action (physics) KW - Control (management) KW - Government (linguistics) SP - 734 EP - 739 ER - TY - JOUR TI - Speed-Based Tactical Deconfliction of Multiple Aircraft Around a Vertiport Through a Conservative Airspace Discretization Algorithm and Constraint Programming PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2226-4310 T2 - Aerospace DO - 10.3390/aerospace13060519 UR - https://doi.org/10.3390/aerospace13060519 DA - 2026-06-03 AU - Iriarte, Imanol C1 - Robotics and Autonomous Systems Group (RSAIT), University of the Basque Country (UPV-EHU), Manuel Lardizabal 1, 20018 San Sebastian, Spain C1 - Tecnalia, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 7, 20009 San Sebastian, Spain AU - Ramos, Estela C1 - Tecnalia, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 7, 20009 San Sebastian, Spain AU - Iglesias, I. C1 - Tecnalia, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 7, 20009 San Sebastian, Spain AU - Del Río, Josu C1 - Robotics and Autonomous Systems Group (RSAIT), University of the Basque Country (UPV-EHU), Manuel Lardizabal 1, 20018 San Sebastian, Spain C1 - Tecnalia, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 7, 20009 San Sebastian, Spain AU - Lasa, Joseba C1 - Tecnalia, Basque Research and Technology Alliance (BRTA), Mikeletegi Pasealekua 7, 20009 San Sebastian, Spain AU - Vilardaga, Santi C1 - Pildo Labs, Carrer de Bac de Roda, 120, 08019 Barcelona, Spain AU - Lucas, Sergi C1 - Pildo Labs, Carrer de Bac de Roda, 120, 08019 Barcelona, Spain AU - Sierra, Basilio C1 - Robotics and Autonomous Systems Group (RSAIT), University of the Basque Country (UPV-EHU), Manuel Lardizabal 1, 20018 San Sebastian, Spain LA - en KW - Discretization KW - Air traffic control KW - Aviation KW - Set (abstract data type) KW - Constraint (computer-aided design) KW - Air traffic management KW - Divergence (linguistics) KW - Minification VL - 13 IS - 6 SP - 519 EP - 519 AB - This article discusses a novel aircraft coordination algorithm for automated vertiport operation. New applications of Innovative Air Mobility (IAM) including inspection, logistics and security UAVs, Urban Air Mobility (UAM) or Regional Air Mobility (RAM) present a coordination challenge, especially near vertiports, as large numbers of vehicles with different characteristics share the airspace, and so avoiding collisions, optimizing resource usage and operating with low human intervention is important.In this paper, this problem is addressed by proposing a new formulation of the aircraft coordination problem that makes use of a discretized airspace to detect potential conflicts and collisions between cooperative and non-cooperative aircraft in the surroundings of a vertiport. The proposed algorithm not only considers the cells traversed by the aircraft, but also the set of adjacent cells, making the algorithm more conservative and robust than other algorithms found in the literature, and achieving a 100% conflict-detection rate. A mathematical model of aircraft dynamics is employed to turn high-level flight plans into detailed aircraft trajectories, using those trajectories to detect potential collisions. The deconfliction problem is formulated as a mixed-integer optimization program that computes orders of pass for every conflict while minimizing the divergence between requested time of arrival (RTA) and estimated time of arrival (ETA). This problem is implemented in OR-Tools to be solved by means of the CP-SAT solver. The validity of the solution is tested by extensive simulation, showing tactical coordination of up to 25 aircraft landing on a vertiport. ER - TY - GEN TI - Intelligent Drone Traffic Management with Multi-Agent Learning PY - 2026 DO - 10.1109/aiei69164.2026.11497249 UR - https://doi.org/10.1109/aiei69164.2026.11497249 DA - 2026-03-26 AU - Das, Archana C1 - New Horizon College of Engineering,Department of Information Science and Engineering,Bangalore,Karnataka,India AU - P, Dhamayanthi C1 - KGiSL Institute of Technology,Department of Computer Science and Engineering,Coimbatore,Tamil Nadu,India AU - Rajeswari, M C1 - Jeppiaar Institute of Technology,Department of Computer Science and Engineering,Chennai,Tamil Nadu,India AU - Revathy, J C1 - Christ the King Engineering College,Department of Artificial Intelligence and Data Science,Coimbatore,Tamil Nadu,India AU - J, Akshya. C1 - SRM Institute of Science and Technology,Department of Computational Intelligence,Chennai,Tamil Nadu,India AU - Sundarrajan, M C1 - SRM Institute of Science and Technology,Department of Networking and Communications,Chennai,Tamil Nadu,India AU - Choudhry, Mani C1 - SRM Institute of Science and Technology,Department of Computing Technologies,Chennai,Tamil Nadu,India KW - Drone KW - Key (lock) KW - Matching (statistics) KW - Feature (linguistics) KW - Training (meteorology) SP - 1 EP - 6 ER - TY - JOUR TI - AI-Based Garbage Segmentation from Aerial Drone Images Using YOLOV8 PY - 2026 SN - 2789-2557 T2 - International Journal of Artificial Intelligence and Machine Learning DO - 10.51483/ijaiml.6.9s.2026.194-209 UR - https://doi.org/10.51483/ijaiml.6.9s.2026.194-209 DA - 2026-01-01 AU - Naik, Sugali C1 - Research Scholar , Department of ECE , B.E.S.T Innovation University , Andhra Pradesh , India AU - Madam, Aravind C1 - Professor & Principal , Department of Electronics and Communication Engineering , West Godavari Institute of Science and Engineering , Prakashraopalem , Nallacherla Mandal , East Godavari District , Andhra Pradesh , India KW - Segmentation KW - Confusion matrix KW - False positive paradox KW - Garbage KW - Drone KW - Metric (unit) KW - Object detection KW - Precision and recall KW - Silhouette VL - 6 IS - 9s SP - 194 EP - 209 AB - The YOLOv8n-seg model has been used for AI garbage segmentation from drone images. The focus is on urban waste monitoring. The model achieved strong results for detection and segment performance with a box precision of 0.821, a recall of 0.656 and a mask mAP50 of 0.581 on the valid set. This metric reveals the capability of segmenting and localizing the trash correctly in the complex visual environment. The confusion matrix showed that the model performed very well in the classification of the background. However, there was one case where the background was incorrectly categorized as "garbage. This means that we need to further fine-tune the model so that both these classes are well-separated. Qualitative assessments demonstrated that in different aerial images, the garbage and other solid wastes were able to be detected with a confident prediction, although there were some false alarms for vegetation and textured surfaces. The loss, accuracy and recall measure have increased continually, indicating that the model continues to learn consistently as it is trained further. Looking at the recall/precision curves, it can also be seen that detection accuracy and completeness were not scalable when the confidence threshold is set to a higher value, as the highest F1 score value is 0.73 when the threshold is the lowest. Although the model has not yet been used on actual UAVs, it also showed the potential for real-time segmentation of video simulations, potentially offering integration in intelligent waste management systems and drone logistics. Future work is recommended that will reduce false positives and help distinguish background. As a result, the research supports lightweight YOLOv8 segmentation models as viable contenders for intelligent automated rubbish monitoring activities. ER - TY - THES TI - A solution technique for the truck and drone parcel delivery problem PY - 2025 T2 - University of Vienna DO - 10.25365/thesis.79578 UR - https://doi.org/10.25365/thesis.79578 DA - 2025-01-01 AU - Lidi, Márk LA - en KW - Context (archaeology) KW - European union KW - Truck AB - Diese Arbeit befasst sich mit den drängenden Herausforderungen des städtischen Güterverkehrs und der letzten Meile in der Europäischen Union. Insbesondere das starke Wachstum des Onlinehandels sowie die fortschreitende Urbanisierung erhöhen den Druck auf bestehende Transportinfrastrukturen. Der Straßengüterverkehr dominiert zwar weiterhin die Logistik, verursacht jedoch einen beträchtlichen Anteil an Treibhausgasemissionen, Luftschadstoffen und Verkehrsstaus (WEF, 2024; Sustainable Urban Logistics Plans, EU, 2023). Vor dem Hintergrund der europäischen Klimapolitik und ambitionierter Nachhaltigkeitsziele gewinnt daher die Entwicklung innovativer Konzepte an Bedeutung, um Transportprozesse mit den Emissionsreduktionsvorgaben in Einklang zu bringen (Levrey, 2024; Mahmoodi et al., 2025). In diesem Kontext wird ein heuristik-basierter Planungsansatz vorgestellt, der klassische Methoden der Routenoptimierung mit neuen Lieferkonzepten – darunter unbemannte Luftfahrzeuge (UAVs) und autonome Systeme – verbindet. Ziel ist es, Lieferketten so zu gestalten, dass technische Rahmenbedingungen wie begrenzte Drohnenreichweiten, Nutzlastgrenzen, Ladeinfrastruktur oder gesetzliche Flugverbotszonen berücksichtigt werden, ohne die praktische Anwendbarkeit durch übermäßige Rechenlast zu gefährden (Mahmoodi et al., 2025; Cicek et al., 2025). Die empirischen Ergebnisse verdeutlichen die Zielkonflikte zwischen Kosten, Zeitaufwand und ökologischen Effekten. Besonders in dicht besiedelten urbanen Räumen lassen sich mit kombinierten Drohnen-Lkw-Modellen die CO₂-Emissionen um bis zu 20 % im Vergleich zu rein dieselbetriebenen Lkw-Systemen senken (Bao et al., 2025). Damit liefert die Arbeit einen Beitrag zur wissenschaftlichen Debatte über nachhaltige urbane Logistik, indem sie sowohl theoretische Grundlagen als auch praxisnahe Ansätze zusammenführt. Der entwickelte Algorithmus unterstreicht das Potenzial hybrider Zustelllösungen, die den Einsatz von Drohnen mit Lkw und die Integration von E-Cargobikes kombinieren. Solche Konzepte eröffnen neue Möglichkeiten, Emissionen zu verringern und die Effizienz in der letzten Meile zu erhöhen. Über die wissenschaftliche Relevanz hinaus bieten die Erkenntnisse wertvolle Anhaltspunkte für politische Entscheidungsträger, Logistikunternehmen und Stadtverwaltungen, die auf dem Weg zu sauberen, resilienten und intelligenten städtischen Transportsystemen sind – im Einklang mit dem European Green Deal sowie den Nachhaltigkeitsstrategien der Europäischen Kommission (Levrey, 2024; Sustainable Urban Logistics Plans, EU, 2023). ER - TY - CHAP TI - Enhanced Stability of Drones Using Optimized PID Control Systems PY - 2026 DO - 10.1201/9781003637714-29 UR - https://doi.org/10.1201/9781003637714-29 DA - 2026-07-08 AU - Mncube, Thembelihle AU - Owolawi, Pius AU - Du, Chunling LA - en KW - Control theory (sociology) KW - Stability (learning theory) KW - Control system KW - Drone KW - PID controller SP - 213 EP - 221 ER - TY - GEN TI - Experimental Analysis of Rotor Noise and Interference Effects in a Dual-Rotor Configuration PY - 2026 DO - 10.2514/6.2026-2077 UR - https://doi.org/10.2514/6.2026-2077 DA - 2026-01-08 AU - Garcia, Diana-Graca C1 - Rheinisch-Westfalische Technische Hochschule Aachen Fakultat fur Maschinenwesen AU - Hörnschemeyer, Ralf C1 - Rheinisch-Westfalische Technische Hochschule Aachen Fakultat fur Maschinenwesen AU - Stumpf, Eike C1 - Rheinisch-Westfalische Technische Hochschule Aachen Fakultat fur Maschinenwesen KW - Rotor (electric) KW - Aerodynamics KW - Noise (video) KW - Thrust KW - Propulsion KW - Wind tunnel KW - Propeller KW - Sensitivity (control systems) ER - TY - GEN TI - Stereo Depth-Camera Drone Tracking in GPS-Denied Environments PY - 2025 DO - 10.2514/6.2025-99199 UR - https://doi.org/10.2514/6.2025-99199 DA - 2025-01-01 AU - Taylor, Aidan C1 - AU - Nguyen, Alex C1 - AU - Christner, Nicholas C1 - AU - Athota, Saanvi C1 - AU - Mikhail, Joseph C1 - AU - Obregon, Marco C1 - AU - Jiang, Yixing C1 - AU - Nokes, Adam C1 - LA - en KW - Drone KW - Computer vision KW - Global Positioning System KW - Artificial intelligence KW - Computer science KW - Stereo camera KW - Tracking (education) KW - Stereopsis KW - Computer graphics (images) AB - This paper presents the development of an autonomous air traffic control (ATC) system utilizing stereo cameras and LiDAR to enhance drone tracking accuracy, particularly in GPS-denied environments. The rapid expansion of unmanned aerial vehicles (UAVs) across various industries has created an urgent need for efficient traffic management solutions. As the number of UAVs increases, planned drone hubs are emerging as centralized points for drone operation and coordination. However, large networks of drones coming in and out of drone hubs will exceed the capabilities of current air traffic control (ATC) systems. Additionally, many environments where drones operate—such as dense urban areas, mountainous regions, or indoor facilities—pose significant challenges for GPS-based navigation. Without external positional data, drones are forced to rely on internal guidance data from inertial measurement units (IMUs) and gyroscopes, but these devices are prone to severe error propagation over time. This project addresses these limitations by integrating stereo cameras and a LiDAR unit mounted on a fixed base station, which estimates drone positions by combining external sensor data with the drone’s internal guidance systems and comparing it to GPS data. Input data comes from both the internal guidance system of the drones, including IMUs and gyroscopes, and a fixed bay station with a mounted LiDAR and stereo camera. The bay station data will then be processed using software tools like ZED and OpenCV to give a drone positional estimate relative to the position of the bay station. This generated data will then be compared against the drone’s internal sensors and GPS data to quantify error margins. The initial aim of the study is to refine the positioning algorithm and, if the accuracy reaches an acceptable threshold, enable autonomous navigation via the external tracking methods without the need for GPS input. Several challenges must be addressed to ensure the feasibility of this system. Environmental conditions such as poor lighting, rain, and other visual obstructions can degrade camera and LiDAR-based tracking performance. Similarly, interference from multiple drones in high-traffic situations can introduce additional sensor noise. To mitigate these issues, open source vision-based detection techniques such as the YOLO object detection algorithm are employed and tested against various lighting conditions. The Drone Estimation Lab (DEL) focuses on research areas that support this study, including: Depth cameras and LiDAR for real-time drone tracking. Mapping of true positions against camera/LiDAR-based external tracking. Pathfinding and obstacle avoidance as stretch goals. This study achieves experimental validation using two drones, multiple stereo cameras, and a LiDAR unit in a controlled testing environment. The data gathered is processed and visualized using MATLAB to aid in optimizing the tracking algorithms used. Given the available computational resources, the external tracking computations are not constrained by onboard processing limitations. The outcomes of this research have significant implications for future drone operations in both commercial and industrial settings. Drone hubs will benefit from precise tracking methods that allow for efficient drone traffic management even in highly congested environments. Additionally, the ability for drones to coordinate within swarms will enable more sophisticated autonomous missions, such as search and rescue operations, environmental monitoring, and package delivery logistics. Beyond individual drone performance, an autonomous drone air traffic control system will help regulate large-scale UAV movement, reducing the risk of mid-air collisions and enhancing the reliability of drone-based services in urban and remote areas. These advancements will pave the way for the widespread adoption of UAV technology in scenarios where traditional GPS-based navigation is insufficient, ultimately ensuring safer and more efficient drone integration into modern airspace. ER - TY - JOUR TI - Transformation of Military Robotics in Defense Systems: A Multi-Domain Capability Analysis PY - 2026 SN - 3031-8939 T2 - CENDEKIA Jurnal Penelitian dan Pengkajian Ilmiah DO - 10.62335/cendekia.v3i6.2616 UR - https://doi.org/10.62335/cendekia.v3i6.2616 DA - 2026-06-11 AU - Muda, Nur AU - Fadilah, M.Faisal AU - Marlina, Enna KW - Software deployment KW - Flexibility (engineering) KW - Robotics KW - Robot KW - Battlefield KW - Key (lock) KW - Situation awareness KW - Drone KW - Component (thermodynamics) VL - 3 IS - 6 SP - 786 EP - 807 AB - Robotic technology has become a critical component in modern military operations due to its ability to enhance operational effectiveness, improve battlefield awareness, and reduce risks to human personnel. The rapid advancement of artificial intelligence, autonomous systems, sensor technologies, and communication networks has significantly expanded the capabilities of military robots across land, air, surface, and underwater domains. This study examines the role of robotic technology in supporting combat operations, focusing on its applications in intelligence, surveillance, reconnaissance (ISR), force protection, logistics support, target acquisition, urban warfare, and electronic warfare. Military robotic systems, including Unmanned Ground Vehicles (UGVs), Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vehicles (USVs), and Unmanned Underwater Vehicles (UUVs), provide enhanced operational flexibility and enable missions in hazardous environments where human deployment would be highly risky. Furthermore, the integration of artificial intelligence enables autonomous navigation, target recognition, sensor fusion, and real-time decision support, thereby improving mission efficiency and accuracy. The study also discusses key advantages of military robotics, including casualty reduction, increased mission endurance, and force multiplication capabilities. However, several challenges remain, such as cybersecurity vulnerabilities, communication disruptions, limited autonomy, energy constraints, and ethical concerns regarding autonomous weapon systems. Future developments are expected to focus on swarm robotics, human-robot teaming, AI-driven autonomy, multi-domain operations, and advanced energy systems. The findings indicate that robotic technology will continue to play an increasingly significant role in shaping future warfare by enhancing military capabilities while minimizing operational risks and human casualties. ER - TY - THES TI - Dronevias: definição de vias de tráfego para aeronaves remotamente pilotadas em ambiente urbano a partir da estimação de risco operacional PY - 2026 PB - Instituto Nacional de Pesquisas Espaciais T2 - Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) DA - 2026-01-01 AU - de Andrade, Rafael LA - pt KW - Measure (data warehouse) KW - Noise (video) KW - Work (physics) KW - Process (computing) ER - TY - JOUR TI - Artificial Intelligence and BIM-Enabled Smart Construction Site Management: A Systematic Review of Site-Level Spatial Decision-Making and Site Layout Optimization-Related Applications for Sustainable Building Delivery PY - 2026 PB - Multidisciplinary Digital Publishing Institute SN - 2624-6511 T2 - Smart Cities DO - 10.3390/smartcities9070112 UR - https://doi.org/10.3390/smartcities9070112 DA - 2026-06-30 AU - Fakhruddin, Zahabiya C1 - Department of Civil Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates AU - Ahmed, Vian C1 - Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates AU - Bahroun, Zied C1 - Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates LA - en KW - Software deployment KW - Field (mathematics) KW - Building information modeling KW - Cloud computing KW - Big data KW - Smart city KW - Thematic map KW - Applications of artificial intelligence VL - 9 IS - 7 SP - 112 EP - 112 AB - Artificial intelligence (AI), building information modeling (BIM), and digital twins are increasingly transforming construction sites into smart, data-driven environments that support safer, more efficient, and more sustainable building and urban infrastructure delivery. However, site-level spatial decision-making related to site layout optimization (SLO) remains constrained by fragmented data environments, limited interoperability, and weak integration between planning, monitoring, and adaptive decision-making. This study presents a systematic literature review of how AI, BIM, and enabling digital technologies are being applied to support smart construction site management, site-level spatial decision-making, and SLO-related applications. A Scopus-based search conducted in October 2025 identified 169 records, of which 63 studies were retained following PRISMA-guided screening. Because explicit SLO studies remain limited, the review synthesizes both directly relevant SLO studies and contextually relevant enabling studies with clear implications for smart and sustainable construction operations. The review combines bibliometric analysis, thematic content analysis, and cross-functional technology mapping to examine the intellectual structure of the field, the main operational domains addressed, and the dominant technological convergences supporting intelligent site decision-making. The findings show that the field is expanding rapidly but remains unevenly consolidated, with greater evidence concentration and practical readiness in real-time digital twin and spatial data management, automated monitoring, and proactive safety intelligence than in closed-loop logistics coordination and autonomous mobility. Across application domains, the dominant technology convergences combine machine learning and deep learning with multidimensional BIM, frequently extended through digital twins, sensors, cloud platforms, UAVs, simulation tools, and GIS-related infrastructures. The review further shows that the main barriers to deployment are not merely algorithmic, but also relate to interoperability, data quality, implementation complexity, human oversight, and limited field validation. Overall, this study provides a structured synthesis of evidence concentration, practical readiness, dominant patterns, and unresolved gaps of AI-BIM-enabled smart construction site management, and outlines directions for more interoperable, human-centered, and field-validated systems that support sustainable smart building and urban infrastructure delivery. ER - TY - JOUR TI - ОПТИМІЗАЦІЯ АНТЕННОЇ ТЕХНІКИ ДЛЯ ПІДВИЩЕННЯ НАДІЙНОСТІ ТА СТАБІЛЬНОСТІ СИГНАЛУ GPS В УСКЛАДНЕНИХ УМОВАХ PY - 2025 SN - 2307-5732 T2 - Herald of Khmelnytskyi National University Technical sciences DO - 10.31891/2307-5732-2025-347-48 UR - https://doi.org/10.31891/2307-5732-2025-347-48 DA - 2025-01-30 AU - ПЛІШ, ВОЛОДИМИР C1 - Державний університет «Інтелектуальних технологій і зв'язку» AU - АНДРУХІВ, ТАРАС C1 - Державний університет «Інтелектуальних технологій і зв'язку» LA - en KW - Global Positioning System KW - Geography KW - Computer science KW - Telecommunications VL - 347 IS - 1 SP - 356 EP - 361 AB - This article explores the critical challenges of maintaining reliable and stable GPS signal reception in complex environments such as urban landscapes, mountainous terrains, and areas with high interference levels. It emphasizes the design, optimization, and implementation of microstrip antennas with circular polarization to enhance the reliability and stability of GPS signals. Addressing issues such as signal reflections from structures and terrain, the study proposes an innovative approach to antenna design that incorporates compact dimensions, efficient signal reception, and advanced polarization techniques. The research integrates theoretical modeling with experimental validation, employing finite element methods to analyze key parameters such as gain, directivity, and standing wave ratios. The experimental tests conducted under variable environmental conditions demonstrate the proposed antenna's capacity to isolate unwanted signals, maintain consistent performance, and significantly enhance signal stability. Special attention is given to the implications of this technology for unmanned aerial vehicles (UAVs) and other mobile platforms, where lightweight and compact antennas are essential for minimizing aerodynamic impact and optimizing performance. By achieving effective circular polarization through innovative feeding mechanisms and phase adjustments, the study demonstrates how the proposed antenna design improves the overall efficiency of GPS-based navigation systems. The article further discusses the comparative analysis of antenna design methodologies and provides insights into the integration of the proposed solutions with modern satellite navigation technologies. It highlights the practical benefits of implementing advanced antenna systems in transport, logistics, and autonomous systems, emphasizing their critical role in enhancing the accuracy and reliability of navigation. Future research directions include expanding the frequency band compatibility of the proposed antenna and its integration with alternative satellite systems to broaden its applicability. The findings of this research contribute to the ongoing development of robust and efficient navigation technologies, particularly for environments with challenging conditions, paving the way for more reliable and versatile GPS applications. ER - TY - JOUR TI - Customisation and Payload Integration of Hexacopter for Enhanced Grocery Delivery PY - 2026 PB - RELX Group (Netherlands) SN - 1556-5068 T2 - SSRN Electronic Journal DO - 10.2139/ssrn.7273801 UR - https://doi.org/10.2139/ssrn.7273801 DA - 2026-01-01 AU - Ntantis, Efstratios C1 - Amity University Dubai, Dubai International Academic City, P.O. Box: 345019, Dubai, Dubai 345019, United Arab Emirates AU - Arora, Sifti C1 - Amity University Dubai KW - Drone KW - Payload (computing) KW - Android (operating system) KW - Process (computing) KW - Studio KW - Hook ER - TY - JOUR TI - Futuristic Technologies for Intelligent Manufacturing and Supply Chain Management PY - 2021 PB - Institution of Engineering and Technology SN - 2516-8398 T2 - IET Collaborative Intelligent Manufacturing DO - 10.1049/cim2.12039 UR - https://doi.org/10.1049/cim2.12039 DA - 2021-09-01 AU - Singh, Pradeep C1 - Jaypee University of Information Technology Waknaghat India C1 - Jaypee University of Information Technology, Waknaghat, India AU - Raut, Rakesh C1 - National Institute of Industrial Engineering Mumbai India C1 - National Institute of Industrial Engineering, Mumbai, India AU - Hong, Wei‐Chiang C1 - Jiangsu Normal University Xuzhou China C1 - Jiangsu Normal University, Xuzhou, China AU - Govindarajan, Usharani C1 - Shanghai Jiao Tong University Shanghai China C1 - Shanghai Jiao Tong University, Shanghai, China LA - en KW - Supply chain KW - Supply chain management KW - Computer science KW - Quality (philosophy) KW - Manufacturing KW - Process (computing) KW - Analytics KW - Manufacturing engineering KW - Industry 4.0 KW - Process management KW - Engineering management KW - Engineering KW - Data science KW - Business KW - Marketing VL - 3 IS - 3 SP - 203 EP - 204 AB - Intelligent manufacturing combines the perspective of people, processes, and machines to impact the overall economics of manufacturing. Futuristic technologies such as Internet of Things, blockchain, virtual reality, edge computing, etc. combined with manufacturing principles form a platform that is leading towards many innovations across industries. Artificial intelligence (AI) and machine learning now play a leading role in enhancing the quality of the manufacturing process. From significant cuts in unplanned downtime to better designed products, manufacturers are applying AI powered analytics on data to improve efficiency, product quality and the safety of employees. Furthermore, manufacturers have benefitted from data-driven innovations for demand planning and logistics management (first and last part) of their supply chains. Tracking production across entire processes and managing the supply chain as an integrated platform is now an urgent need. Hence, there is a demand to further explore the futuristic technologies for intelligent manufacturing and supply chain management. The objective of this Special Issue is to collect papers on the latest trends in industry specific to intelligent manufacturing and supply chain management. This Special Issue has discussions on novel, scientific technological insights, principles, algorithms, and experiences in intelligent manufacturing and supply chain management. After a rigorous round of double-blind peer review process, finally eight papers are accepted for publication in this Special Issue. The first paper is 'Research on dispersion compensation using avalanche photodiode and pin photodiode' by Ma et al. Based on the experimental analysis and the comparison results, the performance of the avalanche photodiode is around 11% better than the pin diode. The second paper is 'Prediction of energy consumption of numerical control machine tools and analysis of key energy-saving technologies' by Qiang et al. In this paper, the energy consumption of numerical control machine tools is analysed, and the relevant energy-saving model is established. The third paper is 'Design and implementation of construction prediction and management platform based on building information modelling and three-dimensional simulation technology in Industry 4.0' by Sun et al. In this paper, the virtual simulation technology is applied to solve problems of building design and damage assessment. The influence of this technology on the overall design of the building is discussed, and further, the future developments for industrial automation are also covered. The fourth research work is 'Analysis of a building collaborative platform for Industry 4.0 based on Building Information Modelling technology' by Ding & Kohli. This paper emphasises on the higher degree of data sharing and strengthen the coordination of the work of various agencies in construction engineering. The fifth paper is 'Knowledge map visualization of technology hotspots and development trends in China's textile manufacturing industry' by Huang et al. The authors in this paper suggest the developmental directions of textile manufacturing from traditional to intelligent trends. Furthermore, this paper also provides a reference for the later trends and the dynamic planning of China's textile manufacturing industry technology. The sixth paper is 'A flight control method for unmanned aerial vehicles based on vibration suppression' by Wang et al. In this paper, unmanned aerial vehicles (UAVs) based on vibration suppression are discussed. The system performance is observed in terms of rise time and overshoot and it is observed from the simulation outcomes that the system is stable at all design points in the range of 0–9000 m, providing a rise time of 10 s and zero overshoot. From this study, it is revealed that the designed controller can effectively suppress the vibration of the flexible mode while ensuring the stability and tracking performance of the UAV. The seventh paper is 'A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India' by Gabani et al. This research covers the viability suggesting that the drone technology can solve the current operational challenges in India's urban cities. Furthermore, vital factors are discussed to enable stakeholders to select a model for drone logistics systems planning and experimentations with current challenges. The eighth research work is 'A novel method of material demand forecasting for power supply chains in industrial applications' by Xiao et al. In this paper, two kinds of collaborative transmission process models of supply chain information are established, and simulation analysis is carried out on the two models by the Monte Carlo method to verify the effect of collaborative transmission of information flow in supply chains within big data environments. The ninth paper is 'Progress of zinc oxide-based nanocomposites in the textile industry' by Huang et al. The proposed work identified that the textiles industry can achieve better piezoelectric properties using zinc oxide-based nanocomposites in various sections of the textile industry and it also identified that the slurry type suspensions are also limited. Finally, the Special Issue discusses 'Supply chain control towers: Technology push or market pull—An assessment tool' by Patsavellas et al. The paper examines the market-pull versus technology-push components of the functionalities enabled by digital supply chain control towers and, building on the outcome of an extensive survey and expert interviews, proposes an assessment tool to aid decision making for the consideration of their adoption. ER - TY - JOUR TI - Technological Architecture of Aerial Mobility Innovation: A Patent‐Based Network, Text‐Mining, and Multicriteria Evaluation Approach PY - 2026 PB - Hindawi Publishing Corporation SN - 0197-6729 T2 - Journal of Advanced Transportation DO - 10.1155/atr/1415764 UR - https://doi.org/10.1155/atr/1415764 DA - 2026-01-01 AU - Sabet, M. AU - Aazami, Adel AU - Kummer, Sebastian AU - Mohammadi, Navid LA - en KW - Modular design KW - Thematic map KW - Component (thermodynamics) KW - Architecture KW - Cluster analysis KW - Prioritization KW - Citation KW - Network architecture VL - 2026 IS - 1 AB - This study presents an integrated assessment of technological development in the aerial mobility innovation ecosystem, combining network analysis, patent clustering, and a multicriteria strategic evaluation model. The empirical corpus centers on unmanned aerial vehicle (UAV) logistics, unmanned aircraft system (UAS) applications, urban air mobility (UAM) platforms, and the avionics, sensing, communication, and materials technologies that enable supply‐chain‐oriented aerial operations, rather than commercial passenger air transport. Patent data sourced from Lens.org were analyzed using Gephi, revealing a highly modular applicant collaboration network shaped by five dominant communities centered around Huawei, Apple, Qualcomm, NVIDIA, and UATC. These applicant communities form structurally cohesive ecosystems that anchor major technological directions within the field. To uncover the thematic organization of innovation, text clustering of patent abstracts identified five coherent technological clusters: data and sensing layer, platform design and manufacturing, network infrastructure and communication, agrotech applications, and materials and component innovation. Together, these clusters represent the core thematic architecture of aerial mobility, capturing the interplay between perception technologies, engineering design, communication infrastructures, domain‐specific applications, and advancements in materials and components. A subsequent TOPSIS‐based strategic evaluation reveals that the data and sensing layer, materials and component innovation, and network infrastructure and communication clusters possess the highest strategic potential based on their citation influence, cross‐domain relevance, and integration across applicant communities. Overall, the findings show that aerial mobility innovation emerges through the interaction of five structurally dominant applicant collaboration communities and five thematically distinct technological clusters, offering a comprehensive understanding of how technological trajectories evolve. The proposed integrated framework offers a replicable patent‐based prioritization signal that can inform preliminary research scanning, monitoring, and agenda‐setting in this rapidly progressing technological domain, without substituting for market, regulatory, or operational assessment. ER - TY - JOUR TI - Intellectualization of Kalman Filters to Increase the Autonomy and Accuracy of Navigation Systems of Unmanned Aerial Vehicles PY - 2026 SN - 2541-9129 T2 - Safety of technogenic and natural systems DO - 10.23947/2541-9129-2026-10-2-152-165 UR - https://doi.org/10.23947/2541-9129-2026-10-2-152-165 DA - 2026-06-06 AU - Контарев, И. C1 - Rostov State Medical University AU - Belik, V. C1 - Don State Technical University LA - ru KW - GNSS applications KW - Kalman filter KW - Inertial navigation system KW - Reliability (semiconductor) KW - Global Positioning System KW - SIGNAL (programming language) KW - Satellite system KW - Air navigation KW - Multipath propagation VL - 10 IS - 2 SP - 152 EP - 165 AB - Introduction. Modern unmanned aerial vehicles (UAVs) are widely used for monitoring territories, aerial photography, and logistics. Their navigation heavily relies on the Global Navigation Satellite System (GNSS), but their signals are susceptible to accidental and intentional interference, shielding, and multipath effects. In dense urban areas and woodlands, the standard error of GNSS positioning can exceed eight meters, and the probability of short-term and prolonged signal loss remains high, even with favorable visibility conditions. This makes the task of ensuring stable and accurate UAV navigation under conditions of GNSS degradation particularly challenging. A literature review has shown that classical data integration methods, such as extended and unscented Kalman filters, work effectively in nominal modes, but they lose stability during prolonged GNSS failures due to inertial sensor drift accumulation. New architectures based on deep learning (e.g. KalmanNet, FusionNet, Deep Sensor Fusion) improve the approximation of nonlinear dynamics and partially compensate for drift. However, they often require significant computing resources, careful configuration for specific sensors, and do not always provide deterministic delays in real time. Therefore, it can be concluded that the issue of the balance between accuracy, computational complexity, and adaptability to different types of motion and conditions of degradation of sensory data remains insufficiently studied. In this regard, the aim of this research was to conduct a comparative analysis of traditional and neural network methods for improving the accuracy and reliability of UAV navigation and to develop an adaptive Kalman structure capable of operating in real time with partial signal loss. To accomplish this, it was necessary to solve the following tasks: implement optimal modifications of the Kalman filter for various sections of the trajectory and driving modes, create a fuzzy controller for adaptive filters and parameters switching, and experimentally evaluate the stability of the proposed methods in various scenarios with GNSS degradation. Materials and Methods . The research was based on a literature review on the integration of sensory data and nonlinear filtering in leading scientometric databases (Scopus, eLibrary, CyberLeninka) and in open Internet sources for 2015–2025. Mathematical modeling was conducted in the MATLAB environment. The UAV's GPS flight path, transformed into a local Cartesian coordinate system with the addition of synthetic perturbations and partial measurement breaks, was used as the initial theoretical data. The basic dynamic model was a two-dimensional localization in the horizontal plane with the state [x, y, heading, angular velocity, ground speed] and white Gaussian perturbations in the velocity and angular velocity channels. The Euler method was used for discretization. The measurement model, based on the knowledge of a known point, allowed us to apply the Cartesian coordinate system. EKF, UKF/SRCDKF and Particle Filter were implemented and compared as reference algorithms for nonlinear filtering. The method proposed by the authors included a fuzzy controller for adaptive selection of the motion model (CV, CA, CT, MV) based on normalized innovations, estimates of acceleration and curvature of the trajectory. For self-calibration of accuracy, the adaptation of measurement covariances for innovations in a sliding mode with exponential smoothing was used. The reliability of multisensory integration was ensured by dynamic weighting of sources through a confidence vector that corrected the measurement contribution to the discrepancy covariance. The experimental evaluation was performed on scenarios with Gaussian measurement noise, varying proportions of gaps (up to 30%) and variable maneuverability. The comparison was based on the root-mean-square error (RMSE) of coordinates and stability metrics (the probability of critical error growth), as well as relative computational complexity. Results. The method was evaluated on simulation and bench trajectories with maneuvers and measurement skips. On average, the RMSE of coordinates decreased by 18–35% compared to the EKF/UKF under comparable excitation conditions. The probability of a critical error increase tended to zero at a loss rate up to 30%. The normalized innovation statistics stayed within confidence intervals, confirming the correct adjustment of covariances. Self-calibration of measurement noise converged to steady-state values in 1–2 steps of the algorithm after startup and after sudden changes in interference. Ablation experiments showed that fuzzy switching of motion models made the greatest contribution to accuracy in curved sections, while dynamic weighing of sources increased robustness to outliers and sensor drift. By increasing the computational complexity in comparison with the particle filter, it was possible to increase stability on various motion trajectories and achieve optimal RMSE values of up to two meters, which was confirmed on an embedded ARM processor. Discussion. The gain in accuracy and stability was due to a combination of a locally adequate kinematic model and online adaptation of sensor confidence, which reduced systematic biases and prevented covariance overclocking. Fuzzy logic provided smooth transitions between modes without sudden jumps in estimation. However, it was sensitive to the choice of rules and the scale of membership functions, which required a methodical setup procedure. Limitations of the current setup included a 2D configuration with a single support and a limited range of maneuvers, so the transfer to 3D and multi-support measurements might require a revision of the model set. Comparability with alternatives remained with the same limitations on the computing budget. With unlimited resources, heavier methods partially reduced the gap. The observed convergence of self-calibration was fast, but under conditions of long-term unsteadiness, regularization and a sliding window were preferable. Conclusion. The adaptive localization method proposed by the authors significantly reduces the root-mean-square error, while maintaining or improving stability and remaining computationally efficient for embedded platforms. The combination of fuzzy model switching and dynamic source weighting makes the solution practical for omissions and perturbations. Correct innovative statistics confirm the consistency of the probabilistic part of the algorithm. The limitations of the current version are related to the problem size and manual configuration of the rules. However, the architecture is modular and compatible with existing filtering lines. Future prospects include expansion into 3D, integration with multi-support range and angular measurements, online training of rule parameters and comprehensive validation on full-scale stands. Overall, the results suggest that the method is well-suited for application in mobile robotics and autonomous navigation systems. ER - TY - JOUR TI - Pathways for the Coordinated Development of Landscape Architecture and the Low-Altitude Economy PY - 2026 PB - Science Press SN - 1673-1530 T2 - Landscape Architecture DO - 10.3724/j.fjyl.la20250491 UR - https://doi.org/10.3724/j.fjyl.la20250491 DA - 2026-01-01 AU - WU, Yan AU - WU, Suke AU - ZHAO, Zhiqiang LA - en KW - Landscape design KW - Landscape architecture KW - Service (business) KW - Landscape assessment KW - Architecture KW - Circular economy KW - Adaptation (eye) KW - Space (punctuation) VL - 33 IS - 1 SP - 93 EP - 99 AB - Objective The low-altitude economy covers low-altitude manufacturing, flight operations, support services and comprehensive service industries, and has the characteristics of spatially three-dimensional, regional dependence, digital ecology, industrial integration and radiation driving. With the continuous development of digitization and informatization, these characteristics increasingly affect the interaction between low-altitude activities and urban and natural environments, which provides new ideas for the development of landscape architecture, and the coordinated development of the two is an important issue in the transformation of landscape architecture. Methods The knowledge map method was used to analyze the research status of environmental low-altitude economy at home and abroad. By combing and reading relevant literature at home and abroad, the research trends of low-altitude economy in logistics, aviation, ecological monitoring and other fields were systematically analyzed by reviewing literature research, case studies and comparative analysis. Results By analyzing the domestic and foreign research, the internal mechanism of the cross research between landscape architecture and low-altitude economy is revealed four aspects. In the aspect of technology application, low-altitude aircraft tend to be transformed from perception tools to "design intelligence" which can guide ecological design and space construction. In the aspect of spatial planning, landscape architecture will realize the spatial transformation from plane extension to three-dimensional reconstruction under the development of low-altitude economy. In the aspect of ecological impact, the research focus has changed from identifying environmental risks by using low-altitude facilities to systematic assessment and control of ecology. In the aspect of humanistic experience, the combination of low-altitude economy and cultural narrative has further stimulated the vitality of landscape architecture discipline. It is found that under the influence of low-altitude economy, the development of landscape architecture faces some new problems, such as imperfect policies and regulations, insufficient adaptability of spatial planning system, systematic lack of ecological protection, technical bottleneck restriction, homogenization dilemma of cultural and tourism integration, including lack of unified standards for low-altitude facility design, traditional two-dimensional planning being difficult to meet the needs of air-space coordination, ecological destruction caused by noise and habitat disturbance, lack of ecological protection system, etc. There are technical bottlenecks in data processing and flight stability; homogenization of tourism products, focusing on tourism over cultural innovation. In order to promote the coordinated development of the two, this paper puts forward the implementation path of the integration of low-altitude economy and landscape architecture: to ensure the adaptation of policies and regulations, to improve policies and regulations and technical standards, to formulate low-altitude greening design and ecological evaluation standards, and to lay a foundation for the integration of landscape architecture and low-altitude economy; Conduct spatial value evaluation, clarify the use, development subject and income distribution mechanism of each low-altitude area, promote the market-oriented operation of public resources, and use unmanned aerial vehicles to carry out ecological background analysis and evaluate the ecological carrying capacity of low-altitude activities; Conduct functional compound planning, make landscape architecture break through the limitation of traditional ground perspective, bring low-altitude airspace into the vertical space system of landscape architecture, add low-altitude related infrastructure in the garden, and realize efficient coordinated utilization of airspace and ground resources; In ecological service monitoring, we should increase the investment in R&D of UAV technology, low-altitude aircraft noise reduction technology, flight safety guarantee technology, etc., improve the intelligent level of landscape architecture monitoring and management, and establish a new ecological assessment mode; Promote the integrated development of culture and tourism, break through the homogenization dilemma from three dimensions of cultural empowerment, spatial differentiation and experience depth, and integrate garden cultural elements into low-altitude experience links, to improve the overall operation effect. Conclusion Based on the research, we have drawn conclusions in three aspects. 1) The global low-altitude economy industry will become the next development hotspot, and it should accelerate the integration with transportation logistics, cultural tourism and other formats, expand more application scenarios, promote the integration of landscape architecture and related industries, and activate the new vitality of landscape architecture. 2) Green space and parks in cities will become important carriers for low-altitude transportation in the future, and planning and design of low-altitude composite public space will be the focus of research, and research will move from two-dimensional garden aesthetic space to three-dimensional traffic spatial pattern. 3) The development of unmanned aerial vehicle and related technologies provide refined intelligent solutions for intelligent garden management, and low-altitude monitoring data provide new tools for ecological value assessments of gardens, significantly improve ecological monitoring and management levels. ER - TY - THES TI - Contributions to Drone Fleet Mission Planning and Monitoring PY - 2020 DO - 10.20868/upm.thesis.65852 UR - https://doi.org/10.20868/upm.thesis.65852 DA - 2020-01-01 AU - Ramos, Iván LA - en KW - Drone KW - Aeronautics KW - Citation KW - Engineering KW - Aerospace engineering KW - Computer science KW - Library science KW - Biology AB - Los servicios basados en los vehículos aéreos no tripulados están todavía en sus primeras fases. Pero los escenarios futuros prevén espacios aéreos urbanos y no urbanos compartidos por múltiples vehículos aéreos no tripulados con misiones muy diferentes como pueden ser la prestación de servicios logísticos, la respuesta a emergencias o el transporte, entre otros muchos. En estos escenarios aparece la figura del proveedor de servicio, que puede ser un autoridad pública o una empresa, quien tendrá en propiedad y operará una flota de vehículos aéreos teledirigidos y que en ocasiones podrá pagar a operadores de drones externos para que realicen ciertos vuelos específicos. La aparición de una herramienta para definir con facilidad la misión, calcular la trayectoria óptima y vigilar la ejecución de la misión en tiempo real beneficiará, en todas las situaciones posibles, al usuario que necesite un servicio gestionando una flota de drones. Esta tesis propone un sistema que puede contribuir a la gestión y control de flotas de drones constituido por una arquitectura basada en microservicios que permite la planificación de misiones y la vigilancia de los drones durante su ejecución. La arquitectura está formada por una serie de microservicios que contienen la principal lógica del sistema: el Servicio de Planificación de Misión, el Servicio de Planificación de Vuelo, el Servicio de Predicción de Trayectoria y el Servicio de Seguimiento y Monitorización. Primero, esta tesis describe los requisitos necesarios que debe cumplir una herramienta de planificación de misiones para la realización de operaciones urbanas y no urbanas. Esta herramienta es capaz de diseñar planes de vuelo teniendo en consideración las restricciones de vuelo y las limitaciones aeroespaciales. El Servicio de Planificación de Misión permite la definición de diferentes tipos de misiones que pueden ir desde las inspecciones hasta las entregas. Estos servicios de planificación y predicción están diseñados para trabajar coordinadamente con otros servicios externos a cargo de la distribución de recursos aeroespaciales de muy bajo nivel como pueden ser las geocercas y las aerovías que presenta una determinada zona. Con esta información, el Servicio de Planificación de Vuelo es capaz de proporcionar un plan de vuelo que cumpla con todas las restricciones vigentes. Por otro lado, el Servicio de Predicción de Trayectoria utiliza un lenguaje formal para describir las instrucciones de una aeronave de tipo quadrotor (QR-AIDL) cuyo objetivo es predecir de forma precisa las instrucciones de guía que modelan las trayectorias de este tipo de aeronaves de forma unívoca permitiendo la obtención eficiente de una trayectoria común. Gracias a este servicio se obtiene la trayectoria que va a seguir un dron para ejecutar una misión. Al tratarse de una herramienta para gestionar una flota de drones, estos servicios de planificación deben proporcionar planes de vuelo que cualquier dron pueda interpretar. Por esta motivo, la herramienta presenta un traductor de planes de vuelo a lenguaje MAVLink para que puedan realizarse las misiones planificadas por cualquier tipo de dron presente en la flota. La función de vigilancia se encarga de estimar la posición y cinemática de los drones a partir de las medidas de los sensores disponibles y el análisis de su comportamiento. Para ello, lo primero que debe hacer este servicio es el filtrado de las medidas que llegan al sistema mediante una fusión de datos provenientes de diferentes tipos de sensores (cooperativos y no cooperativos), así como de la telemetría que envían los drones. Se ha propuesto que el Servicio de Seguimiento y Monitorización esté formado por un sistema que utiliza dos filtros en paralelo (basado en mapa, robusto) con una lógica de selección para obtener la estimación de la posición de los blancos con una mayor precisión. Por otro lado, la función de monitorización se encarga de detectar los posibles conflictos que pueda haber en el espacio aéreo que ocupa la misión ejecutada por el dron captado. Esta funcionalidad emplea las posiciones filtradas de los blancos para comprobar el comportamiento del dron frente a una serie de restricciones y limitaciones espacio-temporales. Esta tesis presenta contribuciones en todos y cada uno de los servicios de los que se compone el sistema que puede contribuir a la gestión y control de flotas de drones. Por último, se presentan resultados que demuestran el correcto funcionamiento de cada uno de los servicios de los que se compone la herramienta, así como su actuación en conjunto. Finalmente se presentan las principales conclusiones de esta tesis, así como las posibles futuras líneas de investigación. ----------ABSTRACT---------- UAV-based services are still in their early stages. While future scenarios foresee urban and non-urban air spaces shared by multiple UAVs with very different missions such as logistics, emergency response or transport, among many others. The figure of the service provider, which may be a public authority or a company, appears in these scenarios. The service provider will own and operate a fleet of drones and may sometimes pay external drone operators to carry out certain specific flights. The appearance of a tool to easily define the mission, calculate the optimal trajectory and monitor the execution of the mission in real time will benefit the user who needs a service managing a fleet of drones. This thesis proposes a system, that can contribute to the management and control of drone fleets, constituted by an architecture based on microservices that allows the planning of missions and the monitoring of drones during their execution. The architecture presented is composed by a series of microservices that contain the main logic of the system: the Mission Planning Service, the Flight Planning Service, the Trajectory Prediction Service and the Tracking and Monitoring Service. First, this thesis describes the necessary requirements that a mission planning tool must meet in order to carry out urban and non-urban operations. This tool is capable of designing flight plans taking into consideration flight restrictions and aerospace limitations. The Mission Planning Service allows the definition of different types of missions that can range from inspections to deliveries. These planning and prediction services are designed to work in coordination with other external services in charge of the distribution of very low level aerospace resources such as geofencing and airways in a given area. With this information, the Flight Planning Service is able to provide a flight plan that meets all current restrictions. On the other hand, the Trajectory Prediction Service uses a formal language to describe the instructions of a quadrotor type aircraft (QR-AIDL) whose objective is to accurately predict the guidance instructions that model the trajectories of this type of aircraft in a univocal way allowing the efficient obtaining of a common trajectory. Thanks to this service, the trajectory that a drone will follow to execute a mission is obtained. As a tool for managing a drone fleet, these planning services must provide flight plans that any drone can interpret. For this reason, the tool features a flight plan translator into MAVLink language so that planned missions can be performed by any type of drone present in the fleet. The monitoring function is responsible for estimating the precise position and kinematics of the drones from the measurements of the available sensors and the analysis of its behaviour. To do this, the first thing this service must do is filter the measurements that arrive at the system by merging data from different types of sensors (cooperative and non-cooperative), as well as the telemetry sent by the drones. It has been proposed that the Tracking and Monitoring Service should consist of a system that uses two filters in parallel (map-based, robust) with a selection logic to obtain the estimate of the position of the targets with greater precision. On the other hand, the monitoring function is in charge of detecting possible conflicts in the airspace occupied by the mission executed by the tracked drone. This functionality uses the filtered positions of the targets to check the behaviour of the drone against a number of space and time constraints. This thesis presents contributions in each one of the services that make up the system that can contribute to the management and control of drone fleets. Finally, results which demonstrate the correct performance of each of the services of which the tool is composed are presented, as well as their performance as a whole. Finally, the main conclusions of this thesis are presented, as well as possible future lines of research. ER - TY - GEN TI - Application of Crystal Oscillator in Positioning PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.22135677 UR - https://doi.org/10.5281/zenodo.22135677 DA - 2026-08-28 AU - , XtalTQ KW - Crystal oscillator KW - SIGNAL (programming language) KW - Synchronization (alternating current) KW - Crystal oven KW - Satellite navigation KW - Positioning system KW - Crystal (programming language) KW - Vibration KW - Frequency drift KW - Precise Point Positioning AB - Application of Crystal Oscillators in Positioning Systems: Precision Timing for Accurate Navigation Application of Crystal Oscillators in Positioning and Navigation Systems In modern positioning technology, accuracy begins with time. Whether a device uses GPS, GNSS, satellite navigation, asset tracking, autonomous driving, drone navigation, or industrial location systems, it depends on precise timing to calculate position. At the center of this timing architecture is a small but critical component: the crystal oscillator. A crystal oscillator for positioning provides the stable frequency reference required to measure time intervals, synchronize signals, and support reliable navigation performance. Without a stable oscillator, a positioning receiver may experience signal errors, poor synchronization, longer acquisition time, or inaccurate location results. As location-based technologies become more advanced, crystal oscillators continue to play a vital role in improving navigation accuracy, signal reliability, and system efficiency. What Is a Crystal Oscillator? A crystal oscillator is an electronic circuit that uses the mechanical resonance of a vibrating crystal, usually quartz, to generate a highly stable electrical signal. Quartz has piezoelectric properties, which means it vibrates at a precise frequency when voltage is applied. This stable vibration becomes the foundation for accurate timing in electronic systems. Crystal oscillators are widely used because they provide: Precise frequency output Excellent timing stability Low power consumption Long-term reliability Compact size Strong performance across many applications In positioning systems, the oscillator acts as the local timing reference. It helps the receiver measure time accurately, process satellite signals, and maintain synchronization with navigation data. Why Precise Timing Matters in Positioning Systems Positioning systems calculate location by measuring time. In satellite-based navigation, such as GPS, GLONASS, Galileo, BeiDou, and other GNSS systems, satellites transmit signals that include timing and orbital information. A receiver on the ground compares the time the signal was transmitted with the time it was received. Because radio signals travel at the speed of light, even a tiny timing error can create a large positioning error. For example, a timing error of just one nanosecond can translate to about 30 centimeters of distance error. Larger timing instability can quickly reduce positioning accuracy. This is why a stable crystal oscillator is essential. It allows the receiver to maintain accurate time measurement and calculate distance from satellites with greater precision. Crystal Oscillator in GPS and GNSS Receivers A GPS receiver must detect weak satellite signals, decode navigation data, and calculate position based on signal timing. The crystal oscillator provides the clock reference that supports these functions. In a typical GNSS receiver, the oscillator helps with: Signal acquisition Carrier tracking Code synchronization Time measurement Frequency reference generation Data demodulation Position calculation When the oscillator maintains a stable frequency, the receiver can lock onto satellite signals faster and track them more reliably. If the oscillator drifts too much, the receiver may struggle to maintain signal lock, especially in weak-signal environments such as urban canyons, forests, tunnels, or indoor-adjacent locations. Precision Timekeeping for Navigation Accuracy One of the most important roles of a crystal oscillator in positioning is precision timekeeping. Satellite navigation depends on accurate measurement of signal travel time. Each satellite broadcasts a time-stamped signal. The receiver calculates how long the signal took to arrive, then converts that time into distance. This distance is called a pseudorange. By collecting pseudorange measurements from multiple satellites, the receiver can calculate its position through trilateration. For this process to work correctly, the receiver must maintain a stable local clock. A crystal oscillator provides this clock. The more stable the oscillator, the more accurately the system can estimate timing differences and improve positioning performance. Synchronization in Positioning Systems Synchronization is another critical function of crystal oscillators in navigation systems. A positioning receiver must align its internal timing with satellite signals and system processing requirements. If timing alignment fails, the receiver may misinterpret signals or produce inaccurate location data. Crystal oscillators support synchronization by keeping internal circuits operating at a consistent reference frequency. This helps ensure that the receiver, signal processor, radio front end, and digital logic all work together correctly. In advanced navigation equipment, synchronization also supports communication between multiple systems. For example, autonomous vehicles may combine GNSS data with inertial sensors, radar, cameras, and vehicle control systems. Stable timing allows these subsystems to share data accurately and operate in harmony. Frequency Stability and Positioning Performance Frequency stability describes how well an oscillator maintains its output frequency under changing conditions. In positioning systems, high frequency stability improves receiver performance by reducing timing drift and frequency offset. Several factors can affect oscillator stability: Temperature changes Supply voltage variation Aging Mechanical vibration Shock Electromagnetic interference PCB layout quality For outdoor positioning devices, temperature stability is especially important. A vehicle tracker, drone, marine navigation unit, or survey device may operate in hot, cold, or rapidly changing environments. If the oscillator frequency shifts with temperature, the receiver may experience reduced accuracy or slower satellite tracking. This is why many high-performance positioning applications use temperature-compensated crystal oscillators, also known as TCXOs. TCXO for GPS and GNSS Positioning A TCXO, or Temperature-Compensated Crystal Oscillator, is a crystal oscillator designed to maintain stable frequency across a wide temperature range. It includes compensation circuitry that corrects frequency changes caused by temperature variation. TCXOs are widely used in GPS and GNSS modules because they offer better stability than standard crystal oscillators while maintaining compact size and low power consumption. Benefits of TCXOs in positioning systems include: Faster satellite acquisition Better signal tracking Improved frequency accuracy Reliable operation across temperature changes Reduced positioning errors Enhanced performance in mobile devices For applications such as fleet tracking, precision agriculture, UAV navigation, timing modules, and high-performance GNSS receivers, TCXOs can significantly improve system reliability. Crystal Oscillators in Asset Tracking and IoT Positioning Location tracking has become a core feature in many IoT applications. Smart logistics, wearable devices, pet trackers, shipping containers, shared mobility platforms, and industrial asset monitoring systems all use positioning technology to report location. These devices often require compact, low-power crystal oscillators. Battery life is a major concern, so the oscillator must provide accurate timing while consuming minimal energy. A crystal oscillator for IoT positioning supports: GNSS wake-up timing Wireless communication synchronization Low-power standby operation Location data time-stamping Reliable tracking updates Efficient battery management In these applications, designers must balance timing accuracy, power consumption, package size, and cost. A carefully selected crystal oscillator can help extend battery life while maintaining dependable location performance. Crystal Oscillators in Automotive Positioning Modern vehicles increasingly depend on accurate positioning. Navigation systems, advanced driver assistance systems, vehicle-to-everything communication, telematics, fleet management, and autonomous driving all require reliable timing. Crystal oscillators support automotive positioning by providing stable frequency references for GNSS receivers, communication modules, sensors, and electronic control units. In autonomous and semi-autonomous vehicles, precise timing is even more important because the system must combine data from multiple sensors in real time. Automotive positioning systems must also withstand temperature extremes, vibration, shock, and electrical noise. High-quality crystal oscillators designed for automotive environments help maintain stable navigation performance under demanding conditions. Crystal Oscillators in Drones and UAV Navigation Drones and unmanned aerial vehicles rely on positioning systems for flight control, route planning, altitude management, mapping, and autonomous operation. A crystal oscillator helps the onboard GNSS receiver maintain timing accuracy while the drone moves through changing environments. Stable oscillator performance supports: Accurate flight positioning Reliable waypoint navigation Better return-to-home functionality Stable communication links Precise sensor data timing Improved mapping and surveying accuracy For professional UAV applications, such as aerial inspection, agriculture, defense, and geospatial mapping, timing stability directly affects mission quality and operational safety. Positioning Beyond GPS: Multi-Sensor Synchronization Many advanced positioning systems do not rely on GNSS alone. They combine multiple technologies, including: Inertial measurement units Accelerometers Gyroscopes Radar LiDAR Cameras Cellular positioning Wi-Fi p ER - TY - GEN TI - Application of Crystal Oscillator in Positioning PY - 2026 PB - European Organization for Nuclear Research T2 - Zenodo (CERN European Organization for Nuclear Research) DO - 10.5281/zenodo.22135676 UR - https://doi.org/10.5281/zenodo.22135676 DA - 2026-08-28 AU - , XtalTQ KW - Crystal oscillator KW - SIGNAL (programming language) KW - Synchronization (alternating current) KW - Crystal oven KW - Satellite navigation KW - Positioning system KW - Crystal (programming language) KW - Vibration KW - Frequency drift KW - Precise Point Positioning AB - Application of Crystal Oscillators in Positioning Systems: Precision Timing for Accurate Navigation Application of Crystal Oscillators in Positioning and Navigation Systems In modern positioning technology, accuracy begins with time. Whether a device uses GPS, GNSS, satellite navigation, asset tracking, autonomous driving, drone navigation, or industrial location systems, it depends on precise timing to calculate position. At the center of this timing architecture is a small but critical component: the crystal oscillator. A crystal oscillator for positioning provides the stable frequency reference required to measure time intervals, synchronize signals, and support reliable navigation performance. Without a stable oscillator, a positioning receiver may experience signal errors, poor synchronization, longer acquisition time, or inaccurate location results. As location-based technologies become more advanced, crystal oscillators continue to play a vital role in improving navigation accuracy, signal reliability, and system efficiency. What Is a Crystal Oscillator? A crystal oscillator is an electronic circuit that uses the mechanical resonance of a vibrating crystal, usually quartz, to generate a highly stable electrical signal. Quartz has piezoelectric properties, which means it vibrates at a precise frequency when voltage is applied. This stable vibration becomes the foundation for accurate timing in electronic systems. Crystal oscillators are widely used because they provide: Precise frequency output Excellent timing stability Low power consumption Long-term reliability Compact size Strong performance across many applications In positioning systems, the oscillator acts as the local timing reference. It helps the receiver measure time accurately, process satellite signals, and maintain synchronization with navigation data. Why Precise Timing Matters in Positioning Systems Positioning systems calculate location by measuring time. In satellite-based navigation, such as GPS, GLONASS, Galileo, BeiDou, and other GNSS systems, satellites transmit signals that include timing and orbital information. A receiver on the ground compares the time the signal was transmitted with the time it was received. Because radio signals travel at the speed of light, even a tiny timing error can create a large positioning error. For example, a timing error of just one nanosecond can translate to about 30 centimeters of distance error. Larger timing instability can quickly reduce positioning accuracy. This is why a stable crystal oscillator is essential. It allows the receiver to maintain accurate time measurement and calculate distance from satellites with greater precision. Crystal Oscillator in GPS and GNSS Receivers A GPS receiver must detect weak satellite signals, decode navigation data, and calculate position based on signal timing. The crystal oscillator provides the clock reference that supports these functions. In a typical GNSS receiver, the oscillator helps with: Signal acquisition Carrier tracking Code synchronization Time measurement Frequency reference generation Data demodulation Position calculation When the oscillator maintains a stable frequency, the receiver can lock onto satellite signals faster and track them more reliably. If the oscillator drifts too much, the receiver may struggle to maintain signal lock, especially in weak-signal environments such as urban canyons, forests, tunnels, or indoor-adjacent locations. Precision Timekeeping for Navigation Accuracy One of the most important roles of a crystal oscillator in positioning is precision timekeeping. Satellite navigation depends on accurate measurement of signal travel time. Each satellite broadcasts a time-stamped signal. The receiver calculates how long the signal took to arrive, then converts that time into distance. This distance is called a pseudorange. By collecting pseudorange measurements from multiple satellites, the receiver can calculate its position through trilateration. For this process to work correctly, the receiver must maintain a stable local clock. A crystal oscillator provides this clock. The more stable the oscillator, the more accurately the system can estimate timing differences and improve positioning performance. Synchronization in Positioning Systems Synchronization is another critical function of crystal oscillators in navigation systems. A positioning receiver must align its internal timing with satellite signals and system processing requirements. If timing alignment fails, the receiver may misinterpret signals or produce inaccurate location data. Crystal oscillators support synchronization by keeping internal circuits operating at a consistent reference frequency. This helps ensure that the receiver, signal processor, radio front end, and digital logic all work together correctly. In advanced navigation equipment, synchronization also supports communication between multiple systems. For example, autonomous vehicles may combine GNSS data with inertial sensors, radar, cameras, and vehicle control systems. Stable timing allows these subsystems to share data accurately and operate in harmony. Frequency Stability and Positioning Performance Frequency stability describes how well an oscillator maintains its output frequency under changing conditions. In positioning systems, high frequency stability improves receiver performance by reducing timing drift and frequency offset. Several factors can affect oscillator stability: Temperature changes Supply voltage variation Aging Mechanical vibration Shock Electromagnetic interference PCB layout quality For outdoor positioning devices, temperature stability is especially important. A vehicle tracker, drone, marine navigation unit, or survey device may operate in hot, cold, or rapidly changing environments. If the oscillator frequency shifts with temperature, the receiver may experience reduced accuracy or slower satellite tracking. This is why many high-performance positioning applications use temperature-compensated crystal oscillators, also known as TCXOs. TCXO for GPS and GNSS Positioning A TCXO, or Temperature-Compensated Crystal Oscillator, is a crystal oscillator designed to maintain stable frequency across a wide temperature range. It includes compensation circuitry that corrects frequency changes caused by temperature variation. TCXOs are widely used in GPS and GNSS modules because they offer better stability than standard crystal oscillators while maintaining compact size and low power consumption. Benefits of TCXOs in positioning systems include: Faster satellite acquisition Better signal tracking Improved frequency accuracy Reliable operation across temperature changes Reduced positioning errors Enhanced performance in mobile devices For applications such as fleet tracking, precision agriculture, UAV navigation, timing modules, and high-performance GNSS receivers, TCXOs can significantly improve system reliability. Crystal Oscillators in Asset Tracking and IoT Positioning Location tracking has become a core feature in many IoT applications. Smart logistics, wearable devices, pet trackers, shipping containers, shared mobility platforms, and industrial asset monitoring systems all use positioning technology to report location. These devices often require compact, low-power crystal oscillators. Battery life is a major concern, so the oscillator must provide accurate timing while consuming minimal energy. A crystal oscillator for IoT positioning supports: GNSS wake-up timing Wireless communication synchronization Low-power standby operation Location data time-stamping Reliable tracking updates Efficient battery management In these applications, designers must balance timing accuracy, power consumption, package size, and cost. A carefully selected crystal oscillator can help extend battery life while maintaining dependable location performance. Crystal Oscillators in Automotive Positioning Modern vehicles increasingly depend on accurate positioning. Navigation systems, advanced driver assistance systems, vehicle-to-everything communication, telematics, fleet management, and autonomous driving all require reliable timing. Crystal oscillators support automotive positioning by providing stable frequency references for GNSS receivers, communication modules, sensors, and electronic control units. In autonomous and semi-autonomous vehicles, precise timing is even more important because the system must combine data from multiple sensors in real time. Automotive positioning systems must also withstand temperature extremes, vibration, shock, and electrical noise. High-quality crystal oscillators designed for automotive environments help maintain stable navigation performance under demanding conditions. Crystal Oscillators in Drones and UAV Navigation Drones and unmanned aerial vehicles rely on positioning systems for flight control, route planning, altitude management, mapping, and autonomous operation. A crystal oscillator helps the onboard GNSS receiver maintain timing accuracy while the drone moves through changing environments. Stable oscillator performance supports: Accurate flight positioning Reliable waypoint navigation Better return-to-home functionality Stable communication links Precise sensor data timing Improved mapping and surveying accuracy For professional UAV applications, such as aerial inspection, agriculture, defense, and geospatial mapping, timing stability directly affects mission quality and operational safety. Positioning Beyond GPS: Multi-Sensor Synchronization Many advanced positioning systems do not rely on GNSS alone. They combine multiple technologies, including: Inertial measurement units Accelerometers Gyroscopes Radar LiDAR Cameras Cellular positioning Wi-Fi p ER -