[ { "id": "https://openalex.org/W2891255005", "doi": "10.1109/icuas.2018.8453297", "title": "Preliminary Research on Remote Planning Methods for Paths of Unmanned Aerial Vehicles in the Safe Aerial Corridor", "year": 2018, "publication_date": "2018-06-01", "type": "conference-paper", "language": "en", "authors": [ "Zhibin Li", "Xiaojun Zhang", "Ying Lu", "Yanxiang Cui", "Minghang Li" ], "first_author": "Zhibin Li", "venue": null, "publisher": null, "abstract": "Opening of the low altitude airspace is imperatively driven by the great economic prospect. As a result, the low-altitude, slow and small (LSM) moving targets like unmanned aerial vehicles (UAVs) will cause hidden dangers to ground facilities, social order and living of people. This accordingly urges development of effective technologies to solve the problem. Different from ground vehicles' route planning, UAVs emphasize more on ground remote planning of paths in the safe aerial corridor. This paper presents preliminary research on this aspect. It firstly proposes a remote planning service structure based on WebGIS (Geographic Information System Based on Internet), then establishes modeling for planning space and its associated objects with reference to specific example, thirdly simulates and analyzes limitations of the structure and makes pertinent improvements to it using ant colony algorithm based 3D safe corridor planning method so as to meet strict requirements from air traffic control on minitype UAVs, and finally summarizes the research findings and renders future research highlights in this field.", "keywords": [ "Low altitude", "Computer science", "Route planning", "The Internet", "Service (business)", "Field (mathematics)", "Motion planning", "Transport engineering", "Geographic information system", "Remote sensing", "Engineering", "Altitude (triangle)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icuas.2018.8453297" }, { "id": "https://openalex.org/W7117368812", "doi": "10.63363/aijfr.2025.v06i06.2574", "title": "Swarm Intelligence-Based Routing for UAV Networks", "year": 2025, "publication_date": "2025-12-20", "type": "article", "language": null, "authors": [ "Neeraj Shrivastava", "Dhara Rathore", "Rajshree Panwar", "Sandeep Singh" ], "first_author": "Neeraj Shrivastava", "venue": "Advanced International Journal for Research", "publisher": null, "abstract": "This paper presents a Particle Swarm Optimization (PSO) based routing framework for unmanned aerial vehicle (UAV) networks. We formulate the routing problem as a multi-objective optimization that minimizes end-to-end delay, maximizes link reliability, and conserves UAV energy. A PSO algorithm is adapted to find near-optimal routes in dynamic UAV topologies. We implement a Python-based simulation to evaluate performance under varying node densities and mobility patterns. Results show that PSO-based routing outperforms baseline greedy and shortest-path methods in terms of packet delivery ratio (PDR) and average energy consumption for a range of scenarios.", "keywords": [ "Particle swarm optimization", "Routing (electronic design automation)", "Network packet", "Energy consumption", "Greedy algorithm", "Range (aeronautics)", "Node (physics)", "Swarm behaviour", "Baseline (sea)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aijfr.com/papers/2025/6/2574.pdf" }, { "id": "https://openalex.org/W4296709221", "doi": "10.11591/ijeecs.v28.i2.pp946-953", "title": "Routing flying Ad Hoc network using salp swarm algorithm", "year": 2022, "publication_date": "2022-09-21", "type": "article", "language": "en", "authors": [ "Alaa Ibrahim Mahmood", "Omar Ibrahim Alsaif", "Ibrahim Ahmed Saleh" ], "first_author": "Alaa Ibrahim Mahmood", "venue": "Indonesian Journal of Electrical Engineering and Computer Science", "publisher": "Institute of Advanced Engineering and Science (IAES)", "abstract": "An Ad-hoc network is collection of mobile nodes without the necessity of existing any centralized access points. In this paper introduce novel protocol that used to improveAdHoc protocol with salp swarm algorithm to routed flying Ad Hoc Network (FANET) by method of simulation. FANET predicated unmanned aerial vehicles (UAV), it designed wireless network has nodes withwith high mobility, actively changing topology and movement in 3D space. The main problem for FANET manner of routing packets among managed nodes. The new protocol based on salp swarm algorithm called “SalpAdHoc” protocol to solve routing problem and less the conjunction, thesimulation results of an experimental study confirming the feasibility of using salp swarm algorithms for routing in FANET are presented.", "keywords": [ "Computer network", "Computer science", "Wireless Routing Protocol", "Wireless ad hoc network", "Optimized Link State Routing Protocol", "Routing protocol", "Mobile ad hoc network", "Distributed computing", "Ad hoc wireless distribution service", "Swarm behaviour", "Destination-Sequenced Distance Vector routing", "Dynamic Source Routing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ijeecs.iaescore.com/index.php/IJEECS/article/download/27303/16803" }, { "id": "https://openalex.org/W4415398064", "doi": "10.1109/icicc66840.2025.11199507", "title": "Collaborative Path Planning for UAV-Truck Delivery", "year": 2025, "publication_date": "2025-08-15", "type": "conference-paper", "language": null, "authors": [ "Wen Jing Huang", "Xianghu Meng", "Ning Wang", "Yujie Wang" ], "first_author": "Wen Jing Huang", "venue": null, "publisher": null, "abstract": "This study formulates a Multi-Truck and MultiUAV Routing Problem with Flexible Recovery Operations (MTMDRP-FRO), aiming to minimize the total travel cost. To solve MTMDRP-FRO, a Two-Stage Adaptive Large Neighborhood Search Algorithm (TSALNS) is developed. The algorithm initially employs a clustering-based greedy method to generate a high-quality initial solution. Subsequently, three destruction operators and two repair operators are designed to perform local search and explore promising solution spaces. Moreover, the Metropolis criterion from simulated annealing is incorporated to enhance the adaptive operator selection mechanism, enabling the algorithm to escape local optima effectively. Comparative simulation experiments conducted against Gurobi, Genetic Algorithm with Variable Neighborhood Search (GAVNS) and Hybrid Clarke and Wright Saving Algorithm (HCW) demonstrate that TSALNS is an efficient and effective approach for solving the MTMDRP-FRO.", "keywords": [ "Simulated annealing", "Local optimum", "Variable neighborhood search", "Local search (optimization)", "Greedy algorithm", "Greedy randomized adaptive search procedure", "Iterated local search", "Operator (biology)", "Variable (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icicc66840.2025.11199507" }, { "id": "https://openalex.org/W4413896555", "doi": "10.1201/9781003370659-8", "title": "Energy-Efficient UAV Routing Optimization Using Genetic Algorithms in Dynamic Logistics Systems", "year": 2025, "publication_date": "2025-09-01", "type": "book-chapter", "language": "en", "authors": [ "Abdullah Tunç Büyüksan", "Kerem Utku Demir", "Durdu Hakan Utku", "Kamer Özgün" ], "first_author": "Abdullah Tunç Büyüksan", "venue": "Advances in Aerospace Engineering", "publisher": "Hindawi Publishing Corporation", "abstract": "The pandemic’s impacts aside, the growth of e-commerce and the development of drone technology have increased demand for freight transportation on a worldwide scale. In order to address this demand, unmanned aerial vehicles, or UAVs, have emerged as a practical logistics solution. Optimizing cargo UAV routes is the aim of this study, as it is expected to play a significant role in today’s logistics systems. Using randomized sites, the proposed methodology takes into account the unique requirements and constraints of cargo UAVs while aiming to minimize energy usage during routing. An optimized Genetic Algorithm (GA) with a focus on vehicle routing problems (VRPs) is used to find the optimal route that meets the requirements. Unlike conventional routing methods, which often assume fixed points for service requests, this methodology permits the assumption of random sites to better mimic real-world scenarios. Scalable solutions for various operational circumstances are provided by the algorithm due to its capacity to manage varying quantities of service-requesting points. The results of the experiments conducted with varying numbers of service-requesting points demonstrate the effectiveness of the proposed methodology. A respectable level of accuracy is revealed when comparing the exact solutions with the GA, particularly in situations with few points. Furthermore, the energy efficiency performance of the algorithm remains constant despite variations in the number of sites requesting services. All things considered, by concentrating on a particular objective, this work developed a revolutionary strategy for UAV routing optimization strategies to determine the best routes in dynamic, constantly changing logistical scenarios.", "keywords": [ "Computer science", "Genetic algorithm", "Routing (electronic design automation)", "Energy (signal processing)", "Routing algorithm", "Algorithm", "Mathematical optimization", "Distributed computing", "Computer network", "Mathematics", "Machine learning", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/9781003370659-8" }, { "id": "https://openalex.org/W2504767057", "doi": "10.1016/s0005-1098(01)00042-5", "title": "Mechanics and control of robots", "year": 2001, "publication_date": "2001-06-01", "type": "article", "language": "en", "authors": [ "Noah J. Cowan" ], "first_author": "Noah J. Cowan", "venue": "Automatica", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Metaheuristic", "Computer science", "Evolutionary algorithm", "Simulated annealing", "Mathematical optimization", "Algorithm", "Artificial intelligence", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/s0005-1098(01)00042-5" }, { "id": "https://openalex.org/W1793487853", "doi": null, "title": "An Integrated Command and Control Architecture Concept for Unmanned Systems in the Year 2030", "year": 2010, "publication_date": "2010-05-31", "type": "article", "language": "en", "authors": [ "Keith B Quincy", "Jamarr J Johnson", "Michael G Moran", "Drew J Nilsson", "Bradley G Thompson" ], "first_author": "Keith B Quincy", "venue": "Naval Postgraduate School (U.S.)", "publisher": null, "abstract": "Abstract : U.S. Forces require an integrated Command and Control Architecture that enables operations of a dynamic mix of manned and unmanned systems. The level of autonomous behavior correlates to: 1) the amount of trust with the reporting vehicles, and 2) the multi-spectral perspective of the observations. The intent to illuminate the architectural issues for force protection in 2030 was based on a multi-phased analytical model of High Value Unit (HVU) defense. The results showed that autonomous unmanned aerial vehicles are required to defeat high-speed incoming missiles. To evaluate the level of autonomous behavior required for an integrated combat architecture, geometric distributions were modeled to determine force positioning, based on a scenario driven Detect-to-Engage timeline. Discrete event simulation was used to schedule operations, and a datalink budget assessment of communications to determine the critical failure paths in the integrated combat architecture. The command and control principles used in the integrated combat architecture were based on Boyd's OODA (Obseve, Orient, Decide, and Act) Loop. A conservative fleet size estimate, given the uncertainties of the coverage overlap and radar detection range, a fleet size of 35 should be anticipated given an UAV detection range of 20km and radar coverage overlap of 4 seconds.", "keywords": [ "Command and control", "Architecture", "Timeline", "Schedule", "Computer science", "Radar", "Range (aeronautics)", "Systems engineering", "Real-time computing", "Systems architecture", "Simulation", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://apps.dtic.mil/dtic/tr/fulltext/u2/a524154.pdf" }, { "id": "https://openalex.org/W3011424635", "doi": "10.1155/2020/3515709", "title": "Inverting the Multiple-Assisting Tool Network Problem to Solve for Optimality", "year": 2020, "publication_date": "2020-03-20", "type": "article", "language": "en", "authors": [ "Robert Rich" ], "first_author": "Robert Rich", "venue": "Advances in Operations Research", "publisher": "Hindawi Publishing Corporation", "abstract": "Many network problems deal with the routing of a main tool comprised of several parallel assisting tools. These problems can be found with multi-tool-head routing of CNC machines, waterjets, plasma sprayers, and cutting machines. Other applications involve logistics, distribution, and material handling that require a main tool with assisting tools. Currently no studies exist that optimally route a main tool comprised of and fitted with multiple tools, nor do any studies evaluate the impact of adding additional capabilities to the tool set. Herein we define the network routing problem for a main tool comprised of multiple secondary tools. We introduce first principles to properly configure the main tool with the appropriate number of supporting tools such that that system is not overstatured. We invert the network geometry to extract the “best case” configuration for toolset configuration to include speed, range, and number of such that the system is lean. Our computational studies reveal that the theorems introduced herein greatly improve the overall system performance without oversaturating it with unused resources. In order to validate experiments, we define a mixed integer program and compare it to our metaheuristics developed for experimentation. Both the MIP and the metaheuristics herein optimally route a main tool with multiple assisting tools as well as the routing of a parcel delivery truck comprised of many drones.", "keywords": [ "Computer science", "Metaheuristic", "Truck", "Routing (electronic design automation)", "Vehicle routing problem", "Distributed computing", "Algorithm", "Computer network", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/aor/2020/3515709.pdf" }, { "id": "https://openalex.org/W4416790712", "doi": "10.1016/j.ifacol.2025.11.516", "title": "Swarm-based Cooperative Coverage Strategy for Amphibious Unmanned Systems in Complex Wetland Environments", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Yuhang He", "Liao Bo", "Qi Dang" ], "first_author": "Yuhang He", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "This study investigates the effectiveness of amphibious unmanned aerial vehicles (Amphi-UAVs) in complex wetland inspections, emphasizing coverage path planning (CPP) optimization for dual-domain operations. We enhance the Scalable Coverage Path Planning (SCoPP) framework by introducing waterborne load balancing during conflict cell allocation, mode-switching path planning, and multi-objective optimization to reduce mission time and energy consumption. Comparative experiments show Amphi-UAVs outperform traditional quadrotor UAVs in wetland environments, especially in endurance and adaptability. We also determine the optimal Amphi-UAVs fleet size for balancing time and energy efficiency. Simulations in the Xixi Wetland validate the algorithm’s real-world applicability.", "keywords": [ "Wetland", "Scalability", "Path (computing)", "Motion planning", "Energy (signal processing)", "Efficient energy use" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2025.11.516" }, { "id": "https://openalex.org/W4402510183", "doi": "10.1109/tvt.2024.3461161", "title": "DRL-AdCAR: Adaptive Coding-Aware Routing With Maximum Coding Opportunities and High-Quality via Deep Reinforcement Learning in FANET", "year": 2024, "publication_date": "2024-09-13", "type": "article", "language": "en", "authors": [ "Bo Song", "Lei Xu", "Ping Wang", "Xiulin Qiu", "Yaqi Ke", "Junjie Gu", "Yuwang Yang", "Anhong Chen", "Fan Zhang" ], "first_author": "Bo Song", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Research on flying ad-hoc networks (FANETs) has become important with the development of unmanned aerial vehicle (UAV) systems. Routing design in FANET is a major challenge due to its inherent characteristics, including dynamic network topology and network self-organization. Coding-aware routing based on network coding improves the performance of the network by selecting paths with more coding opportunities. However, current methods are mostly used in networks with relatively fixed topologies, which are difficult to adapt to the FANET environment. To address these issues, we propose an adaptive coding-aware routing algorithm via deep reinforcement learning (DRL-AdCAR). First, we transform the routing problem into a Markov decision model. Then, coding opportunities, coding gains, and link quality are considered simultaneously in the reward function to avoid the drawbacks of coding-aware routing algorithms that simply aim to increase coding opportunities while greatly affecting other aspects of network performance. In addition, we present an improvement of the deep deterministic policy gradient (DDPG) algorithm for FANET, combining the gated recurrent unit (GRU) and long short-term memory networks (LSTM) algorithms to replace the traditional neural network structure, which ensures prediction accuracy and improves the training efficiency. The experimental results showed that DRL-AdCAR can adaptively select the transmission paths with the most suitable coding opportunities according to environmental changes in FANET, improving coding performance and enhancing the network throughput and packet delivery rate.", "keywords": [ "Reinforcement learning", "Computer science", "Coding (social sciences)", "Computer network", "Artificial intelligence", "Mathematics" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2024.3461161" }, { "id": "https://openalex.org/W2052741162", "doi": "10.1002/rnc.1293", "title": "Cooperative control of unmanned aerial vehicles", "year": 2007, "publication_date": "2007-12-03", "type": "article", "language": "en", "authors": [ "Steven A. Rasmussen", "Corey J. Schumacher" ], "first_author": "Steven A. Rasmussen", "venue": "International Journal of Robust and Nonlinear Control", "publisher": "Wiley", "abstract": "The use of unmanned aerial vehicles (UAV) for military missions has received growing attention in the last decade, with civilian usage also starting to expand. Apart from the obvious advantage of not placing human life at risk, the lack of a human pilot also enables a wealth of new operational paradigms. To realize these new paradigms, the UAVs must have a high level of autonomy and, often, work cooperatively in groups. With the development of UAV systems that have significant processing, communications, navigation, and sensing capabilities, the stage has been set to deploy cooperative control algorithms that effectively increase the capabilities of UAV teams beyond what can be accomplished by individual vehicles. Extensive research has been conducted in recent years on the development of novel cooperative decision and control algorithms to enable effective and efficient utilization of group capabilities. This special issue presents a sampling of solution approaches to problems involving cooperative control of UAVs, with an emphasis on the ‘outer-loop’ and decision-making aspects of the problem. It brings together papers that cover a broad range of cooperative control topics, such as vehicle/task assignment, sensor coverage, cooperative patrol, multi-player games, and sequential resource allocation. In this context, cooperative control is the study, development, and implementation of algorithms that either enable a group of UAVs to perform missions that cannot be accomplished individually, or else improve the team efficiency over that which can be achieved by UAVs acting as independent agents. Owing to the multidisciplinary nature of cooperative decision and control problems, the papers in this issue do not fit neatly within the standard feedback control paradigm. Some aspects of cooperative control, such as formation control and cooperative motion, which are perhaps more familiar to control engineers, are not emphasized. It is our hope that readers will nevertheless find the papers interesting and accessible. Operations involving teams of cooperative agents face numerous fundamental challenges, including computational complexity, information uncertainty, communication constraints, operator interaction or mixed initiative control, and adversary action. The papers in this special issue address a cross-section of these fundamental challenges. These challenges appear in cooperative control problems in forms such as combinatorial solution space, sensor capabilities and limitations, hardware systems capability, operator delays and operator errors, prediction of and reaction to enemy actions, and communication system limitations. In this special issue, the papers are arranged according to the four sub-topics that address task assignment, optimal distributions, multi-player differential games, and mixed initiative control. Task assignment: The first two papers of the issue address task assignment. This is the problem of allocating vehicles to perform tasks, with aspects of task scheduling and path planning commonly also involved. The task assignment problem is dominated by the fundamental difficulties of computational complexity, information uncertainty, and implementability. The first paper, ‘A Robust Approach to the UAV Task Assignment Problem’, by Alighanbari and How, explores methods to construct and deploy UAV assignment algorithms that are insensitive to uncertainties in the environment such as target values and distances traveled by the UAVs. The authors approach this problem by tightly integrating two approaches for improving the robustness of the assignment algorithm. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness, but which can be overly conservative for long duration plans. A second approach is to re-plan as the situational awareness is updated, which results in the best plan given the current information, but can lead to a churning type of instability if the updates are performed too frequently. The strategy proposed in this paper combines robust planning with techniques developed to eliminate churning. In the second paper, entitled ‘Tree Search Algorithm for Assigning Cooperating UAVs to Multiple Tasks’, Rasmussen and Shima address the inherent coupling between path planning, task assignment, and timing. In this paper, task costs are based on flyable trajectories calculated using a Dubin's car model for the vehicle dynamics, but Euclidean distances are used to evaluate unsearched branches of the tree, providing a lower bound for the cost of unevaluated assignments, with candidate optimal solutions providing an upper bound. An iterative application of these lower and upper bounds on active subsets within the feasible set enables efficient pruning of the solution tree, resulting in fast, efficient computation of monotonically improving solutions to the combined path planning, task assignment, and scheduling problems. Optimal distributions: Problems involving distribution of vehicles or mobile sensor agents over regions or target sets are addressed in the next three papers. The first paper in this area is ‘Efficient Routing of Multiple Vehicles with no Explicit Communications,’ by Arsie and Frazzoli. This paper considers a class of dynamic vehicle routing problems, in which a number of mobile agents in the plane must visit target points generated over time by a stochastic process. Motion coordination strategies are designed to minimize the expected time between the appearance of a target point and the time it is visited by one of the agents. Control strategies are presented that require minimal or no communication between agents, but which still provide the same level of steady-state performance achieved by the best known decentralized strategies. It is demonstrated that inter-agent communication does not improve the efficiency of such systems, but merely affects the r", "keywords": [ "Context (archaeology)", "Computer science", "Control (management)", "Set (abstract data type)", "Command and control", "Task (project management)", "Drone", "Multidisciplinary approach", "Operations research", "Systems engineering", "Engineering", "Artificial intelligence" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/rnc.1293" }, { "id": "https://openalex.org/W2354882196", "doi": null, "title": "Study on Route Planning for UAV and the Resource of Threat Model", "year": 2009, "publication_date": "2009-01-01", "type": "article", "language": "en", "authors": [ "Qian Li" ], "first_author": "Qian Li", "venue": "Microcomputer Information", "publisher": "China Computer Users Association", "abstract": "Route planning for unmanned aerial vehicle is essence and indispensability of mission planning.In order to plan precise route of unmanned aerial vehicle,this paper establishes some relative models of threat resources,and plan route of unmanned aerial vehicle based on genetic algorithm,and Solve the problem of threats changing with altitude,which result in imprecise route planning for unmanned aerial vehicle.The simulation results show that the models could more precisely compute unmanned aerial vehicle route.", "keywords": [ "Computer science", "Route planning", "Plan (archaeology)", "Low altitude", "Resource (disambiguation)", "Genetic algorithm", "Motion planning", "Operations research", "Altitude (triangle)", "Artificial intelligence", "Transport engineering", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-WJSJ200919077.htm" }, { "id": "https://openalex.org/W2938498176", "doi": "10.31256/ukras19.13", "title": "Exploiting System Capacity with a Distributed Routing Strategy for UAVs*", "year": 2019, "publication_date": "2019-03-21", "type": "conference-paper", "language": "en", "authors": [ "William Bonnell" ], "first_author": "William Bonnell", "venue": "Journal of robotics & autonomous systems", "publisher": null, "abstract": "This paper presents a routing strategy for UAVs that can be applied in conjunction with lower level navigation and collision avoidance methods. The strategy presented draws inspiration from traditional road vehicle routing where some cars are directed down less busy routes even if it results in a longer path. Our strategy will allow individual UAVs to route themselves in 2D space in order to avoid areas of high-density traffic. The proposed approach is then explored in simulation. Details of the simulation set-up are provided. The results demonstrate that the traffic is safer when the routing strategy is used compared with just a simple collision avoidance method", "keywords": [ "Computer science", "Routing (electronic design automation)", "Distributed computing", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.31256/ukras19.13" }, { "id": "https://openalex.org/W4392391405", "doi": "10.12785/ijcds/150158", "title": "Routing Approaches used for Electrical Vehicles Navigation: A Survey", "year": 2024, "publication_date": "2024-02-01", "type": "article", "language": "en", "authors": [ "Shaima Ebraheem Hejres", "Amine Mahjoub", "Nabil M. Hewahi" ], "first_author": "Shaima Ebraheem Hejres", "venue": "International Journal of Computing and Digital Systems", "publisher": null, "abstract": "The growing demand for Electric Vehicles (EVs) depends on the high integration of this technology in many areas.Therefore, an important area of research raises interest in finding the optimal path-planning solution for electric vehicles.This paper discusses several reviews and analyzes some of the constraints of the techniques used to improve these systems.The paper discusses common models used in Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGV).This paper investigates the planning approaches that lead to finding the optimal route for the tour from the source to the destination.The review outlines the different models and systems of Unmanned Ariel Vehicles (UAV) and Unmanned Ground Vehicles (UGV).This paper can be considered as comprehensive survey research for EV routing techniques to assist researchers choose the appropriate approach for developing a system based on optimization techniques, machine learning, or Hybrid Approaches (HAs) techniques.Optimization techniques are mostly used to find the optimal path and achieve multi-objective goals.Some findings were approved as the best models inspired by natural biological as genetic algorithms, Particle Swarm Optimization, and Ant Colony Optimization.In addition to machine learning techniques as Reinforcement Learning.The hybrid approach techniques that combine optimization and machine learning techniques can increase robustness in solving routing problems.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Transport engineering", "Systems engineering", "Engineering", "Embedded system" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journal.uob.edu.bh:443/bitstream/123456789/5126/3/IJCDS150158_1570894322.pdf" }, { "id": "https://openalex.org/W4362643350", "doi": "10.5281/zenodo.7744436", "title": "International Research in Engineering Sciences", "year": 2023, "publication_date": "2023-03-17", "type": "book", "language": "en", "authors": [ "Doç. Dr. Alper Bideci" ], "first_author": "Doç. Dr. Alper Bideci", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "CONTENTS CHAPTER 1 .................................................................................................................. 7 Equivalent Circuit Models of Battery Technologies as Electrochemical Energy Storage Methods: A Review Study on, Electrical Equivalent Circuit Models of Li-Ion Batteries Dr. Taner ÇARKIT CHAPTER 2 ............................................................................................................... 37 Heuristic Intelligence Approach to Routing Planning of Uavs Networks Asst. Prof. Dr. Sercan YALÇIN CHAPTER 3 ............................................................................................................... 51 Monitoring Water Surfaces By Remote Sensing: The Case of Manyas Kusgolu, Ulubat, And Iznik Lakes in Türkiye Dr. Osman Salih YILMAZ CHAPTER 4 ............................................................................................................... 67 Heterogeneous Fleet Vehicle Routing Problems and the Metaheuristic Approaches Asst. Prof. Melis ALPASLAN TAKAN CHAPTER 5 ............................................................................................................... 85 Approaches With Artificial Intelligence (Ai) Algorithms to Smart System Works Lecturer Ali ÇETİNKAYA CHAPTER 6 ............................................................................................................. 103 Recovery Applications of Waste Foundry Sand Asst. Prof. Merve SOGANCİOGLU KALEM CHAPTER 7 ............................................................................................................. 119 A Comprehensive Outlook On the Influences of Heat Treatment On the Mechanical, Friction, and Wear Properties of the Additively Manufactured Plastics Assist. Prof. Dr. ÇAĞIN BOLAT ISBN: 978-625-6971-50-9 4 CHAPTER 8 ............................................................................................................. 143 Experimental Study on Biocompatibility Properties of Photo-Resin 3d Printer Materials Dr. Bünyamin ÇİÇEK CHAPTER 9 ............................................................................................................. 155 The Spectrum Sensing Techniques and Methods Fikriye ATAMAN CHAPTER 10 .......................................................................................................... 195 Investigation of Physicochemical Parameters Affecting the Crosslinking of Epdm Rubber Chemist Şule ÇİÇEK Prof. Ali KARA, Ph.D CHAPTER 11 .......................................................................................................... 213 Investigation of Intumescent Flame Retardant Coatings On Structural Steels Dr. Zafer KAHRAMAN CHAPTER 12 .......................................................................................................... 231 Modelling and Analysis of Wall Brackets Part Obtained Via Material Extrusion Technique Research Assistant Faik YILAN Research Assistant İbrahim Baki ŞAHİN Assoc. Prof Levent URTEKİN CHAPTER 13 .......................................................................................................... 241 Treatment of Bio-Medical Waste Dr. Aysun ALTIKAT CHAPTER 14 .......................................................................................................... 259 Performance Improvement With Optimization of Machine Learning Methods For Sleep Stage Classification From Eeg Signals Dr. Erdem TUNCER Prof.Dr. Emine Doğru BOLAT International Research in Engineering Sciences 5 CHAPTER 15 .......................................................................................................... 289 Df Based Cooperative Uavs Communication With Co-Channel Interference Asst. Prof. Dr. Muhammet Ali KARABULUT Asst. Prof. Dr. A. F. M. Shahen SHAH CHAPTER 16 .......................................................................................................... 305 Detection and Identification of Microstructural Features in Microtomography Data of Single Foam Struts Using A Convolutional Neural Network Ali Can KAYA Elif SALİHOĞLU CHAPTER 17 .......................................................................................................... 329 Good Laboratory Practices: Differences According To New Version of Iso/Iec 17025, Applications and Regulations in Food Analysis Gülen YEŞİLÖREN AKAL CHAPTER 18 .......................................................................................................... 357 Anomaly Detection Using Labview-Based Machine Learning Algorithms Dr. Mahmut DİRİK CHAPTER 19 .......................................................................................................... 377 Eia Framework in Turkey and A Critical Review of Eia Decisions For the Construction Sector Prof. Güray SALIHOĞLU, Ph.D Mohamed Amin OUFQUIR CHAPTER 20 .......................................................................................................... 409 An Overview Of Pyruvate Biosensors Prof. Şeyda KORKUT URU Assoc. Prof. Dr. M. Samet KILIÇ İlayda DİKKULAK ISBN: 978-625-6971-50-9 6 CHAPTER 21 .......................................................................................................... 429 Modeling of Air Pollution Dr. Mine Tulin ZATEROGLU CHAPTER 22 .......................................................................................................... 441 Porous Geopolymer Adsorbents For Removal Of Heavy Metals and Dyes: A Recent Overview Asst. Prof. Dr. İlker ACAR CHAPTER 23 ..................................................... Hata! Yer işareti tanımlanmamış. Fire Performance Evaluation of A Residential Building: Structural Analaysis And Response Dr. Burak Kaan ÇIRPICI Mr. Abdulsamet AKTURK CHAPTER 24 ..................................................... Hata! Yer işareti tanımlanmamış. Boron Nitride Nanotubes, Their Synthesis Methods and Biological Applications İrem ULUIŞIK YILMAZ, PhD. Atike İNCE YARDIMCI, PhD. CHAPTER 25 .............................................................................................", "keywords": [ "Engineering ethics", "Engineering" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://zenodo.org/record/7744436" }, { "id": "https://openalex.org/W4416243484", "doi": "10.2514/1.i011749", "title": "Autonomous Air–Ground Vehicle Operations Optimization in Hazardous Environments: A Multi-Armed Bandit Approach", "year": 2026, "publication_date": "2026-06-19", "type": "article", "language": "en", "authors": [ "Jimin Choi", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": "Journal of Aerospace Information Systems", "publisher": "American Institute of Aeronautics and Astronautics", "abstract": "Hazardous environments such as chemical spills, radiological zones, and biocontaminated sites pose significant threats to human safety and public infrastructure. Rapid and reliable hazard mitigation in these settings is often unsafe for humans, calling for autonomous systems that can adaptively sense and respond to evolving risks. This paper presents a decision-making framework for autonomous vehicle dispatch in hazardous environments with uncertain and evolving risk levels. The system integrates a Bayesian upper confidence bound (BUCB) sensing strategy with task-specific vehicle routing problems with profits (VRPPs), enabling adaptive coordination of unmanned aerial vehicles (UAVs) for hazard sensing and unmanned ground vehicles (UGVs) for cleaning. Using VRPPs allows selective site visits under resource constraints by assigning each site a visit value that reflects sensing or cleaning priorities. Site-level hazard beliefs are maintained through a time-weighted Bayesian update. BUCB scores guide UAV routing to balance exploration and exploitation under uncertainty, while UGV routes are optimized to maximize expected hazard reduction under resource constraints. Simulation results demonstrate that our framework reduces the number of dispatch cycles required to resolve hazards by around 30% on average compared to uninformed baseline dispatch strategies, underscoring the value of uncertainty-aware vehicle dispatch for reliable hazard mitigation.", "keywords": [ "Hazardous waste", "Hazard", "Resource (disambiguation)", "Routing (electronic design automation)", "Unmanned ground vehicle", "Baseline (sea)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2508.08217" }, { "id": "https://openalex.org/W7130396686", "doi": "10.1109/ic-c68228.2025.00031", "title": "Complete Coverage Path Planning for Multiple Heterogeneous UAVs in Plant Protection", "year": 2025, "publication_date": "2025-04-25", "type": "conference-paper", "language": null, "authors": [ "Cencen Xiao", "Hanmei Zhou", "Jiahao Leng", "Lanfeng Hua", "S. D. Hu", "Yang Yu", "Qishui Zhong" ], "first_author": "Cencen Xiao", "venue": null, "publisher": null, "abstract": "As a new type of agricultural equipment that is efficient, environmentally friendly and safe, plant protection uncrewed aerial vehicles (UAVs) have been widely used in crop pest control in recent years. Complete coverage path planning (CCPP) technology can not only improve the operation efficiency of UAVs and reduce energy consumption, but also ensure complete coverage of the target area while reducing the repetition rate. In this paper, the problem of complete coverage path planning for multi-heterogeneous plant protection UAVs is solved. The goal is to reduce operating time. Firstly, the minimum cell decomposition of the irregular farmland area is carried out. Secondly, the area to be operated is modeled as a graph structure, so that the problem is transformed into a vehicle routing problem (VRP). Finally, a probability-based random heuristic algorithm was proposed to obtain a better complete coverage path in a short time, and numerical simulations were carried out to verify the effectiveness and feasibility of the algorithm.", "keywords": [ "Motion planning", "Any-angle path planning", "Path (computing)", "Heuristic", "Routing (electronic design automation)", "Route planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ic-c68228.2025.00031" }, { "id": "https://openalex.org/W7162817362", "doi": null, "title": "Integrated Exploration-Aware UAV Route Optimization and Path Planning", "year": 2026, "publication_date": "2026-05-27", "type": "preprint", "language": null, "authors": [ "Jimin Choi", "Grant Stagg", "Cameron K. Peterson", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatially imprecise, and subject to change during execution. For example, initial reports may identify a region where a hazard is likely to exist, but the actual hazard may be displaced, partially observed, or entirely unreported. We present an integrated exploration-aware UAV route optimization and path planning framework for hazard monitoring under uncertain and evolving prior information. The environment is represented as a spatial risk map, where each location has an associated belief of hazardous conditions. Reported hazards are modeled as uncertain regions of interest (ROIs) rather than confirmed target locations, requiring the UAV to inspect reported areas while also using its limited flight endurance to explore informative regions. The proposed method solves a vehicle routing problem over reported ROIs, augments the route with auxiliary pseudo-nodes to improve spatial coverage, allocates the remaining flight distance budget across route segments, and optimizes dynamically feasible B-spline trajectories for local exploration. During execution, UAV measurements update a grid-based belief map, and the remaining trajectory is replanned when new information and the remaining budget justify adaptation. Across 48 scenario configurations, online replanning improves average KL reduction by 15.9% over the offline optimized planner and 48.6% over straight-line traversal.", "keywords": [ "Hazard", "Trajectory", "Hazardous waste", "Planner", "Routing (electronic design automation)", "Motion planning", "Path (computing)", "Reduction (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2605.28654" }, { "id": "https://openalex.org/W7162685920", "doi": "10.48550/arxiv.2605.28654", "title": "Integrated Exploration-Aware UAV Route Optimization and Path Planning", "year": 2026, "publication_date": "2026-05-27", "type": "preprint", "language": null, "authors": [ "Jimin Choi", "Grant Stagg", "Cameron K. Peterson", "Max Z. Li" ], "first_author": "Jimin Choi", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatially imprecise, and subject to change during execution. For example, initial reports may identify a region where a hazard is likely to exist, but the actual hazard may be displaced, partially observed, or entirely unreported. We present an integrated exploration-aware UAV route optimization and path planning framework for hazard monitoring under uncertain and evolving prior information. The environment is represented as a spatial risk map, where each location has an associated belief of hazardous conditions. Reported hazards are modeled as uncertain regions of interest (ROIs) rather than confirmed target locations, requiring the UAV to inspect reported areas while also using its limited flight endurance to explore informative regions. The proposed method solves a vehicle routing problem over reported ROIs, augments the route with auxiliary pseudo-nodes to improve spatial coverage, allocates the remaining flight distance budget across route segments, and optimizes dynamically feasible B-spline trajectories for local exploration. During execution, UAV measurements update a grid-based belief map, and the remaining trajectory is replanned when new information and the remaining budget justify adaptation. Across 48 scenario configurations, online replanning improves average KL reduction by 15.9% over the offline optimized planner and 48.6% over straight-line traversal.", "keywords": [ "Hazard", "Trajectory", "Hazardous waste", "Planner", "Routing (electronic design automation)", "Motion planning", "Path (computing)", "Reduction (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2605.28654" }, { "id": "https://openalex.org/W4405346397", "doi": "10.2174/0118722121322545241015160200", "title": "A Review of Research on Shortest Path Planning Algorithms for Mobile Robots", "year": 2024, "publication_date": "2024-12-12", "type": "article", "language": "en", "authors": [ "Lutong Wang", "Xiaodong Yang", "Shouguo Liu", "Hongbo Liu", "Jiakuan Peng", "Weifeng Liu" ], "first_author": "Lutong Wang", "venue": "Recent Patents on Engineering", "publisher": null, "abstract": "Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.", "keywords": [ "Motion planning", "Mobile robot", "Computer science", "Global Positioning System", "Field (mathematics)", "Robot", "Mobile robot navigation", "Real-time computing", "Artificial intelligence", "Operations research", "Engineering", "Robot control" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2174/0118722121322545241015160200" }, { "id": "https://openalex.org/W7130587247", "doi": "10.1109/ictc66702.2025.11389120", "title": "Efficient UAV Coverage Algorithms for Areas Requiring Heterogeneous Sensors", "year": 2025, "publication_date": "2025-10-14", "type": "conference-paper", "language": null, "authors": [ "Taravat Darbouei", "Sanghwan Lee" ], "first_author": "Taravat Darbouei", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) are increasingly used for line coverage, mapping, and surveillance, where efficiency and cost-effectiveness are critical. A key challenge in real-world deployments is that required edges often demand different sensors, yet most prior studies assume UAVs carry only a single sensor type. This work addresses UAV coverage optimization under strict time and energy limits in heterogeneous sensor environments. We develop and compare two strategies: (i) a Greedy algorithm, where each UAV carries a single sensor and services only compatible edges, and (ii) a Multiple Sensors algorithm, where each UAV carries all required sensors and can cover any edge. Using urban datasets from New York, London, and Tehran, we demonstrate distinct trade-offs. The Greedy algorithm achieves the lowest overall operational cost by balancing fleet size and energy consumption, whereas the Multiple Sensors algorithm minimizes the number of UAVs required but incurs higher per-UAV energy usage due to increased payload. These findings provide practical guidelines for selecting deployment strategies based on mission priorities—whether minimizing fleet size or reducing total cost—and support UAV applications in urban planning, disaster response, and smart city operations.", "keywords": [ "Software deployment", "Key (lock)", "Greedy algorithm", "Cover (algebra)", "Drone", "Wireless sensor network", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictc66702.2025.11389120" }, { "id": "https://openalex.org/W7167464387", "doi": "10.2139/ssrn.7067362", "title": "Integrated Exploration-Aware UAV Route Optimization and Path Planning", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "J H Choi", "Grant Stagg", "Cameron K. Peterson", "Max Z. Li" ], "first_author": "J H Choi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Hazard", "Trajectory", "Hazardous waste", "Planner", "Routing (electronic design automation)", "Motion planning", "Path (computing)", "Reduction (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7067362" }, { "id": "https://openalex.org/W4404214331", "doi": "10.1016/j.ejor.2024.11.015", "title": "Multi-objective route planning of an unmanned air vehicle in continuous terrain: An exact and an approximation algorithm", "year": 2024, "publication_date": "2024-11-10", "type": "article", "language": "en", "authors": [ "Erdi Daşdemir", "Murat Köksalan", "Diclehan Tezcaner Öztürk" ], "first_author": "Erdi Daşdemir", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Terrain", "Route planning", "Computer science", "Algorithm", "Mathematical optimization", "Approximation algorithm", "Mathematics", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11511/112817" }, { "id": "https://openalex.org/W4200459527", "doi": "10.1109/ccac51819.2021.9633293", "title": "Distributed Assignment with Energy Constraints of a UAV fleet for Resource Distribution", "year": 2021, "publication_date": "2021-10-19", "type": "conference-paper", "language": "en", "authors": [ "Juan Sebastian Gonzalez-Rojas", "Juan Martinez-Piazuelo", "Nicanor Quijano" ], "first_author": "Juan Sebastian Gonzalez-Rojas", "venue": null, "publisher": null, "abstract": "This paper addresses the distributed resource allocation through unmanned aerial vehicles (UAV) fleets in rural areas. In particular, we cast the resource distribution as an energy constrained vehicle-target assignment problem, and we solve this problem with an iterative distributed graph algorithm. Our proposed method allows an energy efficient assignment, satisfying UAV energy, load, and communication constraints. Our proposed algorithm is characterized through numerical simulations for various fleet sizes and area of operations (AO) partitions. Simulation results illustrate the proposed method.", "keywords": [ "Computer science", "Resource allocation", "Mathematical optimization", "Iterative method", "Resource distribution", "Resource management (computing)", "Resource (disambiguation)", "Energy (signal processing)", "Distributed computing", "Distributed algorithm", "Distributed generation", "Graph" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccac51819.2021.9633293" }, { "id": "https://openalex.org/W4223658008", "doi": "10.48550/arxiv.2204.04767", "title": "Risk-aware UAV-UGV Rendezvous with Chance-Constrained Markov Decision Process", "year": 2022, "publication_date": "2022-04-10", "type": "preprint", "language": "en", "authors": [ "Shi, Guangyao", "Nare Karapetyan", "Asghar, Ahmad Bilal", "Jean-Paul Reddinger", "James M. Dotterweich", "James D. Humann", "Pratap Tokekar" ], "first_author": "Shi, Guangyao", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We study a chance-constrained variant of the cooperative aerial-ground vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited battery capacity and an Unmanned Ground Vehicle (UGV) that can also act as a mobile recharging station need to jointly accomplish a mission such as monitoring a set of points. Due to the limited battery capacity of the UAV, two vehicles sometimes have to deviate from their task to rendezvous and recharge the UAV\\@. Unlike prior work that has focused on the deterministic case, we address the challenge of stochastic energy consumption of the UAV\\@. We are interested in finding the optimal policy that decides when and where to rendezvous such that the expected travel time of the UAV is minimized and the probability of running out of charge is less than a user-defined tolerance. We formulate this problem as a Chance Constrained Markov Decision Process (CCMDP). To the best knowledge of the authors, this is the first CMDP-based formulation for the UAV-UGV routing problems under power consumption uncertainty. We adopt a Linear Programming (LP) based approach to solve the problem optimally. We demonstrate the effectiveness of our formulation in the context of an Intelligence Surveillance and Reconnaissance (ISR) mission.", "keywords": [ "Markov decision process", "Rendezvous", "Computer science", "Partially observable Markov decision process", "Context (archaeology)", "Unmanned ground vehicle", "Routing (electronic design automation)", "Reinforcement learning", "Markov process", "Task (project management)", "Real-time computing", "Process (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2204.04767" }, { "id": "https://openalex.org/W2259596318", "doi": null, "title": "Motion Planning for Unmanned Aerial Vehicles with Resource Constraints", "year": 2012, "publication_date": "2012-10-19", "type": "dissertation", "language": "en", "authors": [ "Kaarthik Sundar" ], "first_author": "Kaarthik Sundar", "venue": "OakTrust (Texas A&M University Libraries)", "publisher": "Texas A&M University", "abstract": "Small Unmanned Aerial Vehicles (UAVs) are currently used in several surveillance applications to monitor a set of targets and collect relevant data. One of the main constraints that characterize a small UAV is the maximum amount of fuel the vehicle can carry. In the thesis, we consider a single UAV routing problem where there are multiple depots and the vehicle is allowed to refuel at any depot. The objective of the problem is to find a path for the UAV such that each target is visited at least once by the vehicle, the fuel constraint is never violated along the path for the UAV, and the total length of the path is a minimum. Mixed integer, linear programming formulations are proposed to solve the problem optimally. As solving these formulations to optimality may take a large amount of time, fast and efficient construction and improvement heuristics are developed to find good sub-optimal solutions to the problem. Simulation results are also presented to corroborate the performance of all the algorithms. In addition to the above contributions, this thesis develops an approximation algorithm for a multiple UAV routing problem with fuel constraints.", "keywords": [ "Motion planning", "Motion (physics)", "Resource (disambiguation)", "Computer science", "Geography", "Artificial intelligence", "Robot" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11694" }, { "id": "https://openalex.org/W7130320877", "doi": "10.1109/isssc66652.2025.11389076", "title": "Intelligent Multi-UAV Docking with Predictive Scheduling and Task Reallocation", "year": 2025, "publication_date": "2025-11-06", "type": "conference-paper", "language": null, "authors": [ "Dhanyashriee S", "Samson Arun Raj" ], "first_author": "Dhanyashriee S", "venue": null, "publisher": null, "abstract": "The growing use of multi-UAV fleets in domains such as surveillance, logistics, and environmental monitoring demands optimized docking operations to avoid congestion and coverage loss. This work proposes an AI-driven docking management framework combining predictive battery scheduling, cooperative task reallocation, and renewable-powered charging stations. The system forecasts time-to-empty using mission context, environmental data, and discharge history, allocating docking slots to maintain minimum active fleet size. A peer-to-peer protocol ensures seamless mission handover to the nearest capable UAV, while docking stations employ hybrid wireless charging and battery swapping powered mainly by solar energy. Simulations show a 30% reduction in docking conflicts, sustained mission coverage above 95%, and up to 22% higher energy efficiency compared to reactive strategies, enabling scalable, sustainable multi-UAV deployments.", "keywords": [ "Scheduling (production processes)", "Docking (animal)", "Handover", "Wireless", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/isssc66652.2025.11389076" }, { "id": "https://openalex.org/W4320714889", "doi": "10.21203/rs.3.rs-2470489/v1", "title": "An Novel Method for Solving Touring-polygons Problem with Obstacles", "year": 2023, "publication_date": "2023-02-15", "type": "preprint", "language": "en", "authors": [ "Wei Ma", "Xiaojing Liu", "Xiaohu Luo" ], "first_author": "Wei Ma", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Computer science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-2470489/latest.pdf" }, { "id": "https://openalex.org/W4408083116", "doi": "10.1007/978-981-96-2244-3_43", "title": "Multi-UAV Target Coverage Path Planning Using Attention Neural Networks", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Guodong Yang", "Shaofeng Chen", "Yang Cao" ], "first_author": "Guodong Yang", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Artificial neural network", "Path (computing)", "Motion planning", "Artificial intelligence", "Deep neural networks", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-96-2244-3_43" }, { "id": "https://openalex.org/W4301301899", "doi": "10.48550/arxiv.1702.08494", "title": "Multi-UAV Routing for Persistent Intelligence Surveillance & Reconnaissance Missions", "year": 2017, "publication_date": "2017-02-27", "type": "preprint", "language": "en", "authors": [ "Satyanarayana G. Manyam", "Rasmussen, Steven", "David W. Casbeer", "Krishna Kalyanam", "Suresh Manickam" ], "first_author": "Satyanarayana G. Manyam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider a Persistent Intelligence, Surveillance and Reconnaissance (PISR) routing problem, which includes collecting data from a set of specified task locations and delivering that data to a control station. Each task is assigned a refresh rate based on its priority, where higher priority tasks require higher refresh rates. The UAV team's objective is to minimize the maximum of the delivery times of all the tasks' data to the control station, while simultaneously, satisfying each task's revisit period constraint. The centralized path planning problem for this PISR routing problem is formulated using mixed integer linear programming and solved using a branch-and-cut algorithm. Heuristics are presented to find sub-optimal feasible solutions that require much less computation time. The algorithms are tested on several instances and their performance is compared with respect to the optimal cost and computation time.", "keywords": [ "Heuristics", "Computer science", "Task (project management)", "Routing (electronic design automation)", "Integer programming", "Real-time computing", "Set (abstract data type)", "Computation", "Control (management)", "Constraint (computer-aided design)", "Integer (computer science)", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.1702.08494" }, { "id": "https://openalex.org/W4405184938", "doi": "10.3390/s24237859", "title": "Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station", "year": 2024, "publication_date": "2024-12-09", "type": "article", "language": "en", "authors": [ "Ercan Erkalkan", "Vedat Topuz", "Ali Buldu" ], "first_author": "Ercan Erkalkan", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.", "keywords": [ "Computer science", "Robustness (evolution)", "Distributed computing", "Routing (electronic design automation)", "Heuristic", "Network topology", "Wireless ad hoc network", "Computer network", "Artificial intelligence", "Wireless", "Telecommunications" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/24/23/7859/pdf?version=1733735906" }, { "id": "https://openalex.org/W1241397470", "doi": "10.5120/21049-3684", "title": "Shortest Path Selection for UAVS using 3-D Coordinates with Collision Avoidance System", "year": 2015, "publication_date": "2015-06-18", "type": "article", "language": "en", "authors": [ "Shivani Dhiman", "Mandeep Singh" ], "first_author": "Shivani Dhiman", "venue": "International Journal of Computer Applications", "publisher": "Foundation of Computer Science", "abstract": "UAV (Unmanned Aerial Vehicle) also known as Drones and classified as Autonomous Aircraft and Remotely Piloted Aircraft.An UAV is an aircraft without having a human piloted aboard.It is usually used for the military and special operation application but also used in growing number of applications.UAV referred for the mission that are too deadly, unclean or the risky one.There are different names for the aircraft like UAS (Unpiloted Air System), UAV (Unpiloted Aerial Vehicle), RPAS (Remote Piloted Aircraft Systems) and model aircraft.In the existing solution there is a routing problem in UAV that has been addressed.The problem is referred to as the CCURP i.e. communication constrained UAV Routing Problem.To solve the CCURP problem, the shortest paths between targets are computed by mean of a graph transformation system.The major problem with the existing system is that, it is not capable to calculate the shortest path between the two points.Also, it is does not evaluate the points of collision and the existing solution is not compatible with the 3-D position of the UAVs.The existing routing algorithm and collision avoidance method does not evaluate the 3-D positions of the UAVs.The proposed algorithm will calculate the shortest path and helps to evaluate the 3-D position of the UAV.The point of collision will be clearly defined and avoided.The parameter used while observations are Delay, Accuracy, Probability of failures, and Throughput.", "keywords": [ "Computer science", "Shortest path problem", "Selection (genetic algorithm)", "Path (computing)", "Collision avoidance", "Collision", "Simulation", "Artificial intelligence", "Theoretical computer science", "Computer network", "Computer security", "Graph" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5120/21049-3684" }, { "id": "https://openalex.org/W4404942622", "doi": null, "title": "Leveraging Latent Dirichlet Allocation and Fuzzy Clustering for Identifying Key UAV Applications in Disaster Response", "year": 2024, "publication_date": "2024-08-22", "type": "article", "language": "en", "authors": [ "Zeynep Yüksel", "Nazmiye Eligüzel", "Süleyman Mete" ], "first_author": "Zeynep Yüksel", "venue": "DergiPark (Istanbul University)", "publisher": "Istanbul University", "abstract": "Over the past few decades, there has been a significant increase in the occurrence of natural disasters, such as earthquakes and landslides, presenting a grave risk to the safety of people's lives and their possessions. Drones, also known as unmanned aerial systems (UAVs), are increasingly attracting the attention of organizations engaged in disaster events, especially in the context of post-disaster emergency response. This research aims to assess the use of UAV applications in the post-disaster phase through a descriptive literature analysis. The evaluation is conducted using the Latent Dirichlet Allocation (LDA) topic modelling and clustering approach, namely the fuzzy c-means algorithm. A total of 433 papers are extracted from the Scopus database. The analysis offers valuable insights into three primary domains: imaging-based damage assessment, emergency communication networks, and vehicle routing optimization. These findings emphasize the significance of technology and streamlined systems in effectively handling complex situations, such as disaster response and network management. By integrating UAVs into disaster response strategies, policymakers can significantly enhance the agility and efficiency of their operations, ultimately saving lives and minimizing the impact of natural disasters on communities. This study can assist in achieving these goals by providing valuable insights and guidance.", "keywords": [ "Latent Dirichlet allocation", "Disaster response", "Key (lock)", "Cluster analysis", "Computer science", "Emergency response", "Fuzzy logic", "Emergency management", "Data mining", "Topic model", "Machine learning", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dergipark.org.tr/en/download/article-file/4149736" }, { "id": "https://openalex.org/W4281703789", "doi": "10.48550/arxiv.2205.12656", "title": "Optimizing UAV Recharge Scheduling for Heterogeneous and Persistent Aerial Service", "year": 2022, "publication_date": "2022-05-25", "type": "preprint", "language": "en", "authors": [ "Edgar Arribas", "Vicent Cholvi", "Vincenzo Mancuso" ], "first_author": "Edgar Arribas", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The adoption of UAVs in communication networks is becoming reality thanks to the deployment of advanced solutions for connecting UAVs and using them as communication relays. However, the use of UAVs introduces novel energy constraints and scheduling challenges in the dynamic management of network devices, due to the need to call back and recharge, or substitute, UAVs that run out of energy. In this paper, we design UAV recharging schemes under realistic assumptions on limited flight times and time consuming charging operations. Such schemes are designed to minimize the size of the fleet to be devoted to a persistent service of a set of aerial locations, hence its cost. We consider a fleet of homogeneous UAVs both under homogeneous and heterogeneous service topologies. For UAVs serving aerial locations with homogeneous distances to a recharge station, we design a simple scheduling, that we name HORR, which we prove to be feasible and optimal, in the sense that it uses the minimum possible number of UAVs to guarantee the coverage of the aerial service locations. For the case of non-evenly distributed aerial locations, we demonstrate that the problem becomes NP-hard, and design a lightweight recharging scheduling scheme, PHERR, that extends the operation of HORR to the heterogeneous case, leveraging the partitioning of the set of service locations. We show that PHERR is near-optimal because it approaches the performance limits identified through a lower bound that we formulate on the total fleet size.", "keywords": [ "Computer science", "Homogeneous", "Software deployment", "Scheduling (production processes)", "Network topology", "Distributed computing", "Real-time computing", "Computer network", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2205.12656" }, { "id": "https://openalex.org/W7131381864", "doi": "10.48550/arxiv.2602.19742", "title": "A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response", "year": 2026, "publication_date": "2026-02-23", "type": "preprint", "language": null, "authors": [ "Yulun Huang", "Zhiyu Wang", "Rajkumar Buyya" ], "first_author": "Yulun Huang", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Wildfire monitoring demands timely data collection and processing for early detection and rapid response. UAV-assisted edge computing is a promising approach, but jointly minimizing end-to-end service response time while satisfying energy, revisit time, and capacity constraints remains challenging. We propose an integrated framework that co-optimizes UAV route planning, fleet sizing, and edge service provisioning for wildfire monitoring. The framework combines fire-history-weighted clustering to prioritize high-risk areas, Quality of Service (QoS)-aware edge assignment balancing proximity and computational load, 2-opt route optimization with adaptive fleet sizing, and a dynamic emergency rerouting mechanism. The key insight is that these subproblems are interdependent: clustering decisions simultaneously shape patrol efficiency and edge workloads, while capacity constraints feed back into feasible configurations. Experiments show that the proposed framework reduces average response time by 70.6--84.2%, energy consumption by 73.8--88.4%, and fleet size by 26.7--42.1% compared to GA, PSO, and greedy baselines. The emergency mechanism responds within 233 seconds, well under the 300-second deadline, with negligible impact on normal operations.", "keywords": [ "Enhanced Data Rates for GSM Evolution", "Cluster analysis", "Provisioning", "Service (business)", "Response time", "Key (lock)", "Energy consumption", "Emergency response" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2602.19742" }, { "id": "https://openalex.org/W7131319572", "doi": "10.1109/icws72778.2026.00136", "title": "A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response", "year": 2026, "publication_date": "2026-07-01", "type": "conference-paper", "language": null, "authors": [ "Yulun Huang", "Zhiyu Wang", "Rajkumar Buyya" ], "first_author": "Yulun Huang", "venue": null, "publisher": null, "abstract": "Wildfire monitoring demands timely data collection and processing for early detection and rapid response. UAV-assisted edge computing is a promising approach, but jointly minimizing end-to-end service response time while satisfying energy, revisit time, and capacity constraints remains challenging. We propose an integrated framework that co-optimizes UAV route planning, fleet sizing, and edge service provisioning for wildfire monitoring. The framework combines fire-history-weighted clustering to prioritize high-risk areas, Quality of Service (QoS)-aware edge assignment balancing proximity and computational load, 2-opt route optimization with adaptive fleet sizing, and a dynamic emergency rerouting mechanism. The key insight is that these subproblems are interdependent: clustering decisions simultaneously shape patrol efficiency and edge workloads, while capacity constraints feed back into feasible configurations. Experiments show that the proposed framework reduces average response time by 70.6--84.2%, energy consumption by 73.8--88.4%, and fleet size by 26.7--42.1% compared to GA, PSO, and greedy baselines. The emergency mechanism responds within 233 seconds, well under the 300-second deadline, with negligible impact on normal operations.", "keywords": [ "Enhanced Data Rates for GSM Evolution", "Cluster analysis", "Provisioning", "Service (business)", "Response time", "Key (lock)", "Energy consumption", "Emergency response" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2602.19742" }, { "id": "https://openalex.org/W4365935229", "doi": "10.1007/978-3-031-29129-6_21", "title": "Fast Hierarchical Bi-criteria Multivehicle Flight Planner for UAV Surveillance Missions", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "José Reginaldo H. Carvalho", "Willians Cassiano de F. Abreu", "Anderson Gadelha Fontoura", "Antonio S. Rodrigues", "Carlos Edison Lopes", "Micila S. M. Pereira" ], "first_author": "José Reginaldo H. Carvalho", "venue": "Design science and innovation", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Disjoint sets", "Computer science", "Real-time computing", "Set (abstract data type)", "Collision avoidance", "Convex hull", "Routing (electronic design automation)", "Planner", "Regular polygon", "Collision", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-29129-6_21" }, { "id": "https://openalex.org/W7134166010", "doi": "10.1155/atr/8498460", "title": "Research on Path Optimization for UAV‐Vehicle Collaboration in Emergency Rescue", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Shouqiang Xue", "Weichuan Yin", "Yun Xiao", "Hongwu Wang", "Yufan Wei" ], "first_author": "Shouqiang Xue", "venue": "Journal of Advanced Transportation", "publisher": "Hindawi Publishing Corporation", "abstract": "With the rapid development of the low‐altitude economy, unmanned aerial vehicles (UAVs) have exhibited enormous potential in emergency rescue and material delivery fields due to their flexibility; however, individual UAVs are often constrained by endurance issues. This paper focuses on rescue scenarios in complex terrains such as mountainous forests and considers service distances, while designing a UAV‐Vehicle collaborative delivery model. It constructs a dual algorithm based on clustering optimization and large‐scale adaptive neighborhood search (ALNS‐Kmeans++), enabling rapid optimization of the problem. Using a benchmark dataset as a sample, the study compares Genetic Algorithms (GA), Tabu Search (TS), and Ant Colony Optimization (ACO). In terms of solution results (total distance cost), ALNS outperforms GA, TS, and ACO by 27.2%, 23.2%, and 23.4%, respectively. The ALNS‐Kmeans++ algorithm integrating the clustering strategy shows better comprehensive practicability. It achieves suboptimal solution result (total distance cost) with the smallest fleet size (7.0% worse than ALNS), but its computational speed is 5.5% faster than that of the ALNS algorithm. In the validation within a rescue case study within tropical rainforests in Yunnan, this delivery model achieves efficient collaboration among three rescue vehicles and six UAVs. Emergency rescue can achieve full coverage and cost reduction through the strategy of dynamic relocation of fewer vehicles and collaborative UAVs. By the clustering optimization and dynamic adjustment of the ALNS‐Kmeans++, the efficiency of rescue tasks can be enhanced. It provides a replicable solution for the scenario‐based implementation of the emergency rescue system.", "keywords": [ "Cluster analysis", "Search and rescue", "Ant colony optimization algorithms", "Benchmark (surveying)", "Genetic algorithm", "Tabu search", "Path (computing)", "Service (business)", "Emergency rescue" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1155/atr/8498460" }, { "id": "https://openalex.org/W7171068040", "doi": "10.2139/ssrn.7067179", "title": "Decentralized Multi-Agent Deep Reinforcement Learning for Autonomous UAV Relay Positioning in Infrastructure-Denied Environments", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Kasai Abdulmajid" ], "first_author": "Kasai Abdulmajid", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Relay", "Reinforcement learning", "Training (meteorology)", "Markov decision process", "Software deployment", "Partially observable Markov decision process", "Controller (irrigation)", "Drone", "Service (business)", "Markov chain" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7067179" }, { "id": "https://openalex.org/W7202170173", "doi": "10.1016/j.oceaneng.2026.127392", "title": "Cooperative multi-robot routing for AUV–UAV inspection with inter-vehicle synchronization", "year": 2026, "publication_date": "2026-08-11", "type": "article", "language": "en", "authors": [ "Alessandro Bucci", "Lorenzo Cecchi", "Alessandro Ridolfi" ], "first_author": "Alessandro Bucci", "venue": "Ocean Engineering", "publisher": "Elsevier BV", "abstract": "This paper addresses an extension of the classical heterogeneous Vehicle Routing Problem (VRP) tailored to cooperative missions involving Autonomous Underwater Vehicles (AUVs) and Unmanned Aerial Vehicles (UAVs) in monitoring scenarios characterized by heterogeneous task requirements and shoreline-avoidance constraints. The proposed formulation considers alternative coverage patterns for area and line inspection tasks, vehicle-type compatibility constraints, and temporal synchronization requirements between UAVs and AUVs. In particular, monitoring time windows are introduced to enable UAV supervision of AUV activities while ensuring coordinated mission execution. To solve this problem, a routing algorithm is developed and evaluated through two realistic case studies of increasing complexity. The approach integrates path-planning techniques to improve travel-time estimation and mission coordination. Its performance is assessed through comparisons with a Greedy Nearest Task baseline, considering mission makespan, task feasibility, workload balance, computational time, and robustness to positioning uncertainty. The results show that the proposed method reduces mission makespan by 21.26%, improves task feasibility, and enhances workload balance compared with the baseline approach. Furthermore, the synchronization mechanism enables effective coordination between heterogeneous vehicles while maintaining feasible mission schedules. High-quality solutions are obtained with computational times of up to 22 s, demonstrating that the proposed framework can address cooperative inspection missions with manageable computational effort.", "keywords": [ "Workload", "Robustness (evolution)", "Synchronization (alternating current)", "Time synchronization", "Scheduling (production processes)", "Job shop scheduling", "Task (project management)", "Baseline (sea)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.oceaneng.2026.127392" }, { "id": "https://openalex.org/W4414970173", "doi": "10.70322/dav.2025.10016", "title": "Integrated Consensus Framework for Task Assignment and Path Planning of a Degraded UAV Fleet", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "T. Barnawi Waleed", "M. Nicholls Gillian", "D. McAllister Charles", "Y. Ortiz Alisha" ], "first_author": "T. Barnawi Waleed", "venue": "Drones and autonomous vehicles/Drones and Autonomous Vehicles", "publisher": null, "abstract": "Unmanned aerial vehicle (UAV) systems can fail during civil and military operations. This presents a significant challenge for human teleoperators (remote pilots) in determining task reallocation after member loss within the fleet. To alleviate the high cognitive load on teleoperators in critical situations, a decentralized strategy was developed to resolve the combined task assignment and vehicle routing problems. This Integrated Consensus Framework (ICF) not only solves the combined problem but also adds a unique ability to identify the loss of a vehicle and dynamically reroute agents to abandoned tasks to achieve a satisfactory solution. ICF is a two-tiered approach that combines a novel algorithm, the Caravan Auction (CarA) algorithm, with a path-planning strategy to identify when UAVs are lost and reallocate orphaned tasks. The CarA Algorithm consists of three phases: auction, consensus, and validation phases. An experiment using Monte Carlo simulations was conducted to determine the performance of ICF. Teleoperators assigned to complete multiple tasks with UAVs in dangerous environments can allow the proposed system to perform task assignments and reallocation while offering only supervisory control as needed. The results indicate this novel approach provides comparable performance to existing strategies, doing so with the addition of randomized UAV loss.", "keywords": [ "Task (project management)", "Motion planning", "Control (management)", "Robot", "Task analysis", "Supervisory control", "Vehicle dynamics", "Path (computing)", "Monte Carlo method" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciepublish.com/index/article/view_full_text/id/699" }, { "id": "https://openalex.org/W3184649165", "doi": "10.1145/3459955.3460612", "title": "UAV based data communication using Wireless Sensor Networks", "year": 2021, "publication_date": "2021-03-17", "type": "conference-paper", "language": "en", "authors": [ "Krishna Murthy Surya Narayanan", "Maher Elshakankiri" ], "first_author": "Krishna Murthy Surya Narayanan", "venue": null, "publisher": null, "abstract": "This paper studies various data collection and transmission technologies and the architectures concerning Wireless Sensor Networks (WSN) with the aid of Unmanned Aerial Vehicles (UAV). The basic communication methodologies between the sensors and the sensor to the UAVs and UAVs to the destination are studied in detail. The architectures that can be used to perform these operations seamlessly and efficiently are explored. The concept of maximizing the efficiency of UAV and WSN by considering numerous impacting factors is studied and a throughput maximization strategy is explored. Different UAV routing mechanisms to decrease the loss in packet data during transmission are considered and an extensive study on this topic is presented. The energy consumption and overall efficiency of the UAV based communication model have been studied in a comparative perspective weighing down the pros and cons of one over the other. An opportunistic communication algorithm gives a more dynamic outlook to the UAV routing problem and also enhances the efficiency of the overall system. Finally, the identified problems in the previous related work are listed and an alternative solution is proposed.", "keywords": [ "Computer science", "Wireless sensor network", "Energy consumption", "Throughput", "Network packet", "Routing (electronic design automation)", "Computer network", "Efficient energy use", "Transmission (telecommunications)", "Wireless", "Maximization", "Real-time computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3459955.3460612" }, { "id": "https://openalex.org/W3187124884", "doi": "10.1109/icc42927.2021.9500856", "title": "Optimum Routing and Slot Formatting in UAV-Assisted 5G Networks", "year": 2021, "publication_date": "2021-06-01", "type": "conference-paper", "language": "en", "authors": [ "Ahmed A. Elbery", "Hossam S. Hassanein", "Hatem Abou-Zeid", "Akram Bin Sediq", "Gary Boudreau" ], "first_author": "Ahmed A. Elbery", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAV) are expected to play a crucial role in the future of 5G and beyond. However, designing efficient routing protocols for UAV is challenging due to the mobility and energy constraints. This problem becomes harder in UAV-assisted 5G networks because of its impact on the time slot assignment for the uplink and downlink in Time Division Duplex (TDD) frame structure. Thus, in this paper, we propose a new optimum routing technique for UAV-assisted TTD 5G networks, the Optimized Load-Balancing Routing (OLBR). The optimum routing problem is formulated in such a way that the decision variables are used to compute the time slot assignment in the 5G connection between UAV nodes. The objective of the optimization model is to minimize the network-wide delay. By distributing traffic across different alternative routes, OLBR minimizes network congestion, resulting in shorter queuing delays. Such a load-balancing also decreases the possibility of node failure due to energy depletion. The proposed OLBR is compared to the shortest path routing using Monte Carlo simulation on two different network topologies at different network traffic loads. The simulation results show that the OLBR produces significant savings in network-wide packet delay compared to the shortest path.", "keywords": [ "Computer science", "Computer network", "Network packet", "Network topology", "Distributed computing", "Link-state routing protocol", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icc42927.2021.9500856" }, { "id": "https://openalex.org/W4287667648", "doi": "10.48550/arxiv.2009.06838", "title": "Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time\\n Deadlines", "year": 2020, "publication_date": "2020-09-14", "type": "preprint", "language": null, "authors": [ "Oussama Ghdiri", "Wael Jaafar", "Safwan Alfattani", "Jihene Ben Abderrazak", "Halim Yanıkömeroğlu" ], "first_author": "Oussama Ghdiri", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In this paper, we focus on energy-efficient UAV-based IoT data collection in\\nsensor networks in which the sensed data have different time deadlines. In the\\ninvestigated setting, the sensors are clustered and managed by cluster heads\\n(CHs), and multiple UAVs are used to collect data from the CHs. The formulated\\nproblem is solved through a two-step approach. In the first step, an efficient\\nmethod is proposed to determine the minimal number of CHs and their best\\nlocations. Subsequently, the minimal number of UAVs and their trajectories are\\nobtained by solving the associated capacitated vehicle routing problem. Results\\nshow the efficiency of our proposed CHs placement method compared to baseline\\napproaches, where bringing the CHs closer to the dockstation allows significant\\nenergy savings. Moreover, among different UAV trajectory planning algorithms,\\nTabu search achieves the best energy consumption. Finally, the impact of the\\nbattery capacity and time deadline are investigated in terms of consumed\\nenergy, number of visited CHs, and number of deployed UAVs.\\n", "keywords": [ "Computer science", "Energy consumption", "Real-time computing", "Tabu search", "Data collection", "Routing (electronic design automation)", "Efficient energy use", "Energy (signal processing)", "Wireless sensor network", "Distributed computing", "Mathematical optimization", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2009.06838" }, { "id": "https://openalex.org/W7171845877", "doi": "10.4050/sm_2016_cs-1284", "title": "A Model-based System of Systems Engineering Distributed Package Delivery Discrete Event Simulation for Optimization of Small UAVs", "year": 2016, "publication_date": "2016-02-09", "type": "conference-paper", "language": null, "authors": [ "Benjamin England", "Atipat Wattanuntachai", "Christopher Duffy", "Daniel Schrage" ], "first_author": "Benjamin England", "venue": null, "publisher": null, "abstract": "The idea of day-of drone deliveries has intrigued logistics providers for its potential to radically shift the consumer goods market and provide a method to quickly deliver small but urgently needed supplies. The use of aerial vehicles for delivery logistics provides a significant improvement in efficiency, while avoiding the uncertainty of road traffic. In order to effectively cover a wide operating area with a limited number of vehicles, logistics operators have employed detailed vehicle and route planning methods. With the incorporation of air vehicles into the framework it becomes necessary to incorporate a detailed and flexible fleet planning, management and optimization method into the vehicle sizing routine. Without this step, which provides specific quantitative results about the relative success or failure of the system, any vehicle and fleet size planning would have the potential to be improperly sized, resulting in either unhappy customers and/or a flawed business model. This paper will present the framework of an optimizing solution approach for the simultaneous sizing of the vehicle and fleet for an air delivery logistics system of systems and the results from its application to a specific business case.", "keywords": [ "Sizing", "Drone", "Fleet management", "Cover (algebra)", "Route planning", "Order (exchange)", "Event (particle physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4050/sm_2016_cs-1284" }, { "id": "https://openalex.org/W4240019658", "doi": "10.1504/ijsnet.2020.109714", "title": "Linear models for total coverage problem with connectivity constraints using multiple unmanned aerial vehicles", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Amani Lamine", "Fethi Mguis", "Hichem Snoussi", "Khaled Ghédira" ], "first_author": "Amani Lamine", "venue": "International Journal of Sensor Networks", "publisher": "Inderscience Publishers", "abstract": "The use of unmanned aerial vehicles (UAVs) has recently increased both in civilian and military operations, and the planning of their routes is critical. This research investigates a routing problem in which a UAV network, equipped with sensors, covers a given area and maintains connectivity with its neighbouring UAVs and the base station, while respecting to the UAVs lifetime. To cover the area, two integer linear programming models are formulated to solve two problems optimally. In the first one, covering means that all positions should be visited. However, in the second one, covering means that every position should be covered at least by one UAV. Due to the limited communication radius of the UAVs, connectivity then has to find inter-UAVs routing paths to satisfy the communication between UAVs and the base. We verify by experiments that the models, using Cplex, can provide an optimal solution of different area dimensions.", "keywords": [ "Computer science", "Integer programming", "Cover (algebra)", "Routing (electronic design automation)", "Linear programming", "Base station", "Position (finance)", "Wireless sensor network", "Real-time computing", "Computer network", "Algorithm" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijsnet.2020.109714" }, { "id": "https://openalex.org/W2956043985", "doi": "10.1007/978-3-030-23756-1_149", "title": "Intelligent Systems for Disaster Management: Unmanned Aerial Vehicles’ Surveillance Problem with Energy Consumption Constraints", "year": 2019, "publication_date": "2019-07-05", "type": "conference-paper", "language": "en", "authors": [ "Ebru Angün", "Uğurcan Dündar" ], "first_author": "Ebru Angün", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Swap (finance)", "Computer science", "Energy consumption", "Sink (geography)", "Operations research", "Routing (electronic design automation)", "Computer network", "Engineering", "Business", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-23756-1_149" }, { "id": "https://openalex.org/W4285246473", "doi": "10.2139/ssrn.4120103", "title": "A Uav Patrolling Path Problem Considering Distributed Recharging Facilities and Control Signal", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Yang Gao", "Chenhao Zhou", "Peilu Gan", "Heng-Qing Ye", "Zhili Zhou" ], "first_author": "Yang Gao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Patrolling", "Path (computing)", "SIGNAL (programming language)", "Control (management)", "Control signal", "Computer science", "Computer security", "Telecommunications", "Computer network", "Political science", "Artificial intelligence", "Law" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4120103" }, { "id": "https://openalex.org/W3153669328", "doi": null, "title": "Sensing in Airspace for Sequential O-D Aircraft Routing", "year": 2021, "publication_date": "2021-02-19", "type": "preprint", "language": "en", "authors": [ "Jing Bo Gao", "Ankur Mani", "Lavanya Marla" ], "first_author": "Jing Bo Gao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": "Aircraft in the National Aviation System (NAS) often rely on wind information from the National Oceanic and Atmospheric Administration (NOAA) to calculate favorable paths. However, wind conditions are dynamic and the NOAA information becomes quickly outdated because it is based upon sparse sampling both in space and time. This leads to inefficient, slower, paths used in practice. A goal of the Federal Aviation Administration’s (FAA) NextGen program is to use dynamic information to reduce inefficiencies. One such way to obtain high quality dynamic information and reduce inefficiency is to use en-route aircraft as ‘sensors’. This raises a natural question, “if a fraction of the aircraft can be used for sampling, how should aircraft be routed to provide most useful information for other aircraft to minimize system costs?” To answer this question, we begin with a stylized model of the aircraft routing problem, and capture the uniquely spatial and temporal correlations in wind dynamics. This allows us to model spatial and temporal correlation between the travel time along different paths, and formulate the travel time as a Brownian surface. Under this uncertainty structure, we answer two questions: (i) if an offline schedule of paths to be sampled is desired, what is the optimal sampling schedule? and (ii) if the paths to be sampled are to be chosen in real time according to flight schedules, what is a near-optimal sampling policy? We provide answers to these questions with provable guarantees. We also generate a comprehensive testbed from real-world flight data and computationally evaluate the performance of our sampling policies. Our testbed consists of seventeen origin-destination airport pairs, with five short haul, seven medium haul and five long-haul pairs. Our results show that collecting the right information and utilizing it to plan future aircraft routes could reduce a flight's travel time and associated fuel burn by 5$\\%$ on average. Our modeling framework and results are also applicable to smaller, intra-city aircraft and unmanned aircraft such as UAVs and drones.", "keywords": [ "Computer science", "Sampling (signal processing)", "Aviation", "Schedule", "Stylized fact", "National Airspace System", "Flight planning", "Operations research", "Real-time computing", "Aerospace engineering", "Engineering", "Telecommunications" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3788984" }, { "id": "https://openalex.org/W7133233108", "doi": "10.1109/jsac.2026.3669481", "title": "Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Ziye Jia", "Sijie He", "Ligang Yuan", "Fuhui Zhou", "Quan Wu", "Zhu Han", "Dusit Tao Niyato" ], "first_author": "Ziye Jia", "venue": "IEEE Journal on Selected Areas in Communications", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.", "keywords": [ "Routing (electronic design automation)", "Scalability", "Static routing", "Policy-based routing", "Dynamic Source Routing", "Link-state routing protocol", "Network topology", "Markov decision process", "Routing protocol" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jsac.2026.3669481" }, { "id": "https://openalex.org/W7172401664", "doi": "10.5753/sbccq.2026.22455", "title": "Applying the Quantum Approximate Optimization Algorithm to the UAV Collision Avoidance Problem", "year": 2026, "publication_date": "2026-07-19", "type": "conference-paper", "language": null, "authors": [ "Kalleb C. Santos", "Maria Eduarda W. M. Vianna", "Mateus K. de Oliveira", "Evandro C. R. Rosa", "Alison Roberto Panisson", "Luiz F. Bittencourt", "Roberto Rodrigues Filho" ], "first_author": "Kalleb C. Santos", "venue": null, "publisher": null, "abstract": "The increasing availability of quantum hardware has stimulated interest in applying quantum algorithms to combinatorial optimization problems. Collision avoidance for unmanned aerial vehicles (UAVs) is one such challenge due to the computational complexity of coordinating multiple agents in shared airspace. In this paper, we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model that captures route selection and collision constraints, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA). To improve efficiency, we introduce a constrained QAOA Ansatz employing an XY-mixer that preserves one-hot route assignment constraints and restricts the search space to valid solutions. We evaluate the approach through noiseless simulations and device-based noise models, analyzing its behavior under realistic conditions and highlighting the opportunities and limitations of near-term quantum hardware for multi-agent routing problems.", "keywords": [ "Collision avoidance", "Quantum", "Quantum computer", "Routing (electronic design automation)", "Optimization problem", "Computational complexity theory", "Quadratic equation", "Noise (video)", "Binary number" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://sol.sbc.org.br/index.php/sbccq/article/download/43259/43025" }, { "id": "https://openalex.org/W4400163189", "doi": "10.48550/arxiv.2406.19001", "title": "Optimal routing and transmission strategies for UAV reconnaissance missions with detection threats", "year": 2024, "publication_date": "2024-06-27", "type": "preprint", "language": "en", "authors": [ "Riley Badenbroek", "Relinde Jurrius", "Lander Verlinde" ], "first_author": "Riley Badenbroek", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider an autonomous reconnaissance mission where an Unmanned Aerial Vehicle (UAV) has to visit several points of interest and communicate the intel back to the base. At every point of interest, the UAV has the option to either send back all available info, or continue to the next point of interest and communicate at a later stage. Both choices have a chance of detection, meaning the mission fails. We wish to maximize the expected amount of information gathered by the mission. This is modelled by a routing problem in a weighted graph. We show that the problem is NP-complete, discuss an ILP formulation, and use a genetic algorithm to find good solutions for up to ten points of interest.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Computer network", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2406.19001" }, { "id": "https://openalex.org/W4410079179", "doi": "10.30837/2522-9818.2025.1.049", "title": "Development of real time data processing algorithms for UAV traffic optimization", "year": 2025, "publication_date": "2025-03-31", "type": "article", "language": "en", "authors": [ "Maksym Yena" ], "first_author": "Maksym Yena", "venue": "Innovative technologies and scientific solutions for industries", "publisher": "Kharkiv National University of Radioelectronics", "abstract": "Subject matter: UAV traffic management processes, including algorithms for processing large data streams in real time to ensure safety, efficiency, and optimal flight routing. Goal: To development and implementation of real-time data processing algorithms to ensure safe, efficient and automated UAV traffic management in urban and rural environments. Tasks: To analyze existing approaches to UAV traffic management and real-time data processing technologies; to develop a mathematical model that takes into account the specifics of UAV routing, including collision avoidance and route optimization; to create an algorithm for processing input data in real time that integrates dynamic traffic changes, weather conditions, and airspace conditions; to implement and test the proposed algorithm in a simulation environment; to conduct a comparative analysis of UAV simulations with and without the proposed algorithm. Methods: To apply nonlinear optimization methods to construct routes that minimize energy consumption, flight time, and collision risk; to use graph-theoretic models to represent airspace as a network with nodes (route points) and edges (potential trajectories), which allows for effective solution of routing problems; to use genetic algorithms to find optimal solutions in complex multi-factor routing problems; to process data based on Kalman filters; creation of virtual copies of the airspace for conducting experiments and evaluating the effectiveness of algorithms in a safe environment. Results: The developed nonlinear optimization algorithms allowed to minimize the energy consumption for UAV flights and the time of task execution; the effectiveness of the approach was confirmed by testing, which showed a reduction in energy consumption and a decrease in the average flight time compared to the basic algorithms; a graph-theoretical model of the airspace was built, which allows to visualize and analyze possible routes; filtering algorithms showed high accuracy in predicting the position of the UAV even under conditions of instability of GPS signals. The introduction of Kalman filters allowed to reduce the error in determining the position of the UAV, which is critical for avoiding collisions. Conclusions: The developed methods ensure safe airspace management and significantly reduce the risks of UAV collisions, which makes them promising for integration into urban and regional management systems; The use of optimization, clustering and evolutionary algorithms allows to improve routing, reduce energy consumption and task execution time.", "keywords": [ "Computer science", "Optimization algorithm", "Algorithm", "Real-time computing", "Data processing", "Mathematical optimization", "Mathematics", "Operating system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journals.uran.ua/itssi/article/download/328659/318479" }, { "id": "https://openalex.org/W4409865829", "doi": "10.1007/s00521-026-12366-8", "title": "Collision-aware cooperative multi-UAV path planning with hierarchical PPO-LSTM", "year": 2026, "publication_date": "2026-08-01", "type": "article", "language": "en", "authors": [ "Alparslan Güzey" ], "first_author": "Alparslan Güzey", "venue": "Neural Computing and Applications", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Collision", "Artificial intelligence", "Distributed computing", "Real-time computing", "Computer security" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-6490892/latest.pdf" }, { "id": "https://openalex.org/W4283723659", "doi": "10.1117/12.2639642", "title": "Research on UAV Ad Hoc network routing based on optimization algorithm", "year": 2022, "publication_date": "2022-06-30", "type": "conference-paper", "language": "en", "authors": [ "Wensheng Chi", "Wenjun Xie", "Peng Zhang", "Wenxuan Lin", "Le Ru" ], "first_author": "Wensheng Chi", "venue": "International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022)", "publisher": null, "abstract": "One of the urgent problems to be solved in UAV cluster warfare is to find a suitable routing algorithm to adapt to the characteristics of UAV Ad Hoc network topology. Based on the further development of cellular network, the wireless Ad Hoc network does not need preset infrastructure, no control center, and can be temporary networked, which is more in line with the needs of unmanned aerial vehicle Ad Hoc network. In consideration of the premise of distributed deployment of unmanned aerial vehicle (UAV), matlab is used for simulation of opportunistic routing algorithm. As for multiple constraints Qos routing problem, tabu search algorithm is adopted to optimize the opportunistic routing algorithm. The experimental results show that, after intelligent algorithm is optimized, along with the increase of the time consumed for algorithm, other constraints impose effective impact on the algorithm, enabling the opportunistic routing algorithm make the optimal choice according to multiple constraints. Then genetic algorithm is used for comparison, and the conclusion that genetic algorithm can get faster convergence is drawn.", "keywords": [ "Computer science", "Wireless ad hoc network", "Destination-Sequenced Distance Vector routing", "Computer network", "Adaptive quality of service multi-hop routing", "Distributed computing", "Wireless Routing Protocol", "Routing (electronic design automation)", "Optimized Link State Routing Protocol", "Tabu search", "Algorithm", "Static routing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.2639642" }, { "id": "https://openalex.org/W4402167622", "doi": "10.1109/icocn63276.2024.10648560", "title": "A Multi-Agent Routing Algorithm with Trajectory Prediction for Highly Dynamic UAV Networks", "year": 2024, "publication_date": "2024-07-26", "type": "conference-paper", "language": "en", "authors": [ "Ziyi Zhao", "Qi Zhang", "Yuanfeng Li", "Xiangjun Xin", "Jianxin Ma", "Meng Sun", "Yi Zhao", "Furong Chai", "Feng Chun Tian", "Fu Wang", "Qinghua Tian", "Yongjun Wang" ], "first_author": "Ziyi Zhao", "venue": null, "publisher": null, "abstract": "To address the routing problems in high-dynamic UAV networks, a multi-agent routing algorithm with trajectory prediction is proposed. Simulations show the proposed algorithm can achieve better performance on the end-to-end delay and packet delivery ratio.", "keywords": [ "Computer science", "Trajectory", "Adaptive routing", "Routing (electronic design automation)", "Routing algorithm", "Algorithm", "Distributed computing", "Static routing", "Computer network", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icocn63276.2024.10648560" }, { "id": "https://openalex.org/W2967423409", "doi": "10.1115/1.4042669", "title": "Unmanned Aerial Vehicle Surveillance Under Visibility and Dwell-Time Constraints", "year": 2019, "publication_date": "2019-01-31", "type": "article", "language": "en", "authors": [ "Jeffrey R. Peters", "Amit Surana", "Grant S. Taylor", "Terry S. Turpin", "Francesco Bullo" ], "first_author": "Jeffrey R. Peters", "venue": "Journal of Dynamic Systems Measurement and Control", "publisher": "ASM International", "abstract": "A framework is introduced for planning unmanned aerial vehicle (UAV) flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, the framework is designed for instances in which each target is associated with a set of imaging parameters, including a desired: (i) tilt angle, (ii) azimuth, with the option of a 360 deg view, and (iii) dwell-time. Tours are sought to image the targets, while minimizing both the total mission time and the time required to reach the initial target. An ϵ-constraint scalarization is used to pose the multi-objective problem as a constrained optimization, which, through careful discretization, can be approximated as a discrete graph-search. It is shown that, in many cases, this approximation is equivalent to a generalized traveling salesperson problem (GTSP). A heuristic procedure for solving the discrete approximation and recovering solutions to the full routing problem is presented and illustrated through numerical studies.", "keywords": [ "Visibility", "Dwell time", "Discretization", "Heuristic", "Computer science", "Set (abstract data type)", "Mathematical optimization", "Constraint (computer-aided design)", "Azimuth", "Graph", "Visibility graph", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1908.05347" }, { "id": "https://openalex.org/W4415970949", "doi": "10.1109/tccn.2025.3630060", "title": "A Reinforcement Learning Method for UAV Delivery Scheduling Under Dynamic Pricing", "year": 2025, "publication_date": "2025-11-06", "type": "article", "language": "en", "authors": [ "Ziyi Hu", "Yue Cao", "Kai Yong Jiang", "Minho Jo", "Lixia Xiao", "Xingyu Zhao" ], "first_author": "Ziyi Hu", "venue": "IEEE Transactions on Cognitive Communications and Networking", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The on-demand delivery service of Unmanned Aerial Vehicles (UAVs) has attracted attention and research worldwide. Under the on-demand delivery model, the UAV transports parcels between ports (e.g., a designated location for UAV to drop off/pick up parcels), to satisfy the rapid response to urgent demand. Due to the complexity of large-scale routing problems and response time limitation, the general heuristic algorithms may obtain locally optimal scheduling solutions, thus reducing the delivery utility. Therefore, we propose a UAV route scheduling scheme based on a two-step hybrid heuristic algorithm. In particular, the biogeography-based optimization algorithm is applied to obtain the optimal set of UAV tasks at the first step. At the second step, the approximate dynamic programming is proposed to schedule task routes, to obtain the global optimal solution. Besides, the price function based on delivery status and demand characteristics is trained through the advantage actor critic algorithm to determine the optimal delivery price, so that the flexibility of UAV scheduling is increased. Based on simulation experiments, our scheme outperforms other solutions on increasing delivery utility, and can provide the optimal delivery price based on customer characteristics.", "keywords": [ "Reinforcement learning", "Scheduling (production processes)", "Job shop scheduling", "Schedule", "Dynamic priority scheduling", "Dynamic programming", "Dynamic pricing", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tccn.2025.3630060" }, { "id": "https://openalex.org/W4287375740", "doi": "10.48550/arxiv.2101.06602", "title": "OPAR: Optimized Predictive and Adaptive Routing for Cooperative UAV\\n Networks", "year": 2021, "publication_date": "2021-01-17", "type": "preprint", "language": "en", "authors": [ "M. Gharib", "Fatemeh Afghah", "Elizabeth Serena Bentley" ], "first_author": "M. Gharib", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Cooperative UAV networks are becoming increasingly popular in military and\\ncivilian applications. Alas, the typical ad-hoc routing protocols, which aim at\\nfinding the shortest path, lead to significant performance degradation because\\nof the 3-dimension highly-dynamic nature of UAV networks and the uneven\\ndistribution of nodes across the network. This paper proposes OPAR, an\\noptimized predictive and adaptive routing protocol, to face this challenging\\nproblem. We model the routing problem with linear programming (LP), where the\\ngoal is to maximize network performance, considering the path lifetime and\\npath-length together. This model relies on a precise link lifetime prediction\\nmechanism. We support the LP problem with a lightweight algorithm to find the\\noptimized solution with a computation complexity of $O(|E|^2)$, where $|E|$ is\\nthe number of network links. We evaluate the OPAR performance and compare it\\nwith the well-known routing algorithms AODV, DSDV, and OLSR to cover a wide\\nrange of proactive and reactive protocols as well as distance vector and\\nlink-state techniques. We performed extensive simulations for different network\\ndensities and mobility patterns using the ns-3 simulator. Results show that\\nOPAR prevents a high volume of routing traffic, increases the successful\\ndelivery by more than $30\\\\%$, improves the throughput $25\\\\%$ on average, and\\ndecreases the flow completion time by an average of $35\\\\%$.\\n", "keywords": [ "Computer science", "Destination-Sequenced Distance Vector routing", "Link-state routing protocol", "Distance-vector routing protocol", "Computer network", "Routing protocol", "Throughput", "Distributed computing", "Routing (electronic design automation)", "Wireless" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.06602" }, { "id": "https://openalex.org/W7140284928", "doi": "10.1109/tie.2026.3670285", "title": "Dual-Stage Coordinated Landing Control for Cross-Domain UAV-USV Fleets", "year": 2026, "publication_date": "2026-03-25", "type": "article", "language": "en", "authors": [ "Jianing Ding", "H. Zhang", "Bin-Bin Hu", "Bin Liu", "Jiajie Huang", "Yunfei Zhang", "Hesheng Wang" ], "first_author": "Jianing Ding", "venue": "IEEE Transactions on Industrial Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This article addresses the challenging issue of coordinating multiple unmanned aerial vehicles (UAVs) to land on appropriate unmanned surface vehicles (USVs) in a distributed cross-domain setting. Although substantial progresses have been made for single UAV-single USV coordination, achieving seamless experimental cooperative landing of multiple UAVs on multiple USVs remains a dilemma. To this end, we establish a dual-stage coordinated landing control framework. In the first stage, a consensus-based auction algorithm (CBAA) is proposed together with a constrained minimum-energy task-execution strategy to enable pairwise UAV-USV matching and tracking. In the second stage, a visual navigation-based landing controller ensures smooth UAV landings onto designated USVs. Afterwards, sufficient conditions are derived to guarantee the finite-time stability of the associated closed-loop systems. Finally, both the effectiveness and superiority of the proposed scheme are validated through real lake-based experiments conducted on a self-established platform consisting of three DJI M100 UAVs and three HUSTER-37 catamaran USVs, along with simulations demonstrating its robustness to increased fleet sizes.", "keywords": [ "Control (management)", "Control system", "Vehicle dynamics", "Trajectory", "Control theory (sociology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.rug.nl/en/publications/87854c8e-7501-40a1-828a-1390cf75fbd0" }, { "id": "https://openalex.org/W4383424487", "doi": "10.2139/ssrn.4502412", "title": "Optimal Routing and Aborting Planning of Uav-Truck with Multi-Source Random Shocks", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Rui Qi Yan", "Qixin Zhang", "Xiaohui He", "Fanping Wei", "Yang Li" ], "first_author": "Rui Qi Yan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Truck", "Computer science", "Routing (electronic design automation)", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4502412" }, { "id": "https://openalex.org/W4385240299", "doi": "10.1002/net.22171", "title": "The single robot line coverage problem: Theory, algorithms, and experiments", "year": 2023, "publication_date": "2023-07-25", "type": "article", "language": "en", "authors": [ "Saurav Kumar Agarwal", "Srinivas Akella" ], "first_author": "Saurav Kumar Agarwal", "venue": "Networks", "publisher": "Wiley", "abstract": "Abstract Line coverage is the task of servicing a given set of one‐dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of therequired graph, that is, the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3 s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs on the UNC Charlotte campus road network.", "keywords": [ "Heuristics", "Correctness", "Computer science", "Algorithm", "Robot", "Linear programming", "Arc routing", "Integer programming", "Graph", "Mathematical optimization", "Routing (electronic design automation)", "Theoretical computer science" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.22171" }, { "id": "https://openalex.org/W7127050785", "doi": "10.1117/12.3101626", "title": "Research on vehicle-UAV collaborative delivery path optimization based on genetic algorithm", "year": 2026, "publication_date": "2026-02-01", "type": "conference-paper", "language": null, "authors": [ "Rui Cheng", "Xu Gao", "Yunjian Bi", "Lifan Li", "Tao Wang" ], "first_author": "Rui Cheng", "venue": null, "publisher": null, "abstract": "To efficiently address the problem of poor accessibility of remote nodes in the “last-mile” stage of logistics distribution, this paper proposes a vehicle-UAV collaborative delivery path optimization model. Under constraints such as the UAV’s maximum flight range, launch and recovery locations, and single service per customer, the model aims to minimize the total travel distance and is formulated as a mixed-integer programming problem. To handle the problem’s complexity, a genetic algorithm integrating dynamic task allocation and local search is designed. The algorithm optimizes vehicle routes through customized encoding and genetic operators, then employs clustering analysis to dynamically plan UAV tasks and assign appropriate launch and recovery points. Finally, a 2-opt local search is introduced to refine the routes. Experimental results on a simulated logistics network demonstrate that the proposed method can consistently generate high-quality collaborative delivery schemes, effectively balancing the utilization of vehicles and UAVs. Compared with the pure vehicle delivery mode, this approach significantly reduces total operating costs and improves delivery efficiency, validating the feasibility and application potential of the proposed model and algorithm in intelligent logistics systems.", "keywords": [ "Genetic algorithm", "Cluster analysis", "Task (project management)", "Path (computing)", "Encoding (memory)", "Key (lock)", "Service (business)", "Vehicle routing problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3101626" }, { "id": "https://openalex.org/W4411621316", "doi": "10.1007/978-981-96-8731-2_41", "title": "Energy-Constrained Joint Path Planning for Vehicle-UAVs in Task Point Coverage", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Yunxiao Cai", "Chunyan Liu", "Shengyan Cai" ], "first_author": "Yunxiao Cai", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Motion planning", "Joint (building)", "Task (project management)", "Point (geometry)", "Path (computing)", "Energy (signal processing)", "Real-time computing", "Operations research", "Artificial intelligence", "Robot", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-96-8731-2_41" }, { "id": "https://openalex.org/W7117326118", "doi": "10.3390/drones10010010", "title": "A Chimpanzee Troop-Inspired Algorithm for Multiple Unmanned Aerial Vehicles on Patrolling Missions", "year": 2025, "publication_date": "2025-12-25", "type": "article", "language": "en", "authors": [ "Ebtesam Aloboud", "Heba A. Kurdi" ], "first_author": "Ebtesam Aloboud", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Persistent patrolling with multiple Unmanned Aerial Vehicles (UAVs) remains challenging due to dynamic surveillance priorities, heterogeneous node importance, and evolving operational constraints. We present the novel Chimpanzee Troop Algorithm for Patrolling (CTAP), a decentralized policy inspired by chimpanzees fission–fusion dynamics and territorial behavior. CTAP provides three capabilities: (i) on-the-fly patrol-group instantiation, (ii) importance-aware territorial partitioning of the patrol graph, and (iii) adaptive boundary expansion via a lightweight shared-memory overlay that coordinates neighboring groups without centralization. Unlike the Ant Colony Optimization (ACO), Heuristic Pathfinder Conscientious Cognitive (HPCC), Recurrent LSTM Path-Maker (RLPM), State-Exchange Bayesian Strategy (SEBS), and Dynamic Task Assignment via Auctions (DTAP) baselines, CTAP couples local-idleness reduction with controlled edge-exploration, yielding stable coverage under shifting demand. We evaluate these approaches across multiple maps and fleet sizes using the average weighted idleness, global worst-weighted idleness, and Time-Normalized Idleness metrics. CTAP reduces the average weighted idleness by 7% to 22% and the global worst-weighted idleness by 30–65% relative to the strongest competitor and attains the lowest Time-Normalized Idleness in every configuration. These results show that a simple, communication-limited, partition-based policy enables robust, scalable patrolling suitable for resource-constrained UAV teams in smart-city environments.", "keywords": [ "Patrolling", "Task (project management)", "Heuristic", "Scalability", "Contiguity", "Node (physics)", "Drone", "Ant colony", "Path (computing)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10010010" }, { "id": "https://openalex.org/W2783589701", "doi": "10.1016/j.disopt.2017.08.005", "title": "A linear time algorithm for the 3 -neighbour Travelling Salesman Problem on a Halin graph and extensions", "year": 2017, "publication_date": "2017-09-22", "type": "article", "language": "en", "authors": [ "Brad Woods", "Abraham P. Punnen", "Tamon Stephen" ], "first_author": "Brad Woods", "venue": "Discrete Optimization", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Mathematics", "Combinatorics", "Travelling salesman problem", "Hamiltonian path", "Algorithm", "Graph", "Discrete mathematics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.disopt.2017.08.005" }, { "id": "https://openalex.org/W7154456124", "doi": "10.20381/ruor-31854", "title": "Intelligent Scheduling and Charging Optimization for CAEVs and UAVs in Urban Transportation Networks", "year": 2026, "publication_date": "2026-04-14", "type": "dissertation", "language": "en", "authors": [ "Palwasha Waheed Shaikh" ], "first_author": "Palwasha Waheed Shaikh", "venue": null, "publisher": null, "abstract": "Urban transportation systems are increasingly strained by congestion, air pollution, and greenhouse gas emissions, motivating the need for scalable and sustainable mobility solutions. Connected Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are key enablers of next generation intelligent transportation systems (ITS), yet their adoption remains constrained by limited battery capacity, fragmented charging infrastructure, and the absence of reliable coordination mechanisms under dynamic and heterogeneous conditions. Existing research often addresses individual charging technologies or isolated vehicle classes, leaving a gap in system-level coordination across ground and aerial platforms. This thesis addresses this gap by demonstrating that charging coordination, rather than charging technology alone, is a critical barrier to sustainable deployment of autonomous electric mobility. A scalable and interoperable three-layer charging network architecture is proposed to support static charging, dynamic wireless charging, and vehicle-to-vehicle (V2V) charging across heterogeneous charging node types. A hybrid coordination topology and unified handshake protocol enable interoperable charging discovery, reservation based scheduling, and execution while remaining compatible with existing communication infrastructure. Building on this architectural foundation, coordinated charging scheduling and trip planning mechanisms that consider both vehicle routing and charging resource allocation are developed using heuristic and learning-based approaches. Two heuristic strategies, Static Heuristic Charging Scheduling Policy (SH-CSP) and Dynamic Heuristic Charging Scheduling Policy (DH-CSP), provide interpretable baselines and handle early and late arrivals. To address scalability and non-stationary demand, Safety, Sustainability, and Scheduling-Aware Feasibility Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG) is introduced to enable adaptive, real-time scheduling under heterogeneous charging conditions. Reliability and fairness are embedded as system-level properties within the architecture and scheduling framework. Simulation-based evaluation demonstrates charging reservation fulfillment rates approaching 98%, waiting time reductions of up to approximately 80% relative to heuristic baselines, and consistently high fairness across heterogeneous charging networks. Comparative evaluation against baseline deep reinforcement learning methods, including DDPG and TD3, and explainable artificial intelligence (XAI) techniques further validate effectiveness and interpretability. Overall, this thesis presents a unified, reliable, and scalable framework for intelligent scheduling and charging optimization through learning-based decision-making in next-generation urban transportation systems.", "keywords": [ "Scheduling (production processes)", "Scalability", "Interoperability", "Reservation", "Software deployment", "Dynamic priority scheduling", "Heuristic", "Network topology", "Wireless" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.20381/ruor-31854" }, { "id": "https://openalex.org/W7167691055", "doi": "10.3389/ffutr.2026.1760027", "title": "Joint optimization of UAVs nest siting and parallel scheduling under solar power supply constraints", "year": 2026, "publication_date": "2026-07-08", "type": "article", "language": "en", "authors": [ "Weihao Zhang", "Wenbin Wu", "Lihui Lin", "Xiaojie Wu" ], "first_author": "Weihao Zhang", "venue": "Frontiers in Future Transportation", "publisher": "Frontiers Media", "abstract": "As power grid maintenance moves toward digitalization and decarbonization, UAVs have become a key technological asset for transmission line inspection, with supporting nests (automated UAV base stations) increasingly evolving toward autonomous, renewable-energy-powered infrastructures. Solar energy is gradually being adopted as the primary power source for these nests, reducing the construction and maintenance costs of external power systems. Nevertheless, the high upfront investment in solar and energy storage, coupled with substantial spatial variability in solar irradiance, poses challenges for efficient nest deployment and UAVs scheduling under large-scale, long-range inspection scenarios. To address these challenges, this paper proposes a two-stage integrated optimization framework for nest siting and UAVs parallel scheduling. In the first stage, a Mixed-Integer Linear Programming (MILP) model is formulated with the objective of minimizing nest construction costs, while an Epsilon-constraint method is used to set a quality threshold. The quality threshold is determined by a synergy score that combines factors such as solar irradiance, tower density, and spatial proximity, to achieve the optimal nest layout. In the second stage, based on the established nest layout and coverage relationships, a variant of the Vehicle Routing Problem (VRP) with endurance and charging constraints is developed, aiming to minimize the total inspection duration. Experimental results show that the optimized nest layout effectively reduces overall construction cost while achieving a higher synergy score. Under the cost-effective siting solution, the cross-tower feasible swapping strategy reduces the system Makespan by approximately 1.3%. The framework has been validated for its effectiveness and scalability in solar-driven UAV inspection deployment and scheduling.", "keywords": [ "Scheduling (production processes)", "Software deployment", "Scalability", "Photovoltaic system", "Solar power", "Key (lock)", "Renewable energy", "Benchmark (surveying)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2026.1760027/pdf" }, { "id": "https://openalex.org/W4385849110", "doi": "10.48550/arxiv.2308.06973", "title": "Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach", "year": 2023, "publication_date": "2023-08-14", "type": "preprint", "language": "en", "authors": [ "Sijie He", "Ziye Jia", "Chao Dong", "Wang, Wei", "Yilu Cao", "Yang Yang", "Qihui Wu" ], "first_author": "Sijie He", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.", "keywords": [ "Reinforcement learning", "Computer science", "Routing (electronic design automation)", "Node (physics)", "Computer network", "Adversary", "Routing domain", "Policy-based routing", "Distributed computing", "Path (computing)", "Static routing", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2308.06973" }, { "id": "https://openalex.org/W4416582069", "doi": "10.1371/journal.pone.0336935", "title": "UAV inspection path optimization in offshore wind farms using the OPTION-A*-DQN algorithm", "year": 2025, "publication_date": "2025-11-24", "type": "article", "language": "en", "authors": [ "Maolin Xu", "Chao Deng", "Xiangyu Hu", "Yuan Lü", "Wenyan Xue", "Bin Zhu" ], "first_author": "Maolin Xu", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "In response to the inefficiencies in offshore wind farm inspections caused by path redundancy and mission omissions, this study proposes a novel path planning method for Unmanned Aerial Vehicle (UAV) inspections, integrating multi-constraint optimization and intelligent scheduling. First, a four-dimensional constraint model is established, encompassing wind speed, charging, minimum UAV fleet size, and dynamic obstacle avoidance. Second, the OPTION-A*-DQN hybrid algorithm is developed by synergizing A* heuristic search with deep reinforcement learning (DRL) to balance global navigation and local optimization. An improved K-Means algorithm further enables efficient topological partitioning for multi-UAV collaboration. Comparative evaluations against original OPTION-DQN and conventional heuristic methods (Dijkstra and Simulated Annealing) demonstrate that the proposed method achieves three key improvements: (1) a 10% higher task completion rate, (2) a 14.9% reduction in path distance, and (3) a 20% faster simulation time. This work significantly advances intelligent path planning for offshore wind farm inspections.", "keywords": [ "Motion planning", "Path (computing)", "Heuristic", "Obstacle", "Redundancy (engineering)", "Reduction (mathematics)", "Offshore wind power", "Obstacle avoidance" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1371/journal.pone.0336935" }, { "id": "https://openalex.org/W7104518169", "doi": "10.5267/j.sci.2025.1.004", "title": "A scientometric review of the blood supply chain literature (2010-2025): Evolution, trends, and intellectual structure", "year": 2025, "publication_date": "2025-01-01", "type": "review", "language": null, "authors": [ "Reza Ramezanian" ], "first_author": "Reza Ramezanian", "venue": "Scientometrica", "publisher": null, "abstract": "The blood supply chain (BSC) is a crucial and intricate system in the healthcare sector, which is marked by perishable products, fluctuating supply and demand, and a major impact of inefficiency. This paper showcases a detailed scientific review of BSC literature from 2010 to 2025 through scientometric methods, thereby mapping out its intellectual structure and development. By scrutinizing both foundational and recent publications, the authors are able to point out the research streams, methodological trends and main scholars. The scrutiny brings forward three leading research paradigms: (1) robust and resilient network design for disaster response, with Jawad as the leading scholar; (2) green and sustainable BSC modeling under uncertainty, where Pishvaee and his team are the main contributors; and (3) integrated inventory-routing problems for perishables, with Ramezanian as the pivotal author. This discipline is moving away from deterministic, single-objective models to the development of intricate multi-objective frameworks under hybrid uncertainties (robust, fuzzy, stochastic) which are being solved increasingly with metaheuristics and supported by case studies from real applications. The new trends include the combination of AI/ML for forecasting and decision-making, blockchain for transparency, and drones for the delivery part. The present review collects all these advancements and gives a succinct direction for both researchers and practitioners.", "keywords": [ "Supply chain", "Scrutiny", "Supply chain management", "Supply network", "Scientific literature", "Big data" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5267/j.sci.2025.1.004" }, { "id": "https://openalex.org/W4327713328", "doi": "10.1201/9781003316671-5", "title": "Application of Data Science in Transportation Systems", "year": 2023, "publication_date": "2023-03-17", "type": "book-chapter", "language": "en", "authors": [ "Rakesh K. Jain", "Ashish R. Joshi", "Bharati H. Gavhane" ], "first_author": "Rakesh K. Jain", "venue": null, "publisher": null, "abstract": "This chapter discusses the different modes and aspects of transportation development. Also, this chapter includes the transportation data analytics, modern data collection techniques, and multimodal transportation system to improve the transportation networks. Before starting any project, the available data required to be analyzed, validated, and applied to the data for designing, modeling, and implementation. The data collection, storage, data security, and quality of data are the main aspects in transportation systems. For analyzing data, the directional traffic volume, pedestrian volume, different classes of vehicles, and pavement distress need to be considered for the prediction of pavement health. For increasing traffic congestion, the modern data collection techniques used, such as ICT tools, include computers, IoT, and electronic data processing (EDP) tools. Also monitoring, image processing, remote-sensing technologies, ATC, and drones are used for data collection. This chapter also discusses the advantages of prognostic analytics for public transportation planning and multimodal transportation. For implementation of MTS advanced technological tools such as cameras, GPS, and geo-locations are used. The data science is applicable in prediction of traffic congestion using neural network, fuzzy systems, vehicle route planning, and also TDM. The study of advancements of new technology, such as self-driving cars and smart parking systems are also explained in this chapter. This chapter discusses in detail a sample case study on bicycle demand. The case study illustrates the use of Python programming language to analyze data and understand the factors affecting demand for a bike sharing business.", "keywords": [ "Computer science", "Data science" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/9781003316671-5" }, { "id": "https://openalex.org/W7161016558", "doi": "10.1145/3807211.3807236", "title": "Innovative Research on a Joint UAV-Vehicle Delivery System for the Low-Altitude Economy", "year": 2026, "publication_date": "2026-02-06", "type": "conference-paper", "language": null, "authors": [ "Xue Lu", "Jizhe Zhang" ], "first_author": "Xue Lu", "venue": null, "publisher": null, "abstract": "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.", "keywords": [ "Payload (computing)", "Context (archaeology)", "Sustainability", "Vehicle routing problem", "Resource allocation", "Routing (electronic design automation)", "Operational efficiency", "Joint (building)", "Resource (disambiguation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3807211.3807236" }, { "id": "https://openalex.org/W7167272639", "doi": "10.3390/app16136688", "title": "Fast and Efficient Data Collection Management Approach with Two-Layer UAV Network with Massive Sensor Nodes", "year": 2026, "publication_date": "2026-07-03", "type": "article", "language": "en", "authors": [ "S I Kim", "Seungho Yoo", "Minjun Kim", "Ukhyun Jeong", "Wooyong Jung", "Hwangnam Kim" ], "first_author": "S I Kim", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload.", "keywords": [ "Multirotor", "Payload (computing)", "Scalability", "Data collection", "Routing (electronic design automation)", "Wireless sensor network", "Planner", "Cluster analysis", "Greedy algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/16/13/6688/pdf?version=1783085024" }, { "id": "https://openalex.org/W7169530677", "doi": "10.1142/s2301385028500483", "title": "A Comparative Study of MILP and MINLP for UAV Conflict Resolution Under Different Spatial Configurations", "year": 2026, "publication_date": "2026-07-18", "type": "article", "language": "en", "authors": [ "Xiaoqin Liu", "Gang Xiao", "Yulong Ding", "Mengqi Xue", "Bin He" ], "first_author": "Xiaoqin Liu", "venue": "Unmanned Systems", "publisher": "World Scientific", "abstract": "The large-scale deployment of unmanned aerial vehicles (UAVs) in low-altitude airspace requires efficient and reliable conflict resolution methods. This paper presents a comparative study of mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) formulations for heading-only UAV conflict resolution under three representative spatial configurations: circular, diamond, and random. To ensure a fair comparison, both formulations are implemented using the SCIP optimization framework. Extensive simulations with varying fleet sizes demonstrate that computational performance is governed primarily by spatial configuration rather than optimization class. In the structured diamond configuration, both models achieve comparable solution quality, with MINLP providing a modest reduction in computation time. In the symmetric circular configuration, MINLP consistently outperforms MILP by avoiding discretization-induced inefficiencies. In the unstructured random configuration, MINLP exhibits substantial advantages in both runtime and solution quality, whereas MILP experiences rapidly increasing computational complexity. These findings indicate that nonlinear optimization is not inherently less efficient than linear optimization for UAV conflict resolution. Instead, the underlying spatial conflict structure plays a decisive role in algorithm performance, providing practical guidance for selecting optimization frameworks in future UAV traffic management systems.", "keywords": [ "Computation", "Software deployment", "Nonlinear programming", "Reduction (mathematics)", "Nonlinear system", "Linear programming", "Conflict resolution" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1142/s2301385028500483" }, { "id": "https://openalex.org/W7204441308", "doi": "10.48550/arxiv.2608.25383", "title": "Traffic-Adaptive Per-Hop Multipath Routing in Multi-Hop UAV Networks", "year": 2026, "publication_date": "2026-08-26", "type": "preprint", "language": null, "authors": [ "Zhenyu Zhao", "Tiankui Zhang", "Xiaoxia Xu", "Yuanpeng Zheng", "Junjie Li", "Xing, Wenjuan" ], "first_author": "Zhenyu Zhao", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In uncrewed aerial vehicle (UAV)-relayed mobile edge computing (MEC) networks, computation tasks generate traffic with diverse latency requirements and data sizes. Routing decisions therefore need to adapt to both traffic characteristics and changing network conditions. Compared with single-path routing, multipath routing is better suited to such heterogeneous traffic because it provides multiple forwarding options and enables flexible traffic splitting. However, conventional multipath routing usually splits traffic over predefined end-to-end paths, making it difficult to respond quickly to link fluctuations and topology changes in UAV networks. To address this issue, we propose a traffic-adaptive per-hop multipath routing method for multi-hop UAV networks, in which each UAV dynamically distributes traffic among multiple candidate next hops. We formulate the routing problem to improve the on-time packet delivery ratio while reducing the packet loss ratio, and model it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we develop a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM). MAPPO-DM follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution. Simulation results show that MAPPO-DM outperforms the baseline methods and maintains robust performance under various network conditions.", "keywords": [ "Multipath routing", "Static routing", "Network packet", "Multipath propagation", "Link-state routing protocol", "Dynamic Source Routing", "Destination-Sequenced Distance Vector routing", "Equal-cost multi-path routing", "Markov decision process" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2608.25383" }, { "id": "https://openalex.org/W7163587719", "doi": "10.63313/ajet.9062", "title": "Multi-UAV Task Allocation Method for Road Emergency Rescue Based on Improved Multi-Objective Genetic Algorithm", "year": 2026, "publication_date": "2026-06-04", "type": "article", "language": null, "authors": [ "Xiaoyu Wang", "Yangshan Tang", "Ying Li" ], "first_author": "Xiaoyu Wang", "venue": "Academic Journal of Emerging Technologies", "publisher": null, "abstract": "To enhance the operational efficiency of multi-UAV material delivery in emergency rescue scenarios, this paper establishes a mathematical model for multi-UAV task allocation and proposes an improved hybrid multi-objective genetic algorithm, namely the AC-MOGA algorithm. Firstly, the multi-UAV material delivery task allocation problem is elaborated. A mathematical model is constructed with three optimization objectives: minimizing the total flight distance of UAVs, reducing the maximum task completion time, and optimizing the weighted total response time. Secondly, Logistic chaotic mapping and adaptive strategies are introduced to modify the multi-objective genetic algorithm. The improvements are implemented in population initialization, crossover and mutation operations to construct the proposed algorithm for problem solving. Finally, based on six groups of VRPLIB benchmark instances, numerical simulations are carried out on the MATLAB platform to obtain the task allocation schemes derived by the AC-MOGA algorithm. Comparative analyses with a state-of-the-art improved genetic algorithm are conducted on identical benchmark instances. The results verify the effectiveness and feasibility of the AC-MOGA algorithm for solving vehicle routing problems.", "keywords": [ "Crossover", "Genetic algorithm", "Benchmark (surveying)", "Task (project management)", "Population", "MATLAB", "Vehicle routing problem", "Mutation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.63313/ajet.9062" }, { "id": "https://openalex.org/W4280552008", "doi": "10.48550/arxiv.2205.07126", "title": "LB-OPAR: Load Balanced Optimized Predictive and Adaptive Routing for Cooperative UAV Networks", "year": 2022, "publication_date": "2022-05-14", "type": "preprint", "language": "en", "authors": [ "M. Gharib", "Fatemeh Afghah", "Elizabeth Serena Bentley" ], "first_author": "M. Gharib", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Cooperative ad-hoc UAV networks have been turning into the primary solution set for situations where establishing a communication infrastructure is not feasible. Search-and-rescue after a disaster and intelligence, surveillance, and reconnaissance (ISR) are two examples where the UAV nodes need to send their collected data cooperatively into a central decision maker unit. Recently proposed SDN-based solutions show incredible performance in managing different aspects of such networks. Alas, the routing problem for the highly dynamic UAV networks has not been addressed adequately. An optimal, reliable, and adaptive routing algorithm compatible with the SDN design and highly dynamic nature of such networks is required to improve the network performance. This paper proposes a load-balanced optimized predictive and adaptive routing (LB-OPAR), an SDN-based routing solution for cooperative UAV networks. LB-OPAR is the extension of our recently published routing algorithm (OPAR) that balances the network load and optimizes the network performance in terms of throughput, success rate, and flow completion time (FCT). We analytically model the routing problem in highly dynamic UAV network and propose a lightweight algorithmic solution to find the optimal solution with $O(|E|^2)$ time complexity where $|E|$ is the total number of network links. We exhaustively evaluate the proposed algorithm's performance using ns-3 network simulator. Results show that LB-OPAR outperforms the benchmark algorithms by $20\\%$ in FCT, by $30\\%$ in flow success rate on average, and up to $400\\%$ in throughput.", "keywords": [ "Computer science", "Benchmark (surveying)", "Routing (electronic design automation)", "Distributed computing", "Throughput", "Destination-Sequenced Distance Vector routing", "Flow routing", "Computer network", "Adaptive routing", "Static routing", "Link-state routing protocol", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2205.07126" }, { "id": "https://openalex.org/W4416247098", "doi": "10.48550/arxiv.2510.19548", "title": "Multi-UAV Flood Monitoring via CVT with Gaussian Mixture of Density Functions for Coverage Control", "year": 2025, "publication_date": "2025-10-22", "type": "preprint", "language": null, "authors": [ "Jie Song", "Yang Bai", "Mikhail M. Svinin", "Naoki Wakamiya" ], "first_author": "Jie Song", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This study presents a control strategy for coordinating multiple unmanned aerial vehicles (UAVs) to monitor unknown flood regions and estimate the extent of inundation. The proposed method adopts a density-driven coverage framework based on Centroidal Voronoi Tessellation (CVT), in which the density function is modeled using a Gaussian Mixture of Density Functions (GMDF). This formulation provides a more accurate characterization of inundated areas compared to conventional axis-aligned Gaussian models. The performance of the two density modeling approaches is systematically evaluated under different UAV fleet sizes (16, 20, and 24), with multiple simulation trials conducted in the ROS/Gazebo environment. The results show that the GMDF-based formulation consistently achieves higher coverage rates, demonstrating its effectiveness in enhancing flood monitoring and improving UAV spatial distribution.", "keywords": [ "Gaussian", "Voronoi diagram", "Probability density function", "Gaussian network model", "Flood myth", "Function (biology)", "Mixture model" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2510.19548" }, { "id": "https://openalex.org/W4404563187", "doi": "10.1109/access.2024.3503686", "title": "Collaborative Routing With Multi-Heuristic Algorithm for Enhanced Delivery Efficiency in Truck-Multi Unmanned Aerial Vehicles Systems", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Ramdhan Nugraha", "Md Masuduzzaman", "Soo Young Shin" ], "first_author": "Ramdhan Nugraha", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "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.", "keywords": [ "Truck", "Computer science", "Heuristic", "Routing (electronic design automation)", "Vehicle routing problem", "Algorithm", "Real-time computing", "Distributed computing", "Computer network", "Artificial intelligence", "Engineering", "Automotive engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2024.3503686" }, { "id": "https://openalex.org/W4412624691", "doi": "10.1109/iccet65872.2025.11082605", "title": "On the GA-based UAV-Assisted Data Collection with Stochastic Arrivals in Remote Sensing Applications", "year": 2025, "publication_date": "2025-05-16", "type": "conference-paper", "language": "en", "authors": [ "Minbin Li", "Qingchun Chen" ], "first_author": "Minbin Li", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicle (UAV)-assisted wireless sensor networks (WSNs) have emerged as a promising paradigm for enabling reliable data acquisition from distributed sensor nodes (SNs) in geographically constrained environments, such as remote wilderness or post-disaster terrains. In large-scale heterogeneous WSNs with stochastic data arrivals, the UAV-assisted data collection design becomes crucial to optimize aerial resource utilization while maintaining reliable data collection. This paper investigates a multi-UAV collaborative data collection framework that holistically integrates ground SN geo-distributions and their stochastic data arrivals. To address this NP-hard optimization problem, we propose a metaheuristic deployment strategy leveraging an enhanced genetic algorithm (GA) with adaptive crossover-mutation operators, aiming to minimize UAV fleet size under strict constraints of all SNs coverage and reliable data collection task. Numerical results reveal that, the proposed GA-based UAV-assisted data collection scheme can determine reasonable multiple UAV deployment to guarantee full ground SNs coverage with the least required UAV number, outperforming greedy heuristic and geo-distribution clustering-based approaches. Furthermore, the proposed UAV-assisted data collection exhibits remarkable adaptability, maintaining service stability across diverse SN geo-distributions and stochastic arrival intensities. Analysis results indicate that, the required number of UAVs will be dependent on the number and geo-distribution of ground SNs. For the given ground geodistribution of all SNs, only when the data arrival rates exceed a specific threshold, the required number of UAVs gradually increases with rising data arrival rates.", "keywords": [ "Computer science", "Data collection", "Real-time computing", "Remote sensing", "Geography", "Statistics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccet65872.2025.11082605" }, { "id": "https://openalex.org/W7202295611", "doi": "10.1109/access.2026.3723185", "title": "Risk-Diffusion Auction for Spatially Coupled Multi-UAV Functional Task Allocation at the Edge", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Jinhao Lv", "Yi Wan", "Hao Sun", "Mengting Tan" ], "first_author": "Jinhao Lv", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Edge computing allows a nearby server or leader UAV to plan with current sensor data. In many multi-UAV missions, mandatory tasks create mission value, while functional tasks such as reconnaissance and jamming reduce the risk of those mandatory tasks. A functional task is useful only when it is placed close enough to the tasks it supports, but executing it also adds travel, payload, and exposure costs. Task selection and route planning are therefore coupled through space. Many existing allocators score tasks separately and do not represent this coupling directly. We propose FTRDA, a Functional-Task Risk-Diffusion Allocation algorithm. FTRDA builds a battlefield dependency graph (BDG) with distance-decayed support edges. An exponential model converts the total support received by a task into its effective risk. The algorithm then ranks functional tasks by risk reduction per unit cost, removes redundant supporters, and uses a heterogeneous sequential single-item auction to build feasible UAV routes. The auction runs on a central edge node and enforces UAV type, capacity, and range constraints. Experiments on $50\\times 50$ grid scenarios vary fleet size, task mix, risk, and support radius. FTRDA reduces mission cost by 8% to 30% relative to six heuristic baselines. On small and medium instances, its solution quality remains close to the CPLEX reference while requiring much less time; on the largest reported comparison, the runtime difference is about four orders of magnitude. The evaluation also reports statistical tests, planning latency, message volume, and edge memory separately. Under the stated static and full-observability assumptions, FTRDA provides a lightweight planning method for IoT-connected UAV fleets. The edge planner is centralized; the method is not a fully distributed swarm-intelligence protocol.", "keywords": [ "Task (project management)", "Enhanced Data Rates for GSM Evolution", "Auction algorithm", "Resource management (computing)", "Resource allocation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2026.3723185" }, { "id": "https://openalex.org/W4401160331", "doi": "10.1145/3657529.3657536", "title": "Optimization Algorithm for AODV-Based Unmanned Cluster Routing with Multiple QoS Objectives", "year": 2024, "publication_date": "2024-04-12", "type": "conference-paper", "language": "en", "authors": [ "Fan Luo", "Jianguo Zhou", "Peng Chen", "Yanming Dong", "Lu Lu" ], "first_author": "Fan Luo", "venue": null, "publisher": null, "abstract": "With their capability for rapid on-demand deployment and strong maneuverability, Unmanned Aerial Vehicles (UAVs) have been extensively utilized in the field of wireless communication. To ensure the quality of data transmission in unmanned cluster collaborative communication, this paper optimizes the path selection mechanism of ad hoc on-demand distance vector (AODV) routing protocol and proposes a path selection algorithm based on multi-objective Quality of Service (QoS) optimization (MQ-AODV). Initially, the QoS metrics required for multimedia data transmission optimization are analyzed, considering the multi-objective QoS routing problem as an NP-complete problem, and approached through multi-objective optimization. Subsequently, an improved genetic algorithm is employed to search for transmission paths with the best network service quality. Finally, implementing this routing algorithm using OPNET network simulation software and constructing a UAV cluster network, comparative experiments are conducted using two types of AODV protocols and the MQ-AODV protocol. The analysis of network simulation results indicates that the improved MQ-AODV protocol enhances performance metrics such as network throughput, packet loss rate, and end-to-end delay.", "keywords": [ "Computer science", "Quality of service", "Computer network", "Cluster (spacecraft)", "Ad hoc On-Demand Distance Vector Routing", "Routing (electronic design automation)", "Algorithm", "Distributed computing", "Routing protocol", "Dynamic Source Routing" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3657529.3657536" }, { "id": "https://openalex.org/W7162012133", "doi": "10.1007/s44196-026-01325-9", "title": "A Genetic-Operator-Integrated Grey Wolf Optimizer for Solving the EV-UAV Collaborative Emergency Logistics Problem", "year": 2026, "publication_date": "2026-05-21", "type": "article", "language": "en", "authors": [ "Can Guo", "Ying Qiao", "Yuelin Gao", "Ruiqian Miao", "Wenyu Zhang" ], "first_author": "Can Guo", "venue": "International Journal of Computational Intelligence Systems", "publisher": "Springer Nature", "abstract": "EV–UAV collaborative routing provides a practical option for post-disaster emergency logistics, particularly when ground infrastructure is severely disrupted. However, optimizing such multi-modal systems is mathematically challenging due to the strict coupling of vehicle routing sequences, temporal synchronization, and charging feasibility and energy constraints under high-altitude post-disaster conditions. Existing metaheuristic algorithms often struggle with the high-dimensional and discrete nature of this problem, which may lead to premature convergence and entrapment in local optima. To address this issue, this study proposes a Genetic-Operator-Integrated Grey Wolf Optimizer (GOI-GWO). The proposed method employs a discrete encoding strategy to adapt the continuous search mechanism of GWO to combinatorial routing problems, incorporates genetic operators to improve population diversity and solution quality, and introduces a multi-neighborhood local search procedure to strengthen local refinement. Experiments on benchmark instances show that GOI-GWO outperforms multiple benchmark algorithms in overall solution quality and robustness, especially on medium- and large-scale instances. In the Tingri earthquake case study, GOI-GWO reduces the mean operational cost by 8.4% compared with ALNS and by 5.7% compared with TSGWO, while maintaining a 100% feasibility rate. In addition, the sensitivity analysis indicates that the baseline objective setting provides a reasonable balance between operational cost and service punctuality. These findings indicate that GOI-GWO is an effective and robust approach for post-disaster EV–UAV collaborative emergency logistics.", "keywords": [ "Benchmark (surveying)", "Premature convergence", "Vehicle routing problem", "Metaheuristic", "Convergence (economics)", "Local search (optimization)", "Routing (electronic design automation)", "Baseline (sea)", "Genetic algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s44196-026-01325-9" }, { "id": "https://openalex.org/W7164393853", "doi": "10.1016/j.ergon.2026.103981", "title": "Homogeneous or heterogeneous? Simulation-based comparison of task allocation strategies for multi-operator UAV swarms control", "year": 2026, "publication_date": "2026-06-11", "type": "article", "language": "en", "authors": [ "Mingwei Chen", "Xiaodong Xu", "Liang Ma" ], "first_author": "Mingwei Chen", "venue": "International Journal of Industrial Ergonomics", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Task (project management)", "Workflow", "Key (lock)", "Blocking (statistics)", "Control (management)", "Command and control", "Parameterized complexity", "Homogeneous", "Monte Carlo method", "Timeline" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ergon.2026.103981" }, { "id": "https://openalex.org/W7127606906", "doi": "10.1109/ccnc65079.2026.11366561", "title": "GADCA: A MARL-Based Graph-Attentive Decentralized Collision Avoidance Framework for UAV in UTM", "year": 2026, "publication_date": "2026-01-09", "type": "conference-paper", "language": null, "authors": [ "Asma Hamissi", "Amine Dhraief", "Layth Sliman" ], "first_author": "Asma Hamissi", "venue": null, "publisher": null, "abstract": "With increasing congestion in urban airspace, centralized Unmanned Aircraft System Traffic Management (UTM) face scalability challenges and risks of single-point failures, driving the need for robust decentralized solutions to ensure safe autonomous UAV navigation. This paper introduces the Graph-Attentive Deep Collision Avoidance (GADCA) framework, a decentralized Multi-Agent Reinforcement Learning (MARL) approach for collision avoidance in urban airspace. GADCA extends the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) by integrating Graph Attention Networks (GAT) to model dynamic inter-agent relationships, enabling UAVs to navigate effectively using local observations and shared situational awareness. Addressing limitations in prior work, GADCA incorporates 3D dynamics, continuous velocity action spaces, and scalable fleet sizes for robust performance. Unlike centralized UTM methods, GADCA operates in a decentralized manner during execution, where each UAV independently makes collision-avoidance decisions based on local and shared information, without relying on a central controller. It combines decentralized MARL execution with graph-based relational reasoning to enhance collision avoidance. Experimental results show that GADCA outperforms MADDPG across fleets of 5 to 10 UAVs, achieving 45.8% to 67.7% improvements in learning performance, 12.0% to 54.5% enhancements in task completion, up to 23% reduction in conflicts, and 58% to 100% reduction in collision risk, with no physical collisions recorded. The framework’s dynamic adjacency matrix and collective reward mechanism effectively balance navigation efficiency and safety in complex 3D urban environments, using a highly realistic Gauss-Markov mobility model.", "keywords": [ "Collision avoidance", "Reinforcement learning", "Scalability", "Situation awareness", "Collision", "Reduction (mathematics)", "Graph", "Task (project management)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccnc65079.2026.11366561" }, { "id": "https://openalex.org/W4406459761", "doi": "10.1109/cdics61497.2023.00013", "title": "Power-Aware Path Planning for Vehicle-Assisted Heterogeneous UAVs in Mobile Crowd Sensing", "year": 2023, "publication_date": "2023-12-08", "type": "conference-paper", "language": "en", "authors": [ "Zixiao Zhou", "Liang Liu", "Wenbin Zhai", "Jiancheng Song", "Yulei Liu" ], "first_author": "Zixiao Zhou", "venue": null, "publisher": null, "abstract": "With the development of microelectronics in recent years, the performance of unmanned aerial vehicles (UAVs) has been improving continuously. Modern rotary-wing UAVs possess high maneuverability and agility, making them widely applied in mobile crowd sensing (MCS). In order to solve the shortcoming of limited battery capacity and expand the mission area of UAVs, the ground vehicle is introduced as a platform for transportation, launch, recycle, and recharging UAVs. However, existing studies only consider the case of vehicle-assisted homogeneous UAVs. In reality, due to different sensing requirements and UAV hardware, vehicles may need to assist heterogeneous UAVs with different sensors, flight speeds, and battery capacities. In this paper, we formalize and study the vehicle-assisted heterogeneous UAVs path planning problem, and decompose it into three sub-problems, namely detection point allocation, UAV path planning, and vehicle route planning. In order to solve the above problems, we proposes an efficient power-aware path planning algorithm for vehicle-assisted multi-heterogeneous-UAV (VHUPA). In VHUPA, we first design the genetic algorithm to find the allocation scheme of the detection points, then plan flight paths of UAVs at each parking spot according to the allocation scheme, and finally optimize the route of the ground vehicle according to the power consumption of UAVs to minimize the waiting time for charging. Performance evaluation demonstrates that time cost of the VHUPA solution is reduced by more than 21% compared with the existing algorithm.", "keywords": [ "Computer science", "Motion planning", "Path (computing)", "Power (physics)", "Mobile robot", "Real-time computing", "Computer network", "Artificial intelligence", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/cdics61497.2023.00013" }, { "id": "https://openalex.org/W7108696896", "doi": "10.1016/j.engappai.2025.113453", "title": "A collaborative bi-level optimization approach for smart logistics: Depot location and unmanned aerial vehicle-based pickup-delivery services", "year": 2025, "publication_date": "2025-12-05", "type": "article", "language": "en", "authors": [ "Yu Zheng", "Wendong Gai", "Jing Zhang", "Maiying Zhong" ], "first_author": "Yu Zheng", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Benchmark (surveying)", "Cluster analysis", "Scheduling (production processes)", "Fuzzy logic", "Software deployment", "Key (lock)", "Convergence (economics)", "Routing (electronic design automation)", "Depot" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2025.113453" }, { "id": "https://openalex.org/W4386876397", "doi": "10.48550/arxiv.2309.09942", "title": "OptiRoute: A Heuristic-assisted Deep Reinforcement Learning Framework for UAV-UGV Collaborative Route Planning", "year": 2023, "publication_date": "2023-09-18", "type": "preprint", "language": "en", "authors": [ "Md Safwan Mondal", "Ramasamy, Subramanian", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles (UAVs) are capable of surveying expansive areas, but their operational range is constrained by limited battery capacity. The deployment of mobile recharging stations using unmanned ground vehicles (UGVs) significantly extends the endurance and effectiveness of UAVs. However, optimizing the routes of both UAVs and UGVs, known as the UAV-UGV cooperative routing problem, poses substantial challenges, particularly with respect to the selection of recharging locations. Here in this paper, we leverage reinforcement learning (RL) for the purpose of identifying optimal recharging locations while employing constraint programming to determine cooperative routes for the UAV and UGV. Our proposed framework is then benchmarked against a baseline solution that employs Genetic Algorithms (GA) to select rendezvous points. Our findings reveal that RL surpasses GA in terms of reducing overall mission time, minimizing UAV-UGV idle time, and mitigating energy consumption for both the UAV and UGV. These results underscore the efficacy of incorporating heuristics to assist RL, a method we refer to as heuristics-assisted RL, in generating high-quality solutions for intricate routing problems.", "keywords": [ "Reinforcement learning", "Computer science", "Unmanned ground vehicle", "Software deployment", "Heuristics", "Leverage (statistics)", "Real-time computing", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2309.09942" }, { "id": "https://openalex.org/W7113095549", "doi": null, "title": "OPTIMIZING UAV DELIVERY ROUTING FOR ISLAND DEMAND SURGES:A DATA-DRIVEN APPROACH TO MINIMIZE CARBON EMISSIONS", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Jayasuriya, Masha Dilmi", "Fernando, W. Madushan", "Thibbotuwawa, Amila", "Nielsen, Peter; id_orcid 0000-0002-4882-7942" ], "first_author": "Jayasuriya, Masha Dilmi", "venue": "VBN Forskningsportal (Aalborg Universitet)", "publisher": "Aalborg University", "abstract": "Unmanned aerial vehicles (UAVs) offer significant advantages over traditional delivery methods, including vertical take-off and landing capabilities, lower costs, and adaptable transport directly to consumers. Since its inception, the Vehicle Routing Problem (VRP) has seen substantial development, enabling routes to decide that reduce costs and improve the effectiveness of operations. So, integrating the VRP with UAVs, UAV routing aims to find the best combination of routes for UAVs that minimize overall distance or time, while enhancing delivery efficiency. This research focuses on optimizing last-mile UAV delivery for rapid response to demand surges on islands, to overcome accessibility challenges. It demonstrates the comparative advantages of UAVs and provides a data-driven approach to estimating carbon emission reductions. We conducted a detailed cost-benefit analysis comparing UAV delivery with existing boat services, considering factors such as delivery time, operational costs, and carbon emissions, using a Maldives hotel chain as a case study. To be more accurate, a long-term cost-benefit analysis was also performed. The analysis indicates that while UAV implementation may not yield immediate savings in the first two years, significant cost-effectiveness and operational efficiency are achievable from the third year onwards. The research also underscores the environmental sustainability of UAVs, showcasing substantial reductions in fuel consumption and carbon footprint compared to traditional boat delivery methods. On average, UAVs achieve an impressive 85% reduction in carbon emissions. Furthermore, the results show that using UAVs in logistics results in more effective delivery options, particularly in island markets where accessibility and rapid response are crucial to overcome demand surges. Moreover, our model's real-world applicability is supported by assumptions that ensure its relevance to large-scale distribution networks. The implications of this research are significant for decision-makers and policymakers, aiding in the formulation of strategies to adopt and integrate UAVs in practical scenarios.", "keywords": [ "Carbon footprint", "Sustainability", "Greenhouse gas", "Vehicle routing problem", "Routing (electronic design automation)", "Fuel efficiency", "Delivery Performance", "Energy consumption" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://vbn.aau.dk/da/publications/5cc00aab-751a-42a9-a60e-1db5fcb026d8" }, { "id": "https://openalex.org/W7161176736", "doi": "10.1109/icaace69793.2026.11508815", "title": "Deployment Method for Emergency Delivery of Multi-Agent UAV Swarms Driven by Large Language Models", "year": 2026, "publication_date": "2026-03-20", "type": "conference-paper", "language": null, "authors": [ "Bingbing You", "Hongyan Cui", "Jun Tang", "Zhengan Zhai" ], "first_author": "Bingbing You", "venue": null, "publisher": null, "abstract": "In response to challenges in natural language instruction parsing, dynamic task scaling, and rigid optimization strategies in urban multi-UAV emergency delivery, this paper proposes a large language model (LLM)-driven multi-agent UAV deployment framework. The LLM serves as a cognitive scheduler for task understanding, structuring, and strategy selection, while Google OR-Tools performs vehicle routing optimization under capacity and priority constraints to ensure feasibility and efficiency. The system adopts a closed-loop multi-agent architecture including intent parsing, task decomposition, deployment optimization, and execution monitoring. Experiments under different task scales compare heuristic, pure LLM, and pure OR-Tools baselines. Results show that the proposed method maintains 100% task completion while reducing total path cost and improving high-priority task satisfaction, demonstrating the scalability and engineering feasibility of the hybrid LLMoptimization architecture.", "keywords": [ "Software deployment", "Key (lock)", "Field (mathematics)", "Identification (biology)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icaace69793.2026.11508815" }, { "id": "https://openalex.org/W7160051786", "doi": "10.21203/rs.3.rs-9449934/v1", "title": "Dual-Mode UAV Scheduling for Urban Flood Response with State-Dependent Release Times", "year": 2026, "publication_date": "2026-05-04", "type": "preprint", "language": null, "authors": [ "Hailong Li", "Ya Li", "Jiawen Zhang", "Hanshen Li" ], "first_author": "Hailong Li", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Swap (finance)", "Bottleneck", "Scheduling (production processes)", "Flood myth", "Greedy algorithm", "Schedule", "Testbed", "Idle" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-9449934/latest.pdf" }, { "id": "https://openalex.org/W7151821751", "doi": "10.2139/ssrn.6546471", "title": "3D Terrain-Aware Multi-Trip UAV Routing in Hydropower Construction: Modeling Energy-Gravity Coupling and Quantifying the \"Gravity Tax\"", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Yifan Yao", "Shanshan Zheng", "Changxi Huang", "Wei Wang", "Yuting Chen", "Gangwei Cai" ], "first_author": "Yifan Yao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Hydropower", "Solver", "Routing (electronic design automation)", "Overhead (engineering)", "Software deployment", "Terrain", "Coupling (piping)", "Convergence (economics)", "Fidelity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6546471" }, { "id": "https://openalex.org/W4417162022", "doi": "10.48550/arxiv.2508.00938", "title": "Trusted Routing for Blockchain-Empowered UAV Networks via Multi-Agent Deep Reinforcement Learning", "year": 2025, "publication_date": "2025-07-31", "type": "preprint", "language": "en", "authors": [ "Ziye Jia", "Sijie He", "Qiuming Zhu", "Wei Wang", "Qihui Wu", "Zhu Han" ], "first_author": "Ziye Jia", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39$\\%$, 12.74$\\%$, and 16.6$\\%$ than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.", "keywords": [ "Routing (electronic design automation)", "Static routing", "Markov decision process", "Integer programming", "Flexibility (engineering)", "Network topology", "Multipath routing", "Reinforcement learning", "Link-state routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2508.00938" }, { "id": "https://openalex.org/W7167044821", "doi": "10.21924/cst.11.1.2026.1901", "title": "Mission-level energy efficiency optimization for multi-UAV data collection using a genetic algorithm", "year": 2026, "publication_date": "2026-07-02", "type": "article", "language": "en", "authors": [ "M.Si Sofyan Anif", "Selo Sulistyo", "Mustika I Wayan" ], "first_author": "M.Si Sofyan Anif", "venue": "Communications in Science and Technology", "publisher": "Komunitas Ilmuwan dan Profesional Muslim Indonesia", "abstract": "The efficient utilization of limited onboard energy remains a fundamental challenge in cooperative multi-UAV data collection missions. Existing routing approaches typically optimize surrogate objectives, such as travel distance or aggregate energy consumption, which do not directly reflect mission effectiveness. The present paper proposes a Mission-level Energy-aware Genetic Algorithm (ME-GA) that directly maximizes mission-level energy efficiency, defined as the ratio of successfully delivered sensing data to total energy consumption. The proposed framework integrates a mission-level simulator into the fitness evaluation, explicitly modeling UAV propulsion, sensing, data buffering, wireless communication, and return-to-base feasibility under energy constraints. Extensive simulations involving up to 9 UAVs and 100 Points of Interest (PoIs) under both grid and random spatial layouts demonstrate that ME-GA consistently achieves high and stable energy efficiency while maintaining near-complete task satisfaction and high data delivery reliability. In comparison to GA-based baselines, the proposed approach enhances energy efficiency by approximately 5–15% across the evaluated scenarios along with a reduction in total travel distance by up to 40% in larger fleet sizes. Overall, the results demonstrate that mission-level energy efficiency serves as a unified and physically meaningful objective for multi-UAV optimization, enabling robust and scalable performance across diverse operational scenarios.", "keywords": [ "Efficient energy use", "Scalability", "Data collection", "Energy (signal processing)", "Genetic algorithm", "Task (project management)", "Grid", "Data aggregator", "Aggregate (composite)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://cst.kipmi.or.id/journal/article/download/1901/258" }, { "id": "https://openalex.org/W7168135343", "doi": "10.1016/j.tra.2026.105137", "title": "UAV-aided mixed fleet management and operations for food delivery under travel time uncertainty", "year": 2026, "publication_date": "2026-07-13", "type": "article", "language": "en", "authors": [ "Wentao Huang", "Chenyang Wu", "Jacqueline T.Y. Lo", "Z L Wang", "Sisi Jian" ], "first_author": "Wentao Huang", "venue": "Transportation Research Part A Policy and Practice", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Fleet management", "Travel time", "Food delivery", "Vehicle miles of travel", "Vehicle routing problem", "Scheduling (production processes)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tra.2026.105137" }, { "id": "https://openalex.org/W4416236712", "doi": "10.21177/1998-4502-2025-17-2-940-951", "title": "Routing considering wind conditions in mountainous areas for the execution of transport-technological cycles of UAVS in precision agriculture", "year": 2025, "publication_date": "2025-06-30", "type": "article", "language": null, "authors": [ "Igor V Kovalev", "K D Astanakulov", "Dmitriy Kovalev" ], "first_author": "Igor V Kovalev", "venue": "Sustainable Development of Mountain Territories", "publisher": null, "abstract": "Introduction. In mountainous areas, the application of unmanned aerial vehicles in precision agriculture faces the challenge of unstable wind conditions, which significantly affect the quality of spraying and the uniformity of treatment distribution. To enhance the efficiency of transport-technological cycles, it is crucial to develop routing algorithms that consider meteorological conditions specific to these regions. Purpose of the research. The aim of this study is to develop and implement an improved routing algorithm for UAVs in precision agriculture that incorporates wind conditions typical of mountainous areas, thereby improving flight stability and the environmental performance of agro-technological operations. Materials and methods. The research is based on the adaptation of the classical Capacitated Vehicle Routing Problem using a parallel variant of the Clarke–Wright algorithm. A modification of the distance matrix was introduced by accounting for the projection of wind speed and direction on UAV trajectories. The software implementation was realized with modern tools and integrated with AnyLogic modeling as well as Python-based user interfaces. Results. Computational experiments demonstrated that incorporating wind factors reduced the total TTC execution time by 12–18% under wind speeds of 3–6 m/s compared to the classical algorithm without wind adjustment. The optimized routes ensured uniform crop coverage, minimized repeated flights, and reduced excessive use of spraying solution. Route stability and planning accuracy were improved under varying meteorological conditions of mountainous landscapes. Discussion. The inclusion of wind corrections in routing algorithms shows significant advantages over traditional approaches that disregard environmental conditions. The proposed method simultaneously addresses resource efficiency, environmental protection, and UAV operational stability in complex mountainous environments. Conclusion. The developed algorithm increases the efficiency and environmental sustainability of UAV-based agricultural operations in mountainous areas. Resume. The study confirms that route optimization considering wind conditions enhances treatment uniformity, reduces time and resource consumption, and improves UAV flight resilience. Suggestions for practical applications and directions for future research. The results can be applied in UAV-based agricultural planning and management in mountainous regions. Future research will focus on integrating real-time weather forecasts, applying machine learning for adaptive routing, and linking the algorithm with crop condition monitoring systems.", "keywords": [ "Stability (learning theory)", "Routing (electronic design automation)", "Wind speed", "Resource (disambiguation)", "Precision agriculture", "Software", "Route planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.21177/1998-4502-2025-17-2-940-951" }, { "id": "https://openalex.org/W7125975544", "doi": "10.5281/zenodo.18397858", "title": "A Fuzzy Multi Objective Optimization Model For A Sustainable And Competitive Uav Delivery Network Under Uncertainty", "year": 2026, "publication_date": "2026-01-28", "type": "article", "language": null, "authors": [ "Mehdi Shahegh", "Behnam Vahadani", "Farhad Eatebari" ], "first_author": "Mehdi Shahegh", "venue": "International Journal of Science Engineering and Technology", "publisher": null, "abstract": "This paper presents a fuzzy multi-objective optimization model for the Green Location-Routing Problem (GLRP) in competitive UAV delivery networks. The model simultaneously optimizes total cost, carbon emissions, and service time while determining facility locations and UAV routes under realistic constraints: capacity, energy, and no-fly zones. To address inherent uncertainties in customer demand and travel times, we develop a fuzzy logic framework that generates robust Pareto-optimal solutions for different confidence levels (α). The resulting fuzzy model is solved using three meta-heuristic algorithms: NSGA-II, MOACO, and MOSA. Numerical results demonstrate that the proposed fuzzy approach yields a more practical and cost-effective design compared to deterministic models, effectively balancing economic and environmental objectives under uncertainty. This study offers logistics managers a robust decision-support tool for sustainable UAV network deployment.", "keywords": [ "Fuzzy logic", "Fuzzy transportation", "Multi-objective optimization", "Robust optimization", "Fuzzy set", "Fuzzy control system", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18397858" }, { "id": "https://openalex.org/W7166097897", "doi": "10.1109/jstars.2026.3707605", "title": "IDACP: Information-Driven Adaptive Cooperative Planning for Ground-Sensor-Conditioned UAV Remote Sensing", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Boxun Han", "Zhiying Xu", "Linzhe Yang", "M Y Liu", "M Li", "Runlong Chang", "Fei Rao", "Lili Liu", "Sheng Wang", "Fu Xu Fu Xu" ], "first_author": "Boxun Han", "venue": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In this article, we present information-driven adaptive cooperative planning (IDACP), a framework that formally conditions adaptive aerial survey planning on the posterior of a preexisting, heterogeneous ground-sensor network. Existing informative path planning methods initialize uncrewed aerial vehicle (UAV) surveys from uninformed or purely covariate-based priors and therefore ignore the substantial posterior information already accumulated by deployed ground sensors, leaving aerial effort misallocated to regions already well constrained by fixed observations. IDACP fuses both modalities within a shared Gaussian process with heterogeneous per-observation noise, and it selects waypoints through a greedy upper-confidence-bound acquisition with analytic variance updates, an adaptive stopping criterion that conserves flight energy when residual uncertainty is low, and an uncertainty-weighted centroidal Voronoi partition for multi-UAV coordination. Across five systematic simulation experiments and a full-scale case study on the 14 972 km $^{2}$ Northeast China Tiger and Leopard National Park, IDACP reduces mission-integrated error by 12%–32% relative to sensor-naive baselines, matches the accuracy of dense-coverage strategies to within 2%–5% in root-mean-square error while consuming 35%–55% less flight energy, and extends its advantage from 32% to 48% as target mobility increases. Closed-form design formulas for sensor density and fleet sizing, together with a robust operating range for the activation radius, turn these findings into concrete deployment guidance for cooperative ground–aerial sensing in geoscience applications, such as wildlife monitoring and environmental hazard assessment.", "keywords": [ "Remote sensing application", "Radar tracker", "Space-based radar", "Synthetic aperture radar", "Earth remote sensing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jstars.2026.3707605" }, { "id": "https://openalex.org/W7202253716", "doi": "10.30574/gjeta.2026.28.2.0206", "title": "Optimizing multi-UAV mission scheduling for army logistics supply chains using metaheuristic algorithms", "year": 2026, "publication_date": "2026-08-12", "type": "article", "language": null, "authors": [ "Alusine Barrie" ], "first_author": "Alusine Barrie", "venue": "Global Journal of Engineering and Technology Advances", "publisher": "GSC Online Press", "abstract": "The increasing integration of Unmanned Aerial Vehicles (UAVs) into US Army logistics operations has introduced complex multi-objective scheduling challenges that conventional optimization methods struggle to address efficiently. This study presents a metaheuristic-based optimization framework for scheduling fleets of military UAVs tasked with last-mile resupply missions in dynamic, contested operational environments. A hybrid approach combining a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is proposed to minimize total mission completion time, fuel consumption, and operational risk while satisfying strict military delivery constraints. The model incorporates stochastic demand, no-fly-zone avoidance, payload capacity limitations, and UAV endurance parameters derived from US Army field logistics doctrine. Computational experiments were conducted on simulated battlefield scenarios with fleet sizes ranging from 5 to 50 UAVs across terrain grids representing forward operating bases. The proposed GA-ACO hybrid achieved an average improvement of 23.4% in mission completion time and 18.7% in fuel efficiency compared with single-algorithm baselines, while reducing the constraint violation rate to 1.8%. The framework also demonstrated superior adaptability to real-time route re-planning under dynamic threat scenarios and converged within the 10-minute Army tactical planning window for fleets of up to 50 UAVs. These findings suggest that hybrid metaheuristic optimization offers a robust and scalable approach to multi-UAV mission scheduling, with significant implications for enhancing Army supply-chain agility and operational readiness.", "keywords": [ "Scheduling (production processes)", "Metaheuristic", "Adaptability", "Job shop scheduling", "Robust optimization", "Ant colony optimization algorithms", "Supply chain", "Genetic algorithm", "Operational planning" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://gjeta.com/sites/default/files/fulltext_pdf/GJETA-2026-0206.pdf" }, { "id": "https://openalex.org/W7125941550", "doi": "10.5281/zenodo.18397859", "title": "A Fuzzy Multi Objective Optimization Model For A Sustainable And Competitive Uav Delivery Network Under Uncertainty", "year": 2026, "publication_date": "2026-01-28", "type": "article", "language": null, "authors": [ "Mehdi Shahegh", "Behnam Vahadani", "Farhad Eatebari" ], "first_author": "Mehdi Shahegh", "venue": "International Journal of Science Engineering and Technology", "publisher": null, "abstract": "This paper presents a fuzzy multi-objective optimization model for the Green Location-Routing Problem (GLRP) in competitive UAV delivery networks. The model simultaneously optimizes total cost, carbon emissions, and service time while determining facility locations and UAV routes under realistic constraints: capacity, energy, and no-fly zones. To address inherent uncertainties in customer demand and travel times, we develop a fuzzy logic framework that generates robust Pareto-optimal solutions for different confidence levels (α). The resulting fuzzy model is solved using three meta-heuristic algorithms: NSGA-II, MOACO, and MOSA. Numerical results demonstrate that the proposed fuzzy approach yields a more practical and cost-effective design compared to deterministic models, effectively balancing economic and environmental objectives under uncertainty. This study offers logistics managers a robust decision-support tool for sustainable UAV network deployment.", "keywords": [ "Fuzzy logic", "Fuzzy transportation", "Multi-objective optimization", "Robust optimization", "Fuzzy set", "Fuzzy control system", "Service (business)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18397859" }, { "id": "https://openalex.org/W7163643130", "doi": "10.3390/drones10060443", "title": "Time-Dependent Path Optimization for Vehicles and UAVs Under Urban Dynamic Traffic and Restricted Zones", "year": 2026, "publication_date": "2026-06-05", "type": "article", "language": "en", "authors": [ "Yuxuan Ji", "L Y Liu", "Yong Wang", "Xi Vincent Wang", "L N Wang" ], "first_author": "Yuxuan Ji", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "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.", "keywords": [ "Solver", "Robustness (evolution)", "Heuristics", "Baseline (sea)", "Genetic algorithm", "Traffic congestion", "Variable neighborhood search", "Robust optimization", "Metaheuristic", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10060443" }, { "id": "https://openalex.org/W3184500213", "doi": null, "title": "OPTIMIZING ENERGY EFFICIENT UAV ROUTING IN SUPPORT OF MARINE CORPS EXPEDITIONARY ADVANCED BASE OPERATIONS", "year": 2020, "publication_date": "2020-06-01", "type": "dissertation", "language": "en", "authors": [ "Adam Jatho" ], "first_author": "Adam Jatho", "venue": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "publisher": "Naval Postgraduate School", "abstract": "Resupplying future United States Marine Corps’ expeditionary advanced bases means developing resilient resupply methods. This thesis looks for a solution to reduce the risks associated with complex resupply operations, where troops and high-value equipment are exposed to the dangers of operations in a contested environment. Using unmanned aerial vehicles (UAVs), logistics missions can be conducted at greater efficiency and at lower risk to the force. This work addresses the problem of last-mile resupply using multiple autonomous UAVs. We develop an optimal UAV routing system, which creates the optimal energy-efficient flight paths for the UAVs between resupply nodes, accounting for changing wind conditions. The optimal minimum energy-trajectory generation (UAV flight path and velocity along the flight path) that connects each pair of nodes is based on the Pontryagin maximum principle. By minimizing energy expenditures required for flight, we increase UAV range and decrease the logistical resupply footprint in contested terrain. In order to minimize energy expenditures among multiple resupply nodes, we build on work done with a multiple-traveling salesmen problem to create optimal UAV delivery routes. While the general case of the optimal routing problem is not new, formulating this task as an optimal trajectory control problem, tied to the optimal routing of military logistics missions, increases the flexibility, agility and effectiveness of the Marine Corps.", "keywords": [ "Aeronautics", "Base (topology)", "Routing (electronic design automation)", "Operations research", "Engineering", "Base station", "Energy (signal processing)", "Marine engineering", "Computer science", "Telecommunications", "Embedded system", "Physics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10945/65554" }, { "id": "https://openalex.org/W4297324218", "doi": "10.36227/techrxiv.19365389.v3", "title": "Q-Learning Aided Intelligent Routing with Maximum Utility in Cognitive UAV Swarm for Emergency Communications", "year": 2022, "publication_date": "2022-09-27", "type": "preprint", "language": "en", "authors": [ "Long Zhang", "Xiaozheng Ma", "Zirui Zhuang", "Haitao Xu", "Vishal Sharma", "Zhu Han" ], "first_author": "Long Zhang", "venue": null, "publisher": null, "abstract": "In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.", "keywords": [ "Swarm behaviour", "Computer science", "Benchmark (surveying)", "Swarm intelligence", "Routing (electronic design automation)", "Metric (unit)", "Real-time computing", "Artificial intelligence", "Algorithm", "Particle swarm optimization", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36227/techrxiv.19365389.v3" }, { "id": "https://openalex.org/W4307099011", "doi": "10.7287/peerj-cs.1119v0.2/reviews/1", "title": "Peer Review #1 of \"Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning (v0.2)\"", "year": 2022, "publication_date": "2022-10-17", "type": "peer-review", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "Large-scale and complex mission environments require UAVs to deal with various types of missions while considering their operational and dynamic constraints.This paper proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV.We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP).Then, we solve this by using an attention-based deep reinforcement learning approach.Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP.For the input to the attentionbased neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors.Besides, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV.Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.", "keywords": [ "Reinforcement learning", "Computer science", "Aeronautics", "Artificial intelligence", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7287/peerj-cs.1119v0.2/reviews/1" }, { "id": "https://openalex.org/W7167077097", "doi": "10.1007/s44163-026-01684-4", "title": "Liquid neural network-based multi-UAV path planning in dynamic, obstacle-aware environments", "year": 2026, "publication_date": "2026-07-02", "type": "article", "language": "en", "authors": [ "Alparslan Güzey", "Mehmet Akif Çifçi", "Arda Yaşar Erdoğan", "Fazlı Yildirim" ], "first_author": "Alparslan Güzey", "venue": "Discover Artificial Intelligence", "publisher": "Springer Nature", "abstract": "Abstract Deploying multiple unmanned aerial vehicles (UAVs) for last-mile delivery in urban airspace requires decentralized path planning under dynamic constraints, including moving obstacles, intermittently active no-fly zones, variable wind, limited onboard energy, and multi-vehicle coordination. Classical planners provide strong routing performance in structured settings, but repeated replanning becomes increasingly burdensome in dynamic environments, whereas learned policies trained purely from demonstrations may suffer from covariate shift during closed-loop execution. We propose a decentralized planning framework in which each UAV is controlled by a compact Liquid Neural Network (LNN) trained via masked behavioral cloning from a coordinated A* oracle. The policy uses continuous-time recurrent dynamics with learnable per-neuron time constants, while legality masks enforce obstacle, airspace, and inter-agent safety constraints during both training and deployment. This design aims to combine reactive decentralized execution with a compact recurrent architecture that can smooth transient disturbances while remaining responsive to persistent environmental changes. The empirical study is conducted over a benchmark family that varies both grid size and fleet size, including compact $$10\\times 10$$ scenarios with 3–5 UAVs and enlarged $$20\\times 20$$ scenarios with 3–5 UAVs. This setup enables separate analysis of fleet scaling within a fixed environment and spatial scale-up at fixed team size. The results show that the proposed LNN policy performs strongly in compact-grid settings, where it consistently achieves the best non-oracle performance across the $$10\\times 10$$ scenarios. In enlarged-grid settings, however, all methods experience substantial degradation, accompanied by sharply increased battery-failure rates. These results indicate that larger-scale deterioration is driven not only by routing complexity, but also by growing energy-feasibility pressure as travel distances increase. Overall, the study suggests that liquid recurrent policies are a promising fit for compact-to-moderate decentralized multi-UAV routing under dynamic constraints, while also clarifying the limitations of the current framework in enlarged environments. The findings therefore provide both a positive assessment of LNN-based control in controlled routing regimes and a more explicit account of the conditions under which performance begins to deteriorate.", "keywords": [ "Motion planning", "Benchmark (surveying)", "Grid", "Variable (mathematics)", "Routing (electronic design automation)", "Vehicle dynamics", "Representation (politics)", "Robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s44163-026-01684-4" }, { "id": "https://openalex.org/W4307098982", "doi": "10.7287/peerj-cs.1119v0.1/reviews/2", "title": "Peer Review #2 of \"Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning (v0.1)\"", "year": 2022, "publication_date": "2022-10-17", "type": "peer-review", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "Large-scale and complex mission environments require UAVs to deal with various types of missions while considering their operational and dynamic constraints.This paper proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV.We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP).Then, we solve this by using an attention-based deep reinforcement learning approach.Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP.For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors.Besides, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV.Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.", "keywords": [ "Reinforcement learning", "Aeronautics", "Computer science", "Artificial intelligence", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://peerj.com/articles/cs-1119v0.2/submission" }, { "id": "https://openalex.org/W4307098986", "doi": "10.7287/peerj-cs.1119v0.1/reviews/1", "title": "Peer Review #1 of \"Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning (v0.1)\"", "year": 2022, "publication_date": "2022-10-17", "type": "peer-review", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "Large-scale and complex mission environments require UAVs to deal with various types of missions while considering their operational and dynamic constraints.This paper proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV.We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP).Then, we solve this by using an attention-based deep reinforcement learning approach.Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP.For the input to the attentionbased neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors.Besides, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV.Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.", "keywords": [ "Reinforcement learning", "Aeronautics", "Computer science", "Artificial intelligence", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7287/peerj-cs.1119v0.1/reviews/1" }, { "id": "https://openalex.org/W4307099009", "doi": "10.7287/peerj-cs.1119v0.2/reviews/2", "title": "Peer Review #2 of \"Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning (v0.2)\"", "year": 2022, "publication_date": "2022-10-17", "type": "peer-review", "language": "en", "authors": [], "first_author": null, "venue": null, "publisher": null, "abstract": "Large-scale and complex mission environments require UAVs to deal with various types of missions while considering their operational and dynamic constraints.This paper proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV.We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP).Then, we solve this by using an attention-based deep reinforcement learning approach.Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP.For the input to the attentionbased neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors.Besides, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV.Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.", "keywords": [ "Reinforcement learning", "Computer science", "Artificial intelligence", "Aeronautics", "Environmental science", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7287/peerj-cs.1119v0.2/reviews/2" }, { "id": "https://openalex.org/W4225563657", "doi": "10.36227/techrxiv.19365389.v2", "title": "Q-Learning Aided Intelligent Routing with Maximum Utility in Cognitive UAV Swarm for Emergency Communications", "year": 2022, "publication_date": "2022-04-06", "type": "preprint", "language": "en", "authors": [ "Long Zhang", "Xiaozheng Ma", "Zirui Zhuang", "Haitao Xu", "Vishal Sharma", "Zhu Han" ], "first_author": "Long Zhang", "venue": null, "publisher": null, "abstract": "In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.", "keywords": [ "Swarm behaviour", "Computer science", "Benchmark (surveying)", "Swarm intelligence", "Routing (electronic design automation)", "Metric (unit)", "Real-time computing", "Artificial intelligence", "Algorithm", "Particle swarm optimization", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/articles/preprint/Q-Learning_Aided_Intelligent_Routing_with_Maximum_Utility_in_Cognitive_UAV_Swarm_for_Emergency_Communications/19365389/2/files/34642829.pdf" }, { "id": "https://openalex.org/W4220722863", "doi": "10.36227/techrxiv.19365389.v1", "title": "Q-Learning Aided Intelligent Routing with Maximum Utility in Cognitive UAV Swarm for Emergency Communications", "year": 2022, "publication_date": "2022-03-17", "type": "preprint", "language": "en", "authors": [ "Long Zhang", "Xiaozheng Ma", "Zirui Zhuang", "Haitao Xu", "Vishal Sharma", "Zhu Han" ], "first_author": "Long Zhang", "venue": null, "publisher": null, "abstract": "In this paper, we attempt to deal with the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which applies the cognitive radio into a swarm of UAVs within a three-hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing with maximum utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric is determined by maximizing the utility, which jointly captures the achievable rate of UAV pair and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for the UAV. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Extensive simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm can significantly increase the accumulated rewards significantly with practical complexity compared to other benchmark schemes with fixed and decaying learning rates.", "keywords": [ "Swarm behaviour", "Computer science", "Benchmark (surveying)", "Swarm intelligence", "Routing (electronic design automation)", "Metric (unit)", "Real-time computing", "Artificial intelligence", "Algorithm", "Particle swarm optimization", "Computer network", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/articles/preprint/Q-Learning_Aided_Intelligent_Routing_with_Maximum_Utility_in_Cognitive_UAV_Swarm_for_Emergency_Communications/19365389/1/files/34392701.pdf" }, { "id": "https://openalex.org/W2323350926", "doi": "10.2514/6.2008-6646", "title": "Genetic Algorithms for Shortest Path Routing of Autonomous Gliders", "year": 2008, "publication_date": "2008-06-15", "type": "conference-paper", "language": "en", "authors": [ "Nazli E. Kahveci", "Pétros Ioannou" ], "first_author": "Nazli E. Kahveci", "venue": "AIAA Guidance, Navigation, and Control Conference and Exhibit", "publisher": null, "abstract": "Onboard power resources pose inevitable limitations on the flight performance of any aerial vehicle regardless of its type, size or mission. Popular soaring techniques recognized to be providing significant energy savings are primarily based on climbing air currents with upward trends called thermals. Intelligent routing strategies yet need to be developed in order to increase the autonomy of the vehicles applying soaring flight methods. One of the main concerns associated with the thermals is their physical nature which requires addressing non-stationary rising air currents potentially drifting in time. In such dynamic applications the performance of the routing schemes evaluated a priori deviates from the optimal one. If the changes on the thermal map are accommodated using online soaring algorithms, an emerging discussion would be focused on the extent to which the prohibitive computational costs of these routing strategies can be supported. In this paper we propose a path planning algorithm serving the purpose of energy efficient soaring route generation with the requirements formulated in the form of a Vehicle Routing Problem (VRP). We define the flight mission of the autonomous aerial vehicle as reaching the assigned destination in minimum time with no expenditure of an onboard power supply and interpret this formulation as Shortest Path (SP) routing. Genetic Algorithms (GAs) based on the natural evolution paradigm are used to reach near-optimal solutions. Accordingly, the thermal locations are represented by the genes on the chromosomes describing the feasible thermal soaring routes. A specific problem scenario is presented for an Unmanned Aerial Vehicle (UAV) where the flight path generated via GAs is compared to the solution reached by an available exact thermal soaring algorithm. Simulation results are provided to confirm the route reached by the GAs to be coinciding with the optimal route obtained by iterating the Floyd-Warshall (FW) Algorithm.", "keywords": [ "Computer science", "Shortest path problem", "K shortest path routing", "Path (computing)", "Routing (electronic design automation)", "Algorithm", "Theoretical computer science", "Computer network" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2008-6646" }, { "id": "https://openalex.org/W4389097756", "doi": "10.21203/rs.3.rs-3650701/v1", "title": "Trajectory Optimization of Unmanned Aerial Vehicles in theElectromagnetic Environment", "year": 2023, "publication_date": "2023-11-28", "type": "preprint", "language": "en", "authors": [ "Anvarbek Atayev", "Jörg Fliege", "Alain B. Zemkoho" ], "first_author": "Anvarbek Atayev", "venue": "Research Square", "publisher": null, "abstract": "Abstract We consider a type of routing problems common in defence and security, in which we control a fleetof unmanned aerial vehicles (UAVs) that have to reach one or more target locations without beingdetected by an adversary. Detection can be carried out by a variety of sensors (radio receivers, cameras,personnel, etc) placed by the adversary around the target sites. We model the act of detecting a UAVfrom first principles by noting that sensors work by monitoring frequencies in the electromagneticspectrum for signals or noise emitted. By this, we are able to provide a flexible and versatile nonlinearoptimisation framework in which the problem is modeled as a novel trajectory optimisation problemwith paths of the UAVs as continuous arcs in an Euclidean space. The flexibility of our approach isexhibited by the fact that we can easily consider various relevant objectives, among them minimisingthe overall probability of detection and maximising the location error that the adversary experienceswhen trying to locate our UAVs. Our model is also versatile enough to consider the act of jamming, inwhich one or more of our UAVs intentionally send out signals to interfere with the operations of theadversary’s sensors. Numerical results show the flexibility of our framework, and that we can solverealistic instances of this problem type.", "keywords": [ "Flexibility (engineering)", "Adversary", "Computer science", "Trajectory", "Jamming", "Noise (video)", "Trajectory optimization", "Variety (cybernetics)", "Real-time computing", "Artificial intelligence", "Computer security", "Image (mathematics)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-3650701/latest.pdf" }, { "id": "https://openalex.org/W4415450000", "doi": "10.21203/rs.3.rs-7807942/v1", "title": "Time-dependent vehicle-UAV path optimization for urban dynamic traffic restriction and no-fly zone", "year": 2025, "publication_date": "2025-10-23", "type": "preprint", "language": null, "authors": [ "Yuxuan Ji", "Linya Liu", "Yong Wang", "Xi Vincent Wang", "Lihui Wang" ], "first_author": "Yuxuan Ji", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Tardiness", "Robustness (evolution)", "Solver", "Energy consumption", "Convergence (economics)", "Scalability", "Scheduling (production processes)", "Traffic congestion", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-7807942/latest.pdf" }, { "id": "https://openalex.org/W4405714943", "doi": "10.48550/arxiv.2412.15537", "title": "Enhancing Large-scale UAV Route Planing with Global and Local Features via Reinforcement Graph Fusion", "year": 2024, "publication_date": "2024-12-20", "type": "preprint", "language": "en", "authors": [ "Tao Zhou", "Kai Ye", "Zeyu Shi", "Lin, Jiajing", "Dejun Xu", "Min Jiang" ], "first_author": "Tao Zhou", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Numerous remarkable advancements have been made in accuracy, speed, and parallelism for solving the Unmanned Aerial Vehicle Route Planing (UAVRP). However, existing UAVRP solvers face challenges when attempting to scale effectively and efficiently for larger instances. In this paper, we present a generalization framework that enables current UAVRP solvers to robustly extend their capabilities to larger instances, accommodating up to 10,000 points, using widely recognized test sets. The UAVRP under a large number of patrol points is a typical large-scale TSP problem.Our proposed framework comprises three distinct steps. Firstly, we employ Delaunay triangulation to extract subgraphs from large instances while preserving global features. Secondly, we utilize an embedded TSP solver to obtain sub-results, followed by graph fusion. Finally, we implement a decoding strategy customizable to the user's requirements, resulting in high-quality solutions, complemented by a warming-up process for the heatmap. To demonstrate the flexibility of our approach, we integrate two representative TSP solvers into our framework and conduct a comprehensive comparative analysis against existing algorithms using large TSP benchmark datasets. The results unequivocally demonstrate that our framework efficiently scales existing TSP solvers to handle large instances and consistently outperforms state-of-the-art (SOTA) methods. Furthermore, since our proposed framework does not necessitate additional training or fine-tuning, we believe that its generality can significantly advance research on end-to-end UAVRP solvers, enabling the application of a broader range of methods to real-world scenarios.", "keywords": [ "Reinforcement learning", "Fusion", "Graph", "Scale (ratio)", "Computer science", "Reinforcement", "Artificial intelligence", "Engineering", "Theoretical computer science", "Geography", "Structural engineering", "Cartography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2412.15537" }, { "id": "https://openalex.org/W7159988394", "doi": "10.1007/s10732-026-09591-8", "title": "A hybrid evolutionary approach for location routing of unmanned aerial vehicles in transmission tower inspection", "year": 2026, "publication_date": "2026-04-29", "type": "article", "language": "en", "authors": [ "Yongliang Lu", "Shaowen Lan", "Raymond Chiong" ], "first_author": "Yongliang Lu", "venue": "Journal of Heuristics", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Transmission (telecommunications)", "Transmission tower", "Routing (electronic design automation)", "Tower", "Drone", "Class (philosophy)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10732-026-09591-8" }, { "id": "https://openalex.org/W4410507467", "doi": "10.13164/re.2025.0342", "title": "An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network", "year": 2025, "publication_date": "2025-05-19", "type": "article", "language": "en", "authors": [ "Hanif Zafor", "Tasher Ali Sheikh", "Nabajyoti Mazumdar", "A. Nag" ], "first_author": "Hanif Zafor", "venue": "Radioengineering", "publisher": "Spolecnost pro radioelektronicke inzenyrstvi", "abstract": "Recently, unmanned aerial vehicles (UAVs) have become more popular due to their ease of adaptability and capability to carry out a variety of activities, including the delivery of services, monitoring and surveillance in military and civilian contexts.One of the most significant challenges in UAV operation is ensuring maximum network lifetime and management of their limited battery life.To solve these problems, we have proposed an effective routing algorithm that finds the best route to minimize UAV routing time and extend network lifetime.This is performed using the Ant Colony Optimization with Local Search (ACO-LS) algorithm for data collection from the clustered IoT network by UAV to ensure maximum network lifetime.It solved the routing problem in the minimum time in the presence of multiple charging stations and optimized the routing path.The simulation was carried out using various performance metrics: network lifetime (NT), energy consumption (EC), number of alive nodes (NAN), and packet delivery percentage (PDP).These parameters were compared with some existing algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and found that our proposed algorithm performs better in terms of higher NT, less EC, more NAN, and higher PDP than the existing algorithms ACO, PSO, and GA.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Hierarchical routing", "Routing algorithm", "Computer network", "Static routing", "Multipath routing", "Distributed computing", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13164/re.2025.0342" }, { "id": "https://openalex.org/W4416251662", "doi": "10.1109/ijcnn64981.2025.11228657", "title": "AMDRL: Simulation-Optimized UAV Network Routing in Knowledge-Defined Networking Using Graph Neural Network", "year": 2025, "publication_date": "2025-06-30", "type": "conference-paper", "language": null, "authors": [ "Jie Li", "Hongjun Ma", "Yinrui Yu", "Yuhan Zhang", "Xingwei Wang" ], "first_author": "Jie Li", "venue": null, "publisher": null, "abstract": "Due to the destruction of ground communication facilities, emergency communication networks are crucial for emergency rescue missions. UAV-Assisted Communication Network (UACN) is becoming a promising approach to establish emergency communication networks. However, the complexity and dynamics of UACN in disaster areas impose strict requirements on routing algorithms. To this end, this paper proposes an efficient routing scheme based on a double-layer UAV-assisted communication architecture to optimize the network performance of UACN and help emergency rescue missions to be completed efficiently. Specifically, we propose a double-layer UAV-assisted communication architecture based on the high coverage requirements of emergency scenarios and the delay-sensitive requirements of emergency rescue. Then, we propose an Attention mechanism-based Message passing Deep Reinforcement Learning (AMDRL) to optimize the routing problem in view of the fact that existing schemes rarely make full use of the information of the network environment. AMDRL uses the characteristics of graph neural network (GNN) to interact with the network topology environment and extract available information through the information passing process between topological links. Finally, we conduct simulation experiments in Knowledge-Defined Networking (KDN). The results show that the network performance of AMDRL is better than that of the baseline algorithm.", "keywords": [ "Network topology", "Routing (electronic design automation)", "Routing domain", "Telecommunications network", "Network architecture", "Routing protocol", "Routing table", "Policy-based routing", "Static routing", "Graph" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ijcnn64981.2025.11228657" }, { "id": "https://openalex.org/W7131371378", "doi": "10.1109/ccwc67433.2026.11393718", "title": "Mission-Aware Resource Allocation and Safe Trajectory Control for Multi-UAV Edge-Enabled Sensing Networks", "year": 2026, "publication_date": "2026-01-05", "type": "conference-paper", "language": null, "authors": [ "Abee Alazzwi", "Petro Mushidi Tshakwanda", "Henok Berhanu Tsegaye", "Michael Devetsikiotis", "Harsh Kumar" ], "first_author": "Abee Alazzwi", "venue": null, "publisher": null, "abstract": "We present HSPPO (Hierarchical Safe Proximal Policy Optimization), a mission-aware multi-agent reinforcement learning (MARL) framework that integrates resource allocation and safe trajectory control for cooperative unmanned aerial vehicles (UAVs) in edge-enabled sensing networks. Rather than introducing new barrier-function theory, HSPPO tightly couples discrete-time zeroing control barrier functions (ZCBFs) with PPO training by embedding safety filtering directly within rollouts and augmenting policy updates with a safety-imitation loss. This design aligns learning with formally safe actions while optimizing deadline-driven latency, energy consumption, and mission utility. Each UAV autonomously selects motion, missionzone association, and computation-communication splitting via a safety-constrained actor-critic, while a hierarchical structure combines a mission-aware scheduler with low-level controllers under centralized training and decentralized execution. We evaluate HSPPO in a simulation-based multi-UAV MEC environment across varying fleet sizes, mission-zone scales, priority distributions, and propagation conditions. In a nominal$1000 \\times 1000 ~\\mathrm{m}$scenario with six UAVs and eight mission zones, HSPPO achieves an 83.7% mission completion rate with only 0.5 safety violations per episode, yielding 96% fewer collisions than a greedy heuristic baseline. It further attains 0.72 normalized latency and 0.75 normalized energy, improving completion by 9.2 percentage points relative to PPO without safety (83.7% vs. 74.5%) and reducing latency and energy by approximately 15–16%. Ablation and scalability studies, including safety-parameter sweeps, fallback-mechanism variants, and mission-context removal, indicate that safety-in-the-loop MARL can enable reliable and deadline-compliant coordination for multi-UAV edge-sensing missions under realistic system constraints.", "keywords": [ "Scalability", "Reinforcement learning", "Resource allocation", "Trajectory", "Latency (audio)", "Embedding", "Scheduling (production processes)", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccwc67433.2026.11393718" }, { "id": "https://openalex.org/W4408037284", "doi": "10.1007/s00607-025-01442-x", "title": "A reliable multi-path cluster routing method for ground-air cooperative flying ad-hoc networks", "year": 2025, "publication_date": "2025-02-28", "type": "article", "language": "en", "authors": [ "Liming Liu", "Wenxuan Xu", "Yanling Shao", "Wenyong Dong", "Hairui Xu", "Hewen Gao", "Pingping Shan", "Junying Guo" ], "first_author": "Liming Liu", "venue": "Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Wireless ad hoc network", "Computer science", "Path (computing)", "Mobile ad hoc network", "Routing (electronic design automation)", "Cluster (spacecraft)", "Computer network", "Optimized Link State Routing Protocol", "Routing protocol", "Telecommunications" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s00607-025-01442-x" }, { "id": "https://openalex.org/W7204198125", "doi": "10.3390/drones10090645", "title": "Multi-Objective Trajectory Planning Method for Air–Ground Collaborative Logistics UAVs Under Preemptive Scheduling", "year": 2026, "publication_date": "2026-08-25", "type": "article", "language": "en", "authors": [ "Jian Deng", "Honghai Zhang", "Mingzhuang Hua", "Bingjie Liang" ], "first_author": "Jian Deng", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays.", "keywords": [ "Initialization", "Cuckoo search", "Benchmark (surveying)", "Scheduling (production processes)", "Population", "Multi-objective optimization", "Tardiness", "Pareto principle", "Job shop scheduling" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones10090645" }, { "id": "https://openalex.org/W7163506701", "doi": "10.2139/ssrn.6880778", "title": "Coordinated UAV-AGV scheduling in intralogistics under no-fly zones and time windows: MILP formulation and heuristic algorithms", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Rupesh Bade Shrestha", "Konstantinos Mykoniatis" ], "first_author": "Rupesh Bade Shrestha", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Heuristics", "Solver", "Scheduling (production processes)", "Scalability", "Workstation", "Linear programming", "Factory (object-oriented programming)", "Drone", "Energy consumption" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6880778" }, { "id": "https://openalex.org/W4323556051", "doi": "10.48550/arxiv.2303.02315", "title": "Optimizing Fuel-Constrained UAV-UGV Routes for Large Scale Coverage: Bilevel Planning in Heterogeneous Multi-Agent Systems", "year": 2023, "publication_date": "2023-03-04", "type": "preprint", "language": "en", "authors": [ "Md Safwan Mondal", "Ramasamy, Subramanian", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/8or8vz) underscores the effectiveness of our multi-agent approach.", "keywords": [ "Testbed", "Heuristics", "Computer science", "Unmanned ground vehicle", "Travelling salesman problem", "Routing (electronic design automation)", "Real-time computing", "Scale (ratio)", "Vehicle routing problem", "Simulation", "Artificial intelligence", "Embedded system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2303.02315" }, { "id": "https://openalex.org/W7162770523", "doi": "10.1016/j.comnet.2026.112428", "title": "Modeling and performance evaluation of DTN-integrated publish–subscribe middleware for UAV-assisted IoT: MQTT vs DDS", "year": 2026, "publication_date": "2026-05-29", "type": "article", "language": "en", "authors": [ "Akram Mebarkia", "Basem Almadani", "Farouq Muhammad Aliyu" ], "first_author": "Akram Mebarkia", "venue": "Computer Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "MQTT", "Middleware (distributed applications)", "Message queue", "Quality of service" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.comnet.2026.112428" }, { "id": "https://openalex.org/W7168125318", "doi": "10.3390/a19070570", "title": "An Adaptive Sliding-Window Ant Colony Optimization Approach for Multi-UAV Delivery Routing with Safety and Lateness Objectives", "year": 2026, "publication_date": "2026-07-11", "type": "article", "language": "en", "authors": [ "Xin-Yi Chen", "Lin Shi", "Jian-Yu Li" ], "first_author": "Xin-Yi Chen", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "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.", "keywords": [ "Ant colony optimization algorithms", "Routing (electronic design automation)", "Heuristic", "Adaptability", "Multi-objective optimization", "Ant colony", "Pareto principle", "Convergence (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1999-4893/19/7/570/pdf?version=1783761772" }, { "id": "https://openalex.org/W4401974447", "doi": "10.1115/1.4066259", "title": "Time-Critical Unified Rendezvous Guidance for an Unmanned Autonomous Vehicle", "year": 2024, "publication_date": "2024-07-01", "type": "article", "language": "en", "authors": [ "Abhinav Sinha", "Shashi Ranjan Kumar" ], "first_author": "Abhinav Sinha", "venue": "Journal of Autonomous Vehicles and Systems", "publisher": "ASM International", "abstract": "Abstract This paper addresses the time-critical rendezvous problem for a pursuing autonomous unmanned vehicle, e.g., an unmanned aerial vehicle (UAV), guided using the concept of true proportional-navigation guidance, which is a variant of proportional-navigation guidance. In existing vehicle routing and flight time-constrained guidance techniques, specific rendezvous guidance commands are designed based on the specific motion of the target. In contrast to that, we propose a unified guidance command for a UAV that guarantees a time-critical rendezvous with a target that moves arbitrarily. We explore the purview of true proportional-navigation guidance and posit that a guidance law thus designed may be a potential candidate for designing time-critical rendezvous strategies against various target motions, even when the pursuer does not necessarily have a speed advantage over the target. We first derive a closed-form expression for the flight duration until rendezvous, over which we exercise control to make the pursuing vehicle rendezvous with the target at any feasible time prescribed a priori. Next, we ensure that the necessary flight-time-based error variable converges to zero with an optimal convergence pattern with respect to a suitable cost function. We finally validate the efficacy of the proposed unified guidance command via numerical simulations.", "keywords": [ "Rendezvous", "Computer science", "Aeronautics", "Aerospace engineering", "Engineering", "Spacecraft" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1115/1.4066259" }, { "id": "https://openalex.org/W7149427512", "doi": "10.2139/ssrn.6510381", "title": "Trust-Aware UAV Resource Allocation for Disaster-Responsive IoMT Communication Networks via Adaptive Reinforcement-Assisted Learning", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Omer Chughtai", "Muhammad Naeem", "Muhammad Khalil Afzal", "Ghazala Rafiq", "Ali Hamdan Alenezi" ], "first_author": "Omer Chughtai", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Probabilistic logic", "Reinforcement learning", "Latency (audio)", "Reliability (semiconductor)", "Scalability", "Integer programming", "Quality of service", "Resource allocation", "Linear programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6510381" }, { "id": "https://openalex.org/W4417521683", "doi": "10.48550/arxiv.2504.21111", "title": "How to Coordinate UAVs and UGVs for Efficient Mission Planning? Optimizing Energy-Constrained Cooperative Routing with a DRL Framework", "year": 2025, "publication_date": "2025-04-29", "type": "preprint", "language": "en", "authors": [ "Md Safwan Mondal", "Ramasamy, Subramanian", "Russo, Luca", "James D. Humann", "James M. Dotterweich", "Pranav A. Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Efficient mission planning for cooperative systems involving Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) requires addressing energy constraints, scalability, and coordination challenges between agents. UAVs excel in rapidly covering large areas but are constrained by limited battery life, while UGVs, with their extended operational range and capability to serve as mobile recharging stations, are hindered by slower speeds. This heterogeneity makes coordination between UAVs and UGVs critical for achieving optimal mission outcomes. In this work, we propose a scalable deep reinforcement learning (DRL) framework to address the energy-constrained cooperative routing problem for multi-agent UAV-UGV teams, aiming to visit a set of task points in minimal time with UAVs relying on UGVs for recharging during the mission. The framework incorporates sortie-wise agent switching to efficiently manage multiple agents, by allocating task points and coordinating actions. Using an encoder-decoder transformer architecture, it optimizes routes and recharging rendezvous for the UAV-UGV team in the task scenario. Extensive computational experiments demonstrate the framework's superior performance over heuristic methods and a DRL baseline, delivering significant improvements in solution quality and runtime efficiency across diverse scenarios. Generalization studies validate its robustness, while dynamic scenario highlights its adaptability to real-time changes with a case study. This work advances UAV-UGV cooperative routing by providing a scalable, efficient, and robust solution for multi-agent mission planning.", "keywords": [ "Rendezvous", "Scalability", "Adaptability", "Reinforcement learning", "Routing (electronic design automation)", "Task (project management)", "Set (abstract data type)", "Heuristic" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2504.21111" }, { "id": "https://openalex.org/W7164052381", "doi": "10.1109/jiot.2026.3701781", "title": "A Digital Twin-Driven Combinatorial Neural Bandit Approach for Multihop UAV Route Optimization in Delay--Doppler-Aware FANETs", "year": 2026, "publication_date": "2026-06-09", "type": "article", "language": null, "authors": [ "Ehab Mahmoud Mohamed", "Mostafa M. Fouda" ], "first_author": "Ehab Mahmoud Mohamed", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper proposes a centralized digital twin (DT)–driven framework for end-to-end route optimization in flying ad hoc networks (FANETs) that explicitly accounts for channel dynamics in delay-Doppler (DD) domain and residual unmanned aerial vehicles (UAVs)’ energy states. A DT server maintains a high-fidelity virtual representation of the physical FANET using compact DD channel descriptors and UAVs’ energy information, thereby reducing communication overhead while enabling proactive best route selection. In this context, the UAV routing problem is formulated as a centralized context-aware combinatorial bandit. The DT server will utilize combinatorial neural Thompson sampling (CN-TS) or combinatorial neural upper confidence bound (CN-UCB) algorithms to learn the non-linear relationship between UAVs’ dynamics and the achievable end-to-end transmitted data for selecting the optimal multi-hop route sequentially. Numerical results demonstrate that the proposed DT framework significantly outperforms conventional approaches in highly dynamic FANET scenarios.", "keywords": [ "Artificial neural network", "Algorithm design", "Optimization problem", "Combinatorial optimization", "Minification" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2026.3701781" }, { "id": "https://openalex.org/W7171489876", "doi": "10.3390/aerospace13080673", "title": "A Multi-Objective Optimization Model for Collaborative UAV–Rider Meal Delivery Routing Using an Enhanced NSGA-II Algorithm", "year": 2026, "publication_date": "2026-07-28", "type": "article", "language": "en", "authors": [ "Shichen Hu", "Yuan Zheng", "Jingqi Dai", "Hui Jiang", "Yiwu Feng" ], "first_author": "Shichen Hu", "venue": "Aerospace", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Urban instant-delivery platforms increasingly require efficient, punctual, and low-carbon delivery services. Unmanned aerial vehicles (UAVs) can reduce dependence on congested ground traffic in meal delivery and further improve overall delivery efficiency through coordinated operations with ground riders at rendezvous points. However, existing studies mainly focus on ground-based routing or simplify UAV-assisted delivery as a single-objective problem, limiting their ability to balance cost, completion time, carbon emissions, and service quality. To address this limitation, this paper investigates a collaborative UAV–rider meal delivery routing problem and formulates a multi-objective optimization model integrating restaurant pickup, UAV transfer, rider last-mile delivery, and soft customer time windows. An Enhanced NSGA-II algorithm is then developed, where hybrid initialization improves solution quality and diversity, adaptive operators balance exploration and exploitation, local search refines route structures, structural repair maintains feasibility, and diversity preservation supports a well-distributed Pareto front. Comparative experiments against NSGA-II, MOPSO, NSGA-III, MOEA/D, MODE, and IMODE, together with scalability, ablation, sensitivity, and case analyses, show that E-NSGA-II provides stronger Pareto-front approximation. The results support its use as a decision-support method for service-aware and low-carbon UAV–rider meal delivery, while also revealing additional computational cost.", "keywords": [ "Initialization", "Routing (electronic design automation)", "Vehicle routing problem", "Probabilistic logic", "Limiting", "Multi-objective optimization", "Service (business)", "Focus (optics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/aerospace13080673" }, { "id": "https://openalex.org/W7214182430", "doi": "10.21203/rs.3.rs-10941530/v1", "title": "Certified-Robust Multi-Objective UAV Routing for Military Resupply under Nonlinear Energy Dynamics: An Adaptive Physics-Informed Approach", "year": 2026, "publication_date": "2026-09-24", "type": "preprint", "language": null, "authors": [ "Karim Benalia", "Karim Beddek", "Melissa Mansouri", "Brahim Oukacha" ], "first_author": "Karim Benalia", "venue": "Research Square", "publisher": null, "abstract": null, "keywords": [ "Scheduling (production processes)", "Benchmark (surveying)", "Nonlinear system", "Schedule", "Routing (electronic design automation)", "Energy (signal processing)", "Energy consumption", "Linear programming", "Recursion (computer science)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.researchsquare.com/article/rs-10941530/latest.pdf" }, { "id": "https://openalex.org/W4385779992", "doi": "10.31223/x5q95x", "title": "Marine Cloud Brightening: an airborne concept", "year": 2023, "publication_date": "2023-08-12", "type": "preprint", "language": "en", "authors": [ "Christian G. Claudel", "Andrew John Lockley", "Fabian Hoffmann", "Younan Xia" ], "first_author": "Christian G. Claudel", "venue": null, "publisher": null, "abstract": "Marine cloud brightening (MCB) is a proposed solar radiation modification (SRM) geoengineering technique to enhance marine boundary layer (MBL) cloud albedo. Extant proposals consider 10,000-100,000 autonomous ships spraying seawater, generating and dispersing sea salt nanoparticles. Alternatively, this paper proposes industrially manufacturing NaCl nanoparticles using ethanol anti-solvent brine precipitation. With desiccation, size optimization and narrowed size distribution, aerosol mass flux reduces by 500x (17x for the dry mass flux). This facilitates Unmanned Aerial Vehicle delivery (e.g. MQ-9 Reaper UAV). Increased speed, altitude and wake turbulence improves areal coverage per vehicle vs. ships - reducing fleet size. Utilizing extant airframe designs improves vehicle technology readiness level (TRL) - potentially improving system operational cost (est. $40B/year) and lead time. This approach further reduces energy requirements (5x less), technical risk and system complexity. Increased readiness amplifies proliferation risk - particularly for inexpensive regional heatwave and hurricane suppression - making governance more urgent.", "keywords": [ "Environmental science", "Extant taxon", "Meteorology", "Aerosol", "Atmospheric sciences", "Geology", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://eartharxiv.org/repository/object/5789/download/11306/" }, { "id": "https://openalex.org/W4399608235", "doi": "10.1061/9780784485521.004", "title": "Vehicle Miles Traveled and Environmental Impacts from On-Demand Delivery: A Literature Review", "year": 2024, "publication_date": "2024-06-13", "type": "conference-paper", "language": "en", "authors": [ "Haishan Liu", "Hao Peng", "Shams Tanvir", "Anurag Pande", "Matthew Barth" ], "first_author": "Haishan Liu", "venue": null, "publisher": null, "abstract": "The boom of e-commerce and the increasing demand for fast and reliable delivery services have led to the thriving of on-demand delivery (ODD), which provides delivery services to food takeout, grocery, pharmacy, and other light-weighted goods. The operational efficiency of ODD is subject to many factors—access to curbside, delays at the pick-up and drop-off locations, order dispatching mode, vehicle routing schedule, and vehicle refueling needs. The fast-growing delivery orders coupled with operational inefficiencies of ODD may lead to higher vehicle miles traveled (VMT) and pollutant emissions. Policymakers as well as practitioners need to evaluate the VMT and emissions impact of ODD, given the consumer behavior, operational paradigm, and business models. This paper conducted a systematic review of the existing literature to synthesize and summarize the impacts of ODD with a specific focus on VMT and emissions. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline was employed to systematically search for related studies in multiple databases and to crystallize the review scope. The impact evaluation was delved into three aspects: customer shopping behavior (online shopping vs. in-store shopping), ODD operational strategy (truck/van vs. green vehicles, professional delivery vs. crowdsourcing), and business models (home delivery vs. depot/collection point). Overall, this study found that online shopping with coordinated ODD can achieve significant VMT and emissions reduction compared with in-store shopping. The reduction extent depends on the customer trip chaining, travel mode choice, residential area type, and the ratio of product return. The use of zero-emissions vehicles in ODD, such as electric van/truck/vehicle, cargo-bike, UAV, provides relatively higher emissions reductions, but also brings new issues such as charging needs or capacity limits. Collection points (e.g., parcel locker, retailer store, postal service point) can reduce the VMT and emissions if they are optimally distributed, and customers visit them in zero-emissions modes or through trip chaining.", "keywords": [ "Computer science" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1061/9780784485521.004" }, { "id": "https://openalex.org/W4406028184", "doi": "10.1109/jiot.2025.3525985", "title": "Adaptive Probabilistic Planning for the Uncertain and Dynamic Orienteering Problem", "year": 2025, "publication_date": "2025-01-03", "type": "article", "language": "en", "authors": [ "Qiuchen Qian", "Yanran Wang", "David Edward Boyle" ], "first_author": "Qiuchen Qian", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The orienteering problem (OP) is a well-studied routing problem that has been extended to incorporate uncertainties, reflecting stochastic or dynamic travel costs, prize-collection costs, and prizes. Existing approaches may, however, be inefficient in real-world applications due to insufficient modeling knowledge and initially unknowable parameters in online scenarios. Thus, we propose the uncertain and dynamic OP (UDOP), modeling travel costs as distributions with unknown and time-variant parameters. UDOP also associates uncertain travel costs with dynamic prizes and prize-collection costs for its objective and budget constraints. To address UDOP, we develop an Adaptive Approach for Probabilistic paths (ADAPT), iteratively performing “execution” and “online planning” based on an initial “offline” solution. The execution phase updates the system status and records online cost observations. The online planner employs a Bayesian approach to adaptively estimate power consumption and optimize path sequence based on safety beliefs. We evaluate ADAPT in a practical autonomous aerial vehicle charging scheduling problem (CSP) for wireless rechargeable sensor networks (WRSNs). The UAV must optimize its path to recharge sensor nodes efficiently while managing its energy under uncertain conditions. ADAPT maintains comparable solution quality and computation time while offering superior robustness. Extensive simulations show that ADAPT achieves a 100% mission success rate (MSR) across all tested scenarios, outperforming comparable heuristic-based and frequentist approaches that fail up to 70% (under challenging conditions) and averaging 67% MSR, respectively. This work advances the field of OP with uncertainties, offering a reliable and efficient approach for real-world applications in uncertain and dynamic environments.", "keywords": [ "Orienteering", "Computer science", "Probabilistic logic", "Mathematical optimization", "Artificial intelligence", "Mathematics" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jiot.2025.3525985" }, { "id": "https://openalex.org/W7203530818", "doi": "10.2139/ssrn.7288403", "title": "Voxel-Level 3D Low-Altitude UAV Routing Planning under Multi-Physics: A Dual-Layer Decoupling Framework with Diffusion Model", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Yanqiao Feng", "liang Tan", "yong Peng", "Dennis Yu" ], "first_author": "Yanqiao Feng", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Terrain", "Decoupling (probability)", "Routing (electronic design automation)", "Greedy algorithm", "Aerodynamics", "Vehicle routing problem", "Flight planning", "Acceleration" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7288403" }, { "id": "https://openalex.org/W7172449836", "doi": "10.1109/jstars.2026.3720481", "title": "LLM-Augmented Spatiotemporal Occupancy-Aware Multi-UAV Scheduling Using OSM-Derived Geospatial Data for Smart-City Last-Mile Delivery", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Israr Ahmad", "Shang Fengjun", "Altaf Hussain", "Tariq Hussain", "Razaz Waheeb Attar", "Amal Hassan Alhazmi" ], "first_author": "Israr Ahmad", "venue": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Urban last-mile delivery with multi-UAV fleets is constrained not only by travel distance, but also by temporary landing-exclusion zones created during parcel drop-off. When several UAVs serve nearby delivery points, these exclusion zones can overlap in space and time, forcing hover waiting and increasing fleet makespan. A further challenge is that rooftop, ground-open, and constrained-corridor deliveries impose different landing radii and occupancy durations, while many scheduling models treat delivery points uniformly. This article proposes an OpenStreetMap (OSM) and large language model (LLM)-augmented spatiotemporal occupancy-aware multi-UAV scheduling framework for smart-city last-mile delivery. OSM building footprints, road networks, and open-space polygons are used to classify delivery points into three landing-zone types, after which representative landing-mode parameters are assigned for exclusion-aware scheduling. An LLM-based adaptive weighting mechanism (gpt-4o-mini-2024-07-18, $T=0$ ) maps zone statistics, conflict density, and urban-morphology cues to priority weights for occupancy-, distance-, and conflict-aware task ordering. Experiments on Tokyo and Shenzhen evaluate 24 configurations across two cities, three fleet sizes, four scheduling strategies, and ten random seeds. The proposed framework reduces makespan by more than 90% relative to a sequential single-trip upper bound reference, confirming the benefit of parallel fleet scheduling under landing-exclusion constraints. Against non-LLM heuristics, the LLM-Adaptive strategy remains competitive and achieves the best result in the resource-constrained Shenzhen 12-UAV case. On a reduced ten-task mixed integer linear programming benchmark solved with CPLEX 22.1 to a gap of at most 0.1%, the LLM-Adaptive strategy closes 4.3%–5.2% of the optimality gap left by the best hand-tuned heuristic at $K=3$ . The results show that OSM-derived zone classification and LLM-based priority weighting provide an interpretable and reproducible approach for exclusion-aware multi-UAV scheduling.", "keywords": [ "Geospatial analysis", "Scheduling (production processes)", "Processor scheduling", "Data modeling", "Distributed database" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/jstars.2026.3720481" }, { "id": "https://openalex.org/W2151194463", "doi": "10.1109/eit.2007.4374507", "title": "Dynamic network refinement in automated aircraft route planning", "year": 2007, "publication_date": "2007-05-01", "type": "conference-paper", "language": "en", "authors": [ "Michael Hennebry", "Kuodi Jian", "Kendall E. Nygard" ], "first_author": "Michael Hennebry", "venue": null, "publisher": null, "abstract": "We describe a routing procedure for unmanned aircraft called Dynamic Resolution Refinement (DRR). This new procedure is based on a variation of the A∗ search algorithm and uses space tessellations. We call this variation search procedure A∗’. The routing problem of interest concerns path planning for an unmanned air vehicle (UAV). The benefit of DRR is that it successively improves the accuracy of the path calculation, and also allows a planner to dynamically choose among multiple near optimal routes. This capability reduces the risk of an aircraft being shot down. Since the DRR starts with coarse resolution, the setup time is short, which is important in mission critical applications. The DRR finds a near optimal solution quickly and works well in a resource bound environment. In such an environment, a uniform fine resolution approach would either take too long to find a solution or find a poor solution.", "keywords": [ "Computer science", "Systems engineering", "Engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/eit.2007.4374507" }, { "id": "https://openalex.org/W2593728881", "doi": null, "title": "Path planning for persistent surveillance applications using fixed-wing unmanned aerial vehicles", "year": 2016, "publication_date": "2016-01-01", "type": "dissertation", "language": "en", "authors": [ "James F. Keller" ], "first_author": "James F. Keller", "venue": "ScholarlyCommons (University of Pennsylvania)", "publisher": "University of Pennsylvania", "abstract": "This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles\\n(UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission,\\nfixed wing vehicles have maneuver constraints that can limit their performance in this role.\\nCurrent technology vehicles are capable of long duration flight with a minimal acoustic\\nfootprint while carrying an array of cameras and sensors. Both military tactical and civilian\\nsafety applications can benefit from this technology. We make three main contributions:\\nC1 A sequential path planner that generates a C2 flight plan to persistently acquire a\\ncovering set of data over a user designated area of interest. The planner features the\\nfollowing innovations:\\n• A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length\\n• A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction\\n• A smooth path generator that provides C2 routes that satisfy user specified curvature constraints\\nC2 A set of algorithms to coordinate multiple UAVs, including mission commencement\\nfrom arbitrary locations to the start of a coordinated mission and de-confliction of\\npaths to avoid collisions with other vehicles and fixed obstacles\\niv\\nC3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing\\nvalidated through flight test experiments on multiple platforms. A variety of tests\\nand platforms are discussed.\\nThe algorithms presented are based on a technical approach with approximately equal\\nemphasis on analysis, computation, dynamic simulation, and flight test experimentation.\\nOur planner (C1) directly takes into account vehicle maneuverability and agility constraints\\nthat could otherwise render simple solutions infeasible. This is especially important when\\nsurveillance objectives elevate the importance of optimized paths. Researchers have devel\\noped a diverse range of solutions for persistent surveillance applications but few directly\\naddress dynamic maneuver constraints.\\nThe key feature of C1 is a two stage sequential solution that discretizes the problem so that\\ngraph search techniques can be combined with parametric polynomial curve generation.\\nA method to abstract the kino-dynamics of the aerial platforms is then presented so that\\na graph search solution can be adapted for this application. An A* Traveling Salesman\\nProblem (TSP) algorithm is developed to search the discretized space using the abstract\\ndistance metric to acquire more data or avoid obstacles. Results of the graph search are\\nthen transcribed into smooth paths based on vehicle maneuver constraints. A complete\\nsolution for a single vehicle periodic tour of the area is developed using the results of the\\ngraph search algorithm. To execute the mission, we present a simultaneous arrival algorithm\\n(C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and\\nto ensure there are no collisions at any of the path intersections.\\nWe present a toolbox of spline-based algorithms (C3) to streamline the development of C2\\ncontinuous paths with numerical stability. These tools are applied to an aerial persistent\\nsurveillance application to illustrate their utility. Comparisons with other parametric poly\\nnomial approaches are highlighted to underscore the benefits of the B-spline framework.\\nPerformance limits with respect to feasibility constraints are documented.", "keywords": [ "Fixed wing", "Motion planning", "Computer science", "Path (computing)", "Drone", "Aeronautics", "Real-time computing", "Remote sensing", "Artificial intelligence", "Geography", "Wing", "Aerospace engineering" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.upenn.edu/handle/20.500.14332/29293" }, { "id": "https://openalex.org/W7214421937", "doi": "10.2139/ssrn.7529191", "title": "Joint Transport–Relay Co-Design for UAV Emergency Logistics in Mountainous Terrain: Handover-Aware Feasibility, Channel-Model Sensitivity, and the Price of Partitioning", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Changxuan Cao", "qiong li", "zexu ouyang", "yulong peng", "Changxuan Cao" ], "first_author": "Changxuan Cao", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Set (abstract data type)", "Bottleneck", "State (computer science)", "Terrain", "Benchmark (surveying)", "Schedule", "Constant (computer programming)", "Lagrangian relaxation", "Upper and lower bounds" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.7529191" }, { "id": "https://openalex.org/W4415243154", "doi": "10.63544/ijss.v4i4.183", "title": "Logistics Entrepreneurship in the Digital Era: Opportunities, Challenges, and Growth Models in Smart Warehousing and Last-Mile Delivery", "year": 2025, "publication_date": "2025-10-14", "type": "article", "language": "en", "authors": [ "Asad A. Ali", "Sikandar Nawab Khattak", "Akbar Ali Rabbani", "Maisam Hussian", "Abdul Mateen" ], "first_author": "Asad A. Ali", "venue": "Inverge Journal of Social Sciences", "publisher": null, "abstract": "The study explored the evolving dynamics of logistics entrepreneurship in the digital era, emphasizing how deep technological integration has fundamentally reshaped smart warehousing and last-mile delivery systems. The primary aim was to examine how digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and automation, contributed to operational efficiency, innovation, and business growth among logistics enterprises. Using a mixed-method approach combining quantitative surveys and qualitative interviews, data were collected from a diverse sample of logistics firms and technology-driven entrepreneurs across multiple regions. The results revealed that digital transformation significantly enhanced productivity, supply chain transparency, and customer satisfaction, with predictive analytics enabling more proactive decision-making. Moreover, innovation capability and technology adoption served as key mediating factors linking robust digital infrastructure to superior entrepreneurial performance. However, significant challenges such as high implementation costs, data privacy risks, and limited technical expertise constrained widespread adoption, particularly among small and medium enterprises. The study concluded that successful logistics entrepreneurship in the digital era depended on a balanced strategy of targeted technological investment, continuous human capital development, and a strong sustainability orientation. Recommendations included promoting public-private partnerships for digital literacy, establishing robust cybersecurity frameworks, and incentivizing eco-efficient logistics practices. The findings provided critical theoretical and practical insights into how agile digital entrepreneurship models could guide the future growth and resilience of logistics industries globally. References Alfalla-Luque, R., Marín-García, J. A., & Medina-López, C. (2023). Digital transformation and logistics performance: The mediating role of innovation capability. International Journal of Logistics Research and Applications, 26(4), 712–730. https://doi.org/10.1080/13675567.2022.2054309 Asif, M., & Sandhu, M. S. (2023). Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance. Journal of Business Insight and Innovation, 2(2), 67-77. Asif, M., Shah, H., & Asim, H. A. H. (2025). Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context. Journal of Asian Development Studies, 14(3), 560-573. Bai, C., & Sarkis, J. (2024). Blockchain technology and sustainable supply chain management: A logistics perspective. Transportation Research Part E: Logistics and Transportation Review, 183, 103707. https://doi.org/10.1016/j.tre.2023.103707 Business Insider. (2025, July). The supply chain’s last mile is complex and expensive. AI has the potential to fix its woes. Business Insider. Chowdhury, P., Paul, S. K., & Moktadir, M. A. (2023). Resilient and sustainable last-mile logistics using digital technologies. Journal of Cleaner Production, 405, 136903. https://doi.org/10.1016/j.jclepro.2023.136903 de Leeuw, J., Bukhsh, Z., & Zhang, Y. (2023). Parcel loss prediction in last-mile delivery: Deep and non-deep approaches with insights from explainable AI. arXiv. https://doi.org/10.48550/arXiv.2310.16602 Fernández, R., & Nieto, M. J. (2023). Digital entrepreneurship in logistics SMEs: The role of technological capabilities and innovation orientation. Technological Forecasting and Social Change, 194, 122612. https://doi.org/10.1016/j.techfore.2023.122612 Gong, Y., Zhang, X., & Liu, Y. (2024). Smart warehousing and logistics innovation under the digital economy. Computers & Industrial Engineering, 187, 109915. https://doi.org/10.1016/j.cie.2023.109915 Jantapoon, K. (2025). The impact of smart warehousing and last-mile delivery on e-commerce supply chain performance: An empirical study using machine learning-enhanced SEM analysis. International Journal of Analysis and Applications, 23(101). Khan, S., Zaman, Z. I., Shiekh, A. A., & Saeed, M. (2025). Smart warehousing in FMCG sector: Challenges and remedies. In Smart Supply Chain Management (pp. 229–247). Springer. Li, F., Yang, X., Zhu, R., Li, T., & Liu, J. (2025). Growth mechanism in transformation and upgrading of logistics industry. Systems, 13(3), 202. https://doi.org/10.3390/systems13030202 Li, J., & Wu, D. (2024). The integration of AI and IoT in optimizing warehouse and delivery systems. International Journal of Production Economics, 267, 109022. https://doi.org/10.1016/j.ijpe.2023.109022 Malik, S., Khonji, M., Elbassioni, K., & Dias, J. (2025). Collaborative last-mile delivery: A multi-platform vehicle routing problem with en-route charging. arXiv. https://doi.org/10.48550/arXiv.2505.23584 Mandal, M. P., & Archetti, C. (2023). A decomposition approach to last mile delivery using public transportation systems. arXiv. https://doi.org/10.48550/arXiv.2306.04219 Moradi, N., Mirzavand Boroujeni, N., Aftabi, N., & Aslani, A. (2024). Two-echelon electric vehicle routing problem in parcel delivery: A literature review. arXiv. Müller, J. M., Buliga, O., & Voigt, K. I. (2023). The role of digital transformation in logistics entrepreneurship: Implications for performance and competitiveness. Industrial Marketing Management, 112, 125–138. https://doi.org/10.1016/j.indmarman.2023.01.009 Pereira, C. R., Da Silva, A. L., & Maffei, M. J. (2024). Predictive analytics and automation for smart logistics performance: Evidence from global logistics firms. Journal of Business Research, 172, 114093. https://doi.org/10.1016/j.jbusres.2023.114093 Rafiq-uz-Zaman, M. (2025). Beyond the blackboards: Building a micro-edtech economy through teacher-led innovation in low-income schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M. (2025). Bridging the skills divide: A comparative study of skill-base", "keywords": [ "Digital transformation", "Agile software development", "Entrepreneurship", "Supply chain", "Digital strategy", "Ambidexterity", "Big data", "Business model", "Sample (material)", "Dynamic capabilities" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://invergejournals.com/index.php/ijss/article/download/183/338" }, { "id": "https://openalex.org/W2791658916", "doi": "10.17825/klr.2017.27.6.193", "title": "The Fourth Industrial Revolution: :- An Exploratory Study on Main Technologies and Supply Chain Management -", "year": 2017, "publication_date": "2017-12-13", "type": "article", "language": "en", "authors": [ "Chang Kun Chung" ], "first_author": "Chang Kun Chung", "venue": "한국물류학회지", "publisher": null, "abstract": "본 연구는 빠르게 진행되고 있는 4차 산업혁명의 주요 기술이 공급사슬관리의 새로운 연구에 직접적인 동기를 제공하고, 학계에 새로운 연구의 기회를 여는 것을 목적으로 한다. 경쟁우위를 창출하는 공급사슬관리에서 정보통신기술의 긍정적인 역할을 다룬 선행연구에서 효율성 증대 및 비용 감소의 긍정적인 영향을 확인하고, 공급사슬관리 산업의 최근 연구 방향의 추세를 살펴보면서 회사 내부의 공급사슬관리의 시스템적인 연구, 회사 외부의 공급사슬관리의 협력적인 연구 그리고 여러 문제를 한꺼번에 동시에 해답을 발견하는 최적화 모형 및 시뮬레이션을 통한 역동적인 연구 방향을 통해서 주요 기술의 영향을 살펴본다. 그리고 4차 산업혁명의 주요한 기술을 활용한 공급사슬관리의 전략적인 활동과 운영적인 활동을 탐색한다. 주요 기술 관련 공급사슬 연구에 대한 시사점을 논의한다. 4차 산업혁명의 주요 기술이 광범위하게 확대 적용되어서 생산성이 증대하고 비용을 감소하면서 효율성을 증대시키고 고객 요구에 따른 온 디맨드(on demand)생산과 고객에게 편의성과 서비스를 제공하는 효과를 논의할 수 있다. 클라우드 환경에서 빅 데이터를 이용하고, 사물인터넷(IoT), 로봇과 드론 및 인공지능 활용으로 공급사슬관리의 역동적인 연구의 최적화 연구 그리고 시뮬레이션 연구가 더욱 활발하고 기업의 의사결정에 크게 기여할 것으로 예상된다. 공급사슬 관련 4차 산업혁명의 기술의 진화가 연구와 기업에 결정적인 변화를 제공할 것으로 예측한다.This research focuses on the exploratory study of main technologies of the fourth industrial revolutions and supply chain management. Main technologies of the fourth industrial revolutions include CPS, IoT and Sensor, Big data, Cloud based computing, 3D printing and additive manufacturing, robotics, drones and Artificial Intelligence, Blockchain. The concept of the fourth industrial revolutions, namely industry 4.0 is defined and the current status on industry 4.0 is reviewed with Germany, United States of America, China, Japan including Korea. Previous researches on Supply Chain Management have been reviewed with supply chain management and competitive advantage based on adaptation, alignment and agility, three directions of systematic` direction, collaborative direction and dynamic direction according to summary of results in researches. In the future, new researches on Supply Chain Management may be opened and enhanced with main technologies of the fourth industrial revolutions, specially with real time data, quickly changing and updated data, Big data analytics and advanced techniques of big data based on cloud computing. Specially, Big data analytics on Supply Chain Management may make important decisions on strategic sourcing, product design and development. And at technical level and operational level, optimization, simulation and other technical methodologies may be used for making important decisions on multi echelon problems, inventory-routing problems, demand and supply forecasting with inventory and production. The implications are discussed and the future research issues are generated and the conclusions note that main technologies of the fourth industrial revolution may open a large of amounts on new operations and research projects related to Supply Chain Management. And a future research will be done with the comparison between introducing costs and operating costs of main technologies of the fourth industrial revolution and business performance.", "keywords": [ "Supply chain management", "Business", "Industrial Revolution", "Supply chain", "Process management", "Operations management", "Engineering management", "Engineering", "Political science", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17825/klr.2017.27.6.193" }, { "id": "https://openalex.org/W4239627302", "doi": "10.1016/0377-2217(94)90322-0", "title": "Editorial Board", "year": 1994, "publication_date": "1994-01-01", "type": "paratext", "language": "en", "authors": [], "first_author": null, "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Operations research", "Point (geometry)", "Routing (electronic design automation)", "Genetic algorithm", "Mathematical optimization", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": true, "open_access_url": "https://doi.org/10.1016/0377-2217(94)90322-0" }, { "id": "https://openalex.org/W4411206441", "doi": "10.1109/ichora65333.2025.11016972", "title": "The Impact of Aerial Vehicle Quantity and Battery Capacity on Task Route Planning in Multi-Hybrid Systems", "year": 2025, "publication_date": "2025-05-23", "type": "conference-paper", "language": "en", "authors": [ "Sukur Mehraliyeva", "Nesli Elxansoy", "Orxan Shahbazov", "Zeynep Sahutoglu" ], "first_author": "Sukur Mehraliyeva", "venue": null, "publisher": null, "abstract": "In this study, the Multi-Drone-Assisted Vehicle Routing Problem (MDAVRP) algorithm, which incorporates the use of multiple drones, was tested under different task scenarios and varying parameters. The performance of the algorithm was evaluated in one, two, and three drone scenarios in terms of both computation times and task duration improvements. The numerical parameters utilized in the study and the problem-solving strategies were detailed. The solution efficiency of MDAVRP was analyzed by comparing it with the Traveling Salesman Problem (TSP) algorithm, and the effects of an increasing number of drones on task durations were examined. Additionally, the impact of factors such as drone utilization rates and battery life on system performance was comprehensively addressed. The results demonstrate that the increase in the number of drones reduces efficiency beyond a certain point and that battery life optimization can mitigate this effect.", "keywords": [ "Battery (electricity)", "Task (project management)", "Computer science", "Automotive engineering", "Battery capacity", "Capacity planning", "Engineering", "Systems engineering", "Power (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ichora65333.2025.11016972" }, { "id": "https://openalex.org/W4292937904", "doi": "10.17760/d20416568", "title": "Optimizing humanitarian logistics operations", "year": 2021, "publication_date": "2021-01-01", "type": "dissertation", "language": "en", "authors": [ "Tasnim Ibn Faiz" ], "first_author": "Tasnim Ibn Faiz", "venue": null, "publisher": null, "abstract": "Immediately after a disaster, humanitarian efforts are hindered by limited logistical resources and infrastructure failures. To address these challenges, we propose several decision frameworks for scheduling and routing of trucks and uncrewed aerial vehicles (UAVs or drones). Through numerical analysis, we provide managerial insights into the impacts of disaster-specific geographical and system parameters on operational performance metrics.In the first framework, we consider the decision problem of replenishing warehouses with emergency supplies following a disaster. We define this problem as a variant of truckload open vehicle routing problems and present two alternative formulations. We designed two column-generation-based solution approaches and several path generation algorithms to solve the resulting models. From numerical experiments, we find that the proposed solution approaches outperform commercial solver in terms of solution time. Due to post-disaster infrastructure failures, obtaining precise demand information and meeting demands using only trucks become all but impossible. To that end, in our second framework, we consider a two-echelon vehicle routing problem, where we assume that trucks transport two sets of UAVs and stop at predefined (satellite) locations to work as UAV launching pads. The first set of dispatched UAVs provide temporary telecommunication signals and obtain demand information, while the second set of UAVs make deliveries to satisfy the demand. We present a two-stage decision approach and design column-generation-based decomposition heuristics to solve the resulting models. From numerical experiments, we find that the proposed approach significantly reduces the solution time with a slight reduction in solution quality. Using a case study with post-disaster scenarios in Puerto Rico, we evaluate the changes in the model outcomes with the variation in several system parameters. In the third framework, we consider a two-echelon vehicle routing problem, where UAVs are deployed only for aid delivery and emergency aid demand is uncertain due to telecommunication infrastructure failure. We develop a two-stage robust optimization approach to handle demand uncertainty. To solve the resulting models, we design a column-and-constraint-generation technique for scenario generation and integrate it with a column-generation-based heuristic designed for the efficient generation of drone routes. We also investigate the effect of various parameters on model outcomes using the same case study.--Author's abstract", "keywords": [ "Column generation", "Heuristics", "Solver", "Truck", "Computer science", "Vehicle routing problem", "Scheduling (production processes)", "Routing (electronic design automation)", "Operations research", "Humanitarian Logistics", "Set (abstract data type)", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.library.northeastern.edu/files/neu:bz617819m/fulltext.pdf" }, { "id": "https://openalex.org/W4242926988", "doi": "10.35940/ijrte.c5945.059120", "title": "Secure Routing Algorithm in Wireless Mesh Network", "year": 2020, "publication_date": "2020-05-30", "type": "article", "language": "en", "authors": [ "Mr. Rajnikant Kundlik Narwade", "P. Balamurugan" ], "first_author": "Mr. Rajnikant Kundlik Narwade", "venue": "International Journal of Recent Technology and Engineering (IJRTE)", "publisher": null, "abstract": "In the today’s’ era of communication technology disaster management plays important role in life saving. Regardless of men made disasters or natural disasters proper communication network can be used to exchange information. In natural calamities wireless sensor mesh network proved to be as good option for communication. Low-altitude Unmanned Aerial Vehicles (UAVs) which is associated with WLAN Mesh Networks (WMNs) can be used in disaster management areas. The advantage of it is it can be installed on demand and it can use for efficient exploration of sized areas. The WMN is consisting of different components such as mesh clients, mesh routers and base station. It is more prone to have attacks of different types such as Denial of Service attack, black hole, gray hole , reply attack, Sybil attack etc. The most important attack which can damage whole working of WMN is routing attack. IEEE 802.11i is used as standard protocol for security in WLAN and IEEE 802.11s is used as secure algorithm for communication in WLAN mesh network. But both of them fail to address issue of routing problem in WLAN mesh network. The primary purpose of this paper is to outline routing algorithm and to design assured routing protocol. In this paper we have proposed improved secure Position-Aware, Secure, and Efficient mesh Routing approach (S-PASER). The proposed mechanisms have obviated more attack than regular IEEE 802.11s/i security mechanism and well known, secure routing protocol. The proposed methodology can be implemented on omnet++ simulator. The simulation results proved to be have better performance and throughput than existing algorithm.", "keywords": [ "Computer network", "Computer science", "Wireless mesh network", "Hazy Sighted Link State Routing Protocol", "Routing protocol", "Dynamic Source Routing", "IEEE 802.11s", "Order One Network Protocol", "Mesh networking", "Computer security", "Distributed computing", "Routing (electronic design automation)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.35940/ijrte.c5945.059120" }, { "id": "https://openalex.org/W4388551212", "doi": "10.4271/13-04-02-0014", "title": "Optimization of Takeaway Delivery Based on Large Neighborhood Search Algorithm", "year": 2023, "publication_date": "2023-11-09", "type": "article", "language": "en", "authors": [ "Qianqian Shao", "Aodi Wu", "Haobo (Rex) Sun", "Zhi Chen", "Weiqi Gou", "Yuyan He" ], "first_author": "Qianqian Shao", "venue": "SAE International journal of sustainable transportation, energy, environment & policy", "publisher": null, "abstract": "The drone logistics distribution method, with its small size, quick delivery, and zero-touch, has progressively entered the mainstream of development due to the global epidemic and the rapidly developing global emerging logistics business. In our investigation, a drone and a delivery man worked together to complete the delivery order to a customer’s home as quickly as possible. We realize the combined delivery network between drones and delivery men and focus on the connection and scheduling between drones and delivery men using existing facilities such as ground airports, unmanned stations, delivery men, and drones. Based on the dynamic-vehicle routing problem model, the establishment of a delivery man and drone with a hybrid model, in order to solve the tarmac unmanned aerial vehicle for take-out delivery scheduling difficulties, linking to the delivery man and an adaptive large neighborhood search algorithm solves the model. The objective function is to reduce customer waiting time. The average delivery time for orders was subsequently shortened thanks to the optimization of the cooperative delivery solution for drones and delivery personnel.", "keywords": [ "Drone", "Computer science", "Scheduling (production processes)", "Vehicle routing problem", "Delivery Performance", "Operations research", "Routing (electronic design automation)", "Engineering", "Computer network", "Operations management", "Industrial engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4271/13-04-02-0014" }, { "id": "https://openalex.org/W4412351760", "doi": "10.24275/azc2024b004b", "title": "La importancia del problema de ruteo de vehículos (VPR) en el sector industrial en la última década", "year": 2024, "publication_date": "2024-12-31", "type": "article", "language": "es", "authors": [ "Roberto Garcia Aguirre", "División de\n Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolina,\n Unidad Azcapotzlco.", "Román Anselmo Mora-Gutiérrez", "Edwin Montes-Orozco" ], "first_author": "Roberto Garcia Aguirre", "venue": "AZCATL", "publisher": null, "abstract": "This work present an analysis of Vehicles Routing Problem (VRP) in the logistics and distibution of goods in las decade. This is not something new, it has been analyzed and studied since 1959, but today, technological development has transformed and contributed to this problem with proposal for use of electric vehicles and drones, with all the advantages and restrictions that they entail.", "keywords": [ "Philosophy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://azcatl.azc.uam.mx/index.php/azcatl/article/download/30/49" }, { "id": "https://openalex.org/W4413869911", "doi": "10.1007/978-3-032-02106-9_78", "title": "CODEL: Collaborative Multi-robot Parcel Delivery for Last-Mile Logistics", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": "en", "authors": [ "Sumbal Malik", "Majid Khonji", "Khaled Elbassioni", "Jorge M.M. Dias" ], "first_author": "Sumbal Malik", "venue": "Mechanisms and machine science", "publisher": "Springer Nature (Netherlands)", "abstract": null, "keywords": [ "Last mile (transportation)", "Mile", "Robot", "Computer science", "Transport engineering", "Engineering", "Geography", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-032-02106-9_78" }, { "id": "https://openalex.org/W4287374018", "doi": "10.48550/arxiv.2101.07877", "title": "Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19\\n Pandemic", "year": 2021, "publication_date": "2021-01-19", "type": "preprint", "language": "en", "authors": [ "Manuel Patchou", "Benjamin Sliwa", "Christian Wietfeld" ], "first_author": "Manuel Patchou", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The integration of small-scale Unmanned Aerial Vehicles (UAVs) into\\nIntelligent Transportation Systems (ITSs) will empower novel smart-city\\napplications and services. After the unforeseen outbreak of the COVID-19\\npandemic, the public demand for delivery services has multiplied. Mobile\\nrobotic systems inherently offer the potential for minimizing the amount of\\ndirect human-to-human interactions with the parcel delivery process. The\\nproposed system-of-systems consists of various complex aspects such as\\nassigning and distributing delivery jobs, establishing and maintaining reliable\\ncommunication links between the vehicles, as well as path planning and mobility\\ncontrol. In this paper, we apply a system-level perspective for identifying key\\nchallenges and promising solution approaches for modeling, analysis, and\\noptimization of UAV-aided parcel delivery. We present a system-of-systems model\\nfor UAV-assisted parcel delivery to cope with higher capacity requirements\\ninduced by the COVID-19. To demonstrate the benefits of hybrid vehicular\\ndelivery, we present a case study focusing on the prioritization of\\ntime-critical deliveries such as medical goods. The results further confirm\\nthat the capacity of traditional delivery fleets can be upgraded with drone\\nusage. Furthermore, we observe that the delay incurred by prioritizing\\ntime-critical deliveries can be compensated with drone deployment. Finally,\\ncentralized and decentralized communication approaches for data transmission\\ninside hybrid delivery fleets are compared.\\n", "keywords": [ "Drone", "Software deployment", "Computer science", "Process (computing)", "Prioritization", "Coronavirus disease 2019 (COVID-19)", "Engineering", "Process management" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.07877" }, { "id": "https://openalex.org/W7210153134", "doi": "10.48676/unibo/amsdottorato/13129", "title": "Models and algorithms for routing and network optimization problems", "year": 2026, "publication_date": "2026-09-14", "type": "dissertation", "language": "en", "authors": [ "Federico Michelotto" ], "first_author": "Federico Michelotto", "venue": "AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "publisher": "University of Bologna", "abstract": "This PhD thesis explores three major optimization challenges in modern transportation and communication systems. The first chapter addressed the Flying Sidekick Traveling Salesman Problem with Variable Drone Speeds (FSTSP-VDS), a novel variant of the TSP that models coordinated truck-drone delivery with dynamically adjustable drone speeds. This chapter proposes the first exact method for the FSTSP-VDS, assuming a convex drone energy-per-meter function, even if the drone power consumption is non-linear with respect to speed. Results indicate that adaptive speed control can reduce delivery completion times by more than 32%. A genetic algorithm is also proposed to handle larger instances, significantly outperforming existing heuristics. The second chapter addressed the Capacitated Vehicle Routing Problem (CVRP). This chapter investigates the independent optimization of different solution components while maintaining feasibility. It uncovers a counterintuitive structural property of the CVRP that allows for a novel sequence-based decomposition approach, offering new insights into parallel optimization for routing problems. The third chapter addressed the Routing and Wavelength Assignment (RWA) in Optical Networks under a shared protection policy to ensure communication survivability. Integer Linear Programming relaxations and an iterated local search metaheuristic are proposed. Computational experiments on realistic instances show that these algorithms achieve feasible solutions with tight optimality gaps, effectively balancing network efficiency and survivability.", "keywords": [ "Drone", "Routing (electronic design automation)", "Metaheuristic", "Vehicle routing problem", "Optimization problem", "Integer programming", "Iterated local search", "Linear programming", "Convex optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://amsdottorato.unibo.it/view/dottorati/DOT547/>," }, { "id": "https://openalex.org/W4301400614", "doi": "10.48550/arxiv.2204.09753", "title": "Path Planning Algorithms for Robotic Aquaculture Monitoring", "year": 2022, "publication_date": "2022-04-20", "type": "preprint", "language": "en", "authors": [ "Anthony B. Davis", "Srijita Mukherjee", "Paul S. Wills", "Bing Ouyang" ], "first_author": "Anthony B. Davis", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (AM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.", "keywords": [ "Aquaculture", "Drone", "Solver", "Routing (electronic design automation)", "Underwater", "Motion planning", "Environmental science", "Computer science", "Path (computing)", "Water quality", "Benchmark (surveying)", "Fish " ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2204.09753" }, { "id": "https://openalex.org/W2965996301", "doi": null, "title": "배송 네트워크에서 드론의 유용성 검증 :차량과 드론을 혼용한 배송 네트워크의 경로계획", "year": 2016, "publication_date": "2016-08-01", "type": "article", "language": "ko", "authors": [ "정예림", "박태준", "민윤홍" ], "first_author": "정예림", "venue": "한국경영과학회지", "publisher": null, "abstract": "This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon’s instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.", "keywords": [ "Drone", "Computer science", "Inefficiency", "Metropolitan area", "Traffic congestion", "Mode (computer interface)", "Routing (electronic design automation)", "Operations research", "Transport engineering", "Computer network", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07001192" }, { "id": "https://openalex.org/W2541870265", "doi": null, "title": "배송 네트워크에서 드론의 유용성 검증", "year": 2016, "publication_date": "2016-08-01", "type": "article", "language": "ko", "authors": [ "정예림", "박태준", "민윤홍" ], "first_author": "정예림", "venue": "한국경영과학회지", "publisher": null, "abstract": "This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon’s instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.", "keywords": [ "Drone", "Computer science", "Metropolitan area", "Inefficiency", "Traffic congestion", "Mode (computer interface)", "Routing (electronic design automation)", "Transport engineering", "Operations research", "Computer network", "Engineering", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07001192?q=%5Bdrone%C2%A7coldb%C2%A72%C2%A751%C2%A73%5D&Multimedia=0&SearchAll=drone&isFullText=0&specificParam=0&SearchMethod=0&Sort=1&SortType=desc" }, { "id": "https://openalex.org/W4411757674", "doi": "10.1080/16258312.2025.2517526", "title": "Optimisation of contactless logistics delivery under emergency pandemic prevention policies", "year": 2025, "publication_date": "2025-06-28", "type": "article", "language": "en", "authors": [ "Jiaojiao Gao", "Qing Xin Xiao", "Xiuping Guo" ], "first_author": "Jiaojiao Gao", "venue": "Supply Chain Forum an International Journal", "publisher": "Taylor & Francis", "abstract": "Pandemic outbreaks present significant challenges for emergency logistics, demanding approaches that balance distribution efficiency with infection risk mitigation. This paper introduces the CRP&VRPD (Collaborative Routing Problem & Vehicle Routing Problem with Drones), an optimisation framework for truck-drone collaborative contactless delivery systems operating under pandemic-related policies. The mathematical model integrates key constraints, including risk-zone-based service differentiation, contactless delivery protocols, and vehicle/drone capacity limits, providing a tailored approach for pandemic response logistics. Addressing the NP-hard nature of the CRP&VRPD, we propose TK-SA, a two-phase hybrid heuristic. Phase one determines optimal locations for contactless pickup points using an enhanced K-means++ algorithm incorporating a spatiotemporal distance metric. Phase two employs an enhanced simulated annealing algorithm, featuring problem-specific operators and acceleration strategies, to optimise the coordinated truck and drone routes. The efficacy and performance of the proposed model and the TK-SA algorithm are validated through computational experiments, including a case study based on a 50-node benchmark instance. The case study demonstrates that the proposed collaborative system can reduce the total delivery time by approximately 51% compared to a traditional truck-only baseline, while ensuring adherence to contactless protocols. Sensitivity analyses explore the impact of key parameters, including drone payload capacity and high-risk zone definitions, on system performance.", "keywords": [ "Pandemic", "Business", "Coronavirus disease 2019 (COVID-19)", "Medical emergency", "Aeronautics", "Transport engineering", "Engineering", "Medicine" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/16258312.2025.2517526" }, { "id": "https://openalex.org/W7208803551", "doi": null, "title": "Integral efficiency assessment of cargo unmanned aerial vehicles", "year": 2025, "publication_date": "2025-11-01", "type": "dissertation", "language": "en", "authors": [ "Li Haoyang", "Volodymyr P. Kharchenko" ], "first_author": "Li Haoyang", "venue": "Electronic Institutional Repository of the National Aviation University of Ukraine (National Aviation University, Ukraine)", "publisher": "National University \"Kyiv Aviation Institute\"", "abstract": "1. V. Kharchenko, “The use of unmanned aircraft systems for fast delivery of goods,” Logistics in Aviation, no. 3–4, pp. 89–91, 2020, The International University of Logistics and Transport in Wroclaw. 2. Dorling, Kevin & Heinrichs, Jordan & Messier, Geoffrey & Magierowski, Sebastian. (2016). Vehicle Routing Problems for Drone Delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47. 1-16. 10.1109/TSMC.2016.2582745.", "keywords": [ "Drone", "Routing (electronic design automation)", "Vehicle routing problem", "Humanitarian Logistics", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://er.kai.edu.ua/bitstreams/010c0847-3e6c-49c6-896e-393698d5bea6/download" }, { "id": "https://openalex.org/W7168438158", "doi": null, "title": "ドローンの速度選択を考慮した配送計画問題に対する発見的解法", "year": 2026, "publication_date": "2026-07-01", "type": "article", "language": "ja", "authors": [ "Yo SHIMIZU" ], "first_author": "Yo SHIMIZU", "venue": "Institutional Repositories DataBase (IRDB)", "publisher": "National Institute of Informatics", "abstract": "Tamke らは, この乖離を解消すべく, 現実的な機体のエ ネルギー消費を考慮した VRPD-DSS (Vehicle Routing Problem with Drones and Drone Speed Selection) を 提唱した [1].本研究では VRPD-DSS に対し, 先行研 究とは異なる独自の経路設計プロセスを組み込んだメタ ヒューリスティクスを提案する.", "keywords": [ "Drone", "Routing (electronic design automation)", "Position (finance)", "Work (physics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://chuo-u.repo.nii.ac.jp/record/2004102/files/nenpou24N8100013J.pdf" }, { "id": "https://openalex.org/W4408187023", "doi": "10.2139/ssrn.5168415", "title": "Reducing Rural Delivery Costs Through Optimization of Advanced Technologies", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "Marcella Kaplan", "Kevin P. Heaslip" ], "first_author": "Marcella Kaplan", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Business", "Computer science", "Risk analysis (engineering)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5168415" }, { "id": "https://openalex.org/W4253870241", "doi": "10.5194/nhess-2017-192-ac3", "title": "Revised manuscript", "year": 2017, "publication_date": "2017-09-18", "type": "peer-review", "language": "en", "authors": [ "Salvatore Scudero" ], "first_author": "Salvatore Scudero", "venue": null, "publisher": null, "abstract": "Vehicle routing problem and UAV application in the post-earthquake scenario\"", "keywords": [ "Philosophy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5194/nhess-2017-192-ac3" }, { "id": "https://openalex.org/W7213446938", "doi": "10.1287/msom.2024.1197", "title": "Last-Mile Humanitarian Logistics Planning with Isolated Communities", "year": 2026, "publication_date": "2026-09-17", "type": "article", "language": "en", "authors": [ "Mahdi Noorizadegan", "Mohammad Fattahi", "Esmaeil Keyvanshokooh", "Jon M. Stauffer" ], "first_author": "Mahdi Noorizadegan", "venue": "Manufacturing & Service Operations Management", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "Problem Definition: This paper addresses the critical challenge of delivering humanitarian aid to points of distribution (PoDs) after a disaster under uncertainty, particularly when some PoDs become isolated due to road/bridge damages. Motivated by the Eta and Iota hurricanes in Honduras, we introduce a new Last-mile Humanitarian Logistics Planning problem that jointly determines: the location and capacity of staging areas (SAs), the fleet-sizes of heterogeneous mobile units-ground and aerial, the allocation of mobile units to SAs, and routing decisions ensuring all PoDs are served within a target delivery time. Mobile units can perform multiple trips within this window, enabling efficient fleet-size optimization. Methodology/Results: We formulate this problem as a Parallel Drone–Vehicle Routing Problem with Location and Fleet-Size Decisions, modeled as a route-based mixed-integer program that captures uncertainty in demand and travel times via a unified chance constraint framework. This framework accommodates multiple uncertainty modeling approaches—standard chance-constraints, CVaR-based constraints, and distributionally robust chance-constraints along with a deterministic benchmark, allowing decision-makers to control robustness levels under varying data availability. To solve this complex problem exactly, we develop a tailored Branch-and-Price algorithm, where the nonlinear pricing subproblem is reformulated as a Shortest-Path Problem with Chance-Constraints, efficiently solved by a customized dynamic programming approach with new dominance conditions. Notably, the complexity of the uncertainty model remains comparable to the deterministic counterpart, making our approach practical and scalable. Managerial Implications: A case study and synthetic instances demonstrate our framework’s versatility and practical relevance. Our results uncover critical trade-offs between robustness and investment, the effects of delivery time targets and isolation on network structure and fleet-size, and differences across uncertainty-modeling approaches. They also highlight the role of aerial fleet composition on operational efficiency and economic performance. These analyses provide actionable guidance on network configuration, fleet-sizing, and preferred modeling approaches under limited data and resources typically seen in humanitarian response.", "keywords": [ "Robustness (evolution)", "Robust optimization", "Humanitarian Logistics", "TRIPS architecture", "Vehicle routing problem", "Facility location problem", "Stackelberg competition", "Column generation", "Humanitarian aid", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/msom.2024.1197" }, { "id": "https://openalex.org/W4230916325", "doi": "10.32920/ryerson.14660943", "title": "Multimodal Automated Last-Mile Delivery System: Design and Application", "year": 2021, "publication_date": "2021-06-08", "type": "other", "language": "en", "authors": [ "Farah Samouh" ], "first_author": "Farah Samouh", "venue": null, "publisher": null, "abstract": "This thesis focuses on exploring the emerging automated technologies for last-mile on-demand food delivery as a new means of transportation to reduce congestion in urban areas. In order to achieve that 4 systems are designed and evaluated: Robot delivery system, drone delivery system and two hybrid delivery systems. Both hybrid systems are based on hub-spoke networks, Hybrid System 1.0 uses robots for phase one of the delivery and drones for phase two Hybrid System 2.0 uses drones for phase one and robots for phase two. To evaluate the efficiency of these systems, an in-house agent-based simulation model in MATLAB is developed for the City of Mississauga. 30 scenarios are tested differing in terms of demand and fleet size. The results show that Hybrid system 2.0 is the most efficient system of all four proposed due to the use of hub, customer waiting time and landing zones for drones.", "keywords": [ "Drone", "Last mile (transportation)", "Hybrid system", "Computer science", "Food delivery", "Robot", "Delivery system", "Mile", "Simulation", "Phase (matter)", "Transport engineering", "Real-time computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.32920/ryerson.14660943" }, { "id": "https://openalex.org/W4231495250", "doi": "10.32920/ryerson.14660943.v1", "title": "Multimodal Automated Last-Mile Delivery System: Design and Application", "year": 2021, "publication_date": "2021-06-08", "type": "other", "language": "en", "authors": [ "Farah Samouh" ], "first_author": "Farah Samouh", "venue": null, "publisher": null, "abstract": "This thesis focuses on exploring the emerging automated technologies for last-mile on-demand food delivery as a new means of transportation to reduce congestion in urban areas. In order to achieve that 4 systems are designed and evaluated: Robot delivery system, drone delivery system and two hybrid delivery systems. Both hybrid systems are based on hub-spoke networks, Hybrid System 1.0 uses robots for phase one of the delivery and drones for phase two Hybrid System 2.0 uses drones for phase one and robots for phase two. To evaluate the efficiency of these systems, an in-house agent-based simulation model in MATLAB is developed for the City of Mississauga. 30 scenarios are tested differing in terms of demand and fleet size. The results show that Hybrid system 2.0 is the most efficient system of all four proposed due to the use of hub, customer waiting time and landing zones for drones.", "keywords": [ "Drone", "Last mile (transportation)", "Hybrid system", "Computer science", "Food delivery", "Robot", "Delivery system", "Mile", "Real-time computing", "Transport engineering", "Simulation", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://rshare.library.ryerson.ca/articles/thesis/Multimodal_Automated_Last-Mile_Delivery_System_Design_and_Application/14660943/1/files/28143447.pdf" }, { "id": "https://openalex.org/W2620131115", "doi": "10.1016/s0377-2217(86)80026-1", "title": "", "year": 1986, "publication_date": "1986-10-01", "type": "article", "language": "en", "authors": [ "Jan B. M. van Doremalen" ], "first_author": "Jan B. M. van Doremalen", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Orienteering", "Drone", "Computer science", "Mathematical optimization", "Context (archaeology)", "Benchmark (surveying)", "Operations research", "Mathematics", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/s0377-2217(86)80026-1" }, { "id": "https://openalex.org/W7160960962", "doi": "10.3390/engproc2026133121", "title": "System-of-Systems Guided Agent Communication and Collaboration in Aerial Wildfire Fighting", "year": 2026, "publication_date": "2026-05-12", "type": "conference-paper", "language": "en", "authors": [ "Nikolaos Kalliatakis", "Nabih Naeem", "Prajwal Shiva Prakasha" ], "first_author": "Nikolaos Kalliatakis", "venue": null, "publisher": null, "abstract": "The year 2025 saw the continuing trend of worsening wildfire severity and impact with escalating costs, burnt area and casualties. Subsequently, the capability for a rapid response operation is ever-growing, with aerial assets providing a key role in fulfilling this function. One problem with aerial suppression is the reliance on updated fire data and precise fire front information. Drones or other long-endurance vehicles are commonly used to assist in this matter, providing real-time data and imagery to the manned suppression bombers. The interactions and collaboration between these systems to achieve an improved wildfire suppression can be classified as a system-of-systems (SoS). To facilitate the design, interaction and communication of the surveillance drones and suppression aircraft, this paper develops a holistic framework using an agent-based simulation. The framework allows for the analysis of top-level drone design parameters and operational considerations with their communication and collaboration both with each other and the suppressive agents. The results showcase the importance of swath radius for better wildfire coverage and suppression, with radii less than 50 m preventing successful exploration of the fire. The importance of monitoring is highlighted by the observed greater reductions in burnt area and fleet energy usage when increasing the monitoring agent fleet size by 50% compared to the same increase in suppression agent fleet size.", "keywords": [ "Drone", "Key (lock)", "Firefighting", "Front (military)", "Disaster response", "Incident response", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2673-4591/133/1/121/pdf?version=1778574798" }, { "id": "https://openalex.org/W7116637352", "doi": null, "title": "Systems of Systems Guided Agent Communication and Collaboration in Aerial Wildfire Fighting", "year": 2025, "publication_date": "2025-01-01", "type": "conference-paper", "language": null, "authors": [ "Nikolaos Kalliatakis", "Nabih Naeem", "Prajwal Shiva Prakasha" ], "first_author": "Nikolaos Kalliatakis", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": "2025 saw the continuing trend of worsening wildfire severity and impact with escalating costs, burnt area and casualties. Subsequently, the capability for a rapid response operation is ever-growing, with aerial assets providing a key role in fulfilling this function. One problem with aerial suppression is the reliance on updated fire data and precise fire front information. Drones or other long-endurance vehicles are commonly used to assist in this matter, providing real-time data and imagery to the manned suppression bombers. The interactions and collaboration between these systems to achieve an improved wildfire suppression can be classified as a system-of-systems (SoS). To facilitate the design, interaction and communication of the surveillance drones and suppression aircraft, this paper develops a holistic framework using an agent-based simulation. The framework allows for the analysis of top-level drone design parameters and operational considerations with their communication and collaboration both with each other and the suppressive agents. The results showcase the importance of swath radius for better wildfire coverage and suppression, with radii less than 50 m preventing successful exploration of the fire. The importance of monitoring is highlighted by the observed greater reductions in burnt area and fleet energy usage when increasing the monitoring agent fleet size by 50% compared to the same increase in suppression agent fleet size", "keywords": [ "Drone", "Key (lock)", "Firefighting", "Front (military)", "Disaster response", "Incident response", "RADIUS", "Energy (signal processing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/219893/" }, { "id": "https://openalex.org/W7141282354", "doi": "10.1109/icpege67691.2026.11451454", "title": "Attention-Based Hierarchical Collaborative Optimization for Multi-Agent Path Planning and Resource Allocation", "year": 2026, "publication_date": "2026-01-28", "type": "conference-paper", "language": null, "authors": [ "Wanxuan Gao", "Yingqi Zhu", "Ruimeng Wang" ], "first_author": "Wanxuan Gao", "venue": null, "publisher": null, "abstract": "Path length and battery amp-hours constrain each other. Ignoring this coupling produces deadlocks in warehouses and mid-air returns for drones. We fold the two sub-problems into one two-layer model. The top layer runs self-attention only on agent pairs whose swept volumes overlap within the next At; the bottom layer casts every future energy or CPU request as a query vector and matches it against the closest, fastest, least loaded supplier. Information is exchanged every 0.5 s, so a reroute immediately triggers a re-allocation and vice-versa. Tests with fifty AGVs and twenty UAVs show completion time down 18–25 % and resource utilisation up 13–19 % when the fleet size swings from 10 to 50. The code is public.", "keywords": [ "Resource allocation", "Path (computing)", "Resource management (computing)", "Resource (disambiguation)", "Motion planning", "Scheme (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icpege67691.2026.11451454" }, { "id": "https://openalex.org/W4312808061", "doi": "10.1007/978-3-030-72322-4_126-1", "title": "Energy-Smart Transportation Systems", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "Saba Sabet", "Bilal Farooq" ], "first_author": "Saba Sabet", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Electrification", "Transport engineering", "Sustainable transport", "Greenhouse gas", "Environmental economics", "Engineering", "Business", "Sustainability", "Electricity", "Economics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-72322-4_126-1" }, { "id": "https://openalex.org/W7103896025", "doi": "10.6084/m9.figshare.30514928", "title": "delete", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Fuzzy logic", "Fuzzy control system", "Routing (electronic design automation)", "Fuzzy set", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.6084/m9.figshare.30514928" }, { "id": "https://openalex.org/W7103898362", "doi": "10.6084/m9.figshare.29974870.v2", "title": "delete", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "Fang, Yuan" ], "first_author": "Fang, Yuan", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Fuzzy logic", "Fuzzy control system", "Routing (electronic design automation)", "Fuzzy set", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/dataset/A_Novel_Drone_Routing_Problem_Considering_Hybrid_Charging_and_Fuzzy_Time_Windows_Using_an_Enhanced_Adaptive_Large_Neighborhood_Search_Algorithm/29974870" }, { "id": "https://openalex.org/W7103885000", "doi": "10.6084/m9.figshare.30514928.v3", "title": "delete", "year": 2025, "publication_date": "2025-01-01", "type": "dataset", "language": null, "authors": [ "purpose), Authors (for anonymous review" ], "first_author": "purpose), Authors (for anonymous review", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "This is the experimental data of the manuscript entitled \"A Novel Drone Routing Problem Considering Hybrid Charging and Fuzzy Time Windows Using an Enhanced Adaptive Large Neighborhood Search Algorithm\".", "keywords": [ "Fuzzy logic", "Fuzzy control system", "Routing (electronic design automation)", "Fuzzy set", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/dataset/A_Novel_Drone_Routing_Problem_Considering_Hybrid_Charging_and_Fuzzy_Time_Windows_Using_an_Enhanced_Adaptive_Large_Neighborhood_Search_Algorithm/30514928" }, { "id": "https://openalex.org/W2012202657", "doi": "10.1016/j.optm.2009.04.028", "title": "Choroideremia: A Case Report and Review of Differentials", "year": 2009, "publication_date": "2009-05-23", "type": "article", "language": "en", "authors": [ "Raneat Cohen", "Rebecca S. Diller", "Dennis J. Light" ], "first_author": "Raneat Cohen", "venue": "Optometry", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Backtracking", "Computer science", "Heuristic", "Key (lock)", "Operations research", "Mathematical optimization", "Routing (electronic design automation)", "Trajectory", "Artificial intelligence", "Algorithm", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.optm.2009.04.028" }, { "id": "https://openalex.org/W4293449818", "doi": "10.48550/arxiv.2011.01492", "title": "Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection", "year": 2020, "publication_date": "2020-11-03", "type": "preprint", "language": "en", "authors": [ "Tianshuang Gao", "Shashwata Mandal", "Sourabh Bhattacharya" ], "first_author": "Tianshuang Gao", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform deployed for biometric data collection in-the-wild. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an $m$TSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.", "keywords": [ "Computer science", "Task (project management)", "Motion planning", "Robot", "Vehicle routing problem", "Quadcopter", "Routing (electronic design automation)", "Cluster analysis", "Drone", "Artificial intelligence", "Distributed computing", "Real-time computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2011.01492" }, { "id": "https://openalex.org/W7213425564", "doi": "10.5281/zenodo.22784575", "title": "MVDRP-instances", "year": 2026, "publication_date": "2026-09-16", "type": "dataset", "language": null, "authors": [ "Baotong Wu" ], "first_author": "Baotong Wu", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "MVDRP Instances This repository contains 90 benchmark instances for the multi-vessel and drone routing problem for offshore wind turbine collaborative inspection. The instances are generated based on the geographical layout of the Hornsea 1 offshore wind farm. Repository structure • Hornsea_1_coordinates.xlsx Contains the geographical coordinates of the 174 offshore wind turbines in the Hornsea 1 offshore wind farm. • 90_instances/ • Small_instances/ Contains 30 benchmark instances with 10–50 wind turbines and 1–3 vessels. Each vessel carries either two or three drones. • Medium_instances/ Contains 30 benchmark instances with 51–100 wind turbines and 2–4 vessels. Each vessel carries either two or three drones. • Large_instances/ Contains 30 benchmark instances with 101–174 wind turbines and 3–6 vessels. Each vessel carries either two or three drones. • README.md Instance naming Each benchmark instance is named in the form: H1-n-v-u.xlsx where:• H1 denotes the Hornsea 1 offshore wind farm;• n denotes the number of wind turbines;• v denotes the number of vessels;• u denotes the number of drones carried by each vessel.", "keywords": [ "Benchmark (surveying)", "Offshore wind power", "Turbine", "Wind power", "Submarine pipeline" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.22784575" }, { "id": "https://openalex.org/W7213410207", "doi": "10.5281/zenodo.22784576", "title": "MVDRP-instances", "year": 2026, "publication_date": "2026-09-16", "type": "dataset", "language": null, "authors": [ "Baotong Wu" ], "first_author": "Baotong Wu", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "MVDRP Instances This repository contains 90 benchmark instances for the multi-vessel and drone routing problem for offshore wind turbine collaborative inspection. The instances are generated based on the geographical layout of the Hornsea 1 offshore wind farm. Repository structure • Hornsea_1_coordinates.xlsx Contains the geographical coordinates of the 174 offshore wind turbines in the Hornsea 1 offshore wind farm. • 90_instances/ • Small_instances/ Contains 30 benchmark instances with 10–50 wind turbines and 1–3 vessels. Each vessel carries either two or three drones. • Medium_instances/ Contains 30 benchmark instances with 51–100 wind turbines and 2–4 vessels. Each vessel carries either two or three drones. • Large_instances/ Contains 30 benchmark instances with 101–174 wind turbines and 3–6 vessels. Each vessel carries either two or three drones. • README.md Instance naming Each benchmark instance is named in the form: H1-n-v-u.xlsx where:• H1 denotes the Hornsea 1 offshore wind farm;• n denotes the number of wind turbines;• v denotes the number of vessels;• u denotes the number of drones carried by each vessel.", "keywords": [ "Benchmark (surveying)", "Offshore wind power", "Turbine", "Wind power", "Submarine pipeline" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.22784576" }, { "id": "https://openalex.org/W2622493741", "doi": null, "title": "Scheduling of unmanned aerial vehicles for supply chain networks", "year": 2014, "publication_date": "2014-01-01", "type": "report", "language": "en", "authors": [ "Nagarkar Anay Shriniwas" ], "first_author": "Nagarkar Anay Shriniwas", "venue": "DR-NTU (Nanyang Technological University)", "publisher": "Nanyang Technological University", "abstract": "This report has been written with the aim of providing the reader with an overview of the author’s work and learning experience during completion of the Final Year Project at the School of Computer Engineering at Nanyang Technological University, Singapore. The objective of this report is to give the reader a detailed description and simulation of the author’s work in the scheduling of unmanned aerial vehicles in supply chain networks. \\n \\nThe growth of online shopping and e-commerce in the past decade has made a lot of changes in the supply chain and logistics end-user network as companies try to get products to the customers in the fastest and most hassle free way. Customers nowadays enjoy the luxury of being able to get whatever they want at the click of a button, without leaving the comfort of their house. Operational performance is extremely important in improving customer satisfaction. The use of Unmanned Aerial Vehicles (UAVs) or drones for package delivery is a revolutionary idea that can change the way the logistics networks work drastically. It would be able to provide with better logistical efficiency as well as promote service quality and better the customer experience. \\n \\nThis project uses the Capacitated Vehicle Routing Problem to find a solution to the scheduling of unmanned aerial vehicles. It aims to simulate such a working system and find the most optimum and low cost path from the depot to the destination nodes, keeping in mind various constraints that are faced by the vehicles and the network. This report describes testing of the results of the experiment as well as an analysis of the results. Finally, it also attempts to provide a conclusion and a recommendation for the improvement of the solution. The entire project has been developed using the C# programming language. \\n \\n", "keywords": [ "Scheduling (production processes)", "Supply chain", "Computer science", "Drone", "Supply chain management", "Real-time computing", "Operations management", "Business", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10356/59259" }, { "id": "https://openalex.org/W4363649145", "doi": "10.1109/eebda56825.2023.10090846", "title": "Research on emergency supplies distribution in 5G network environment based on K-class mean value algorithm", "year": 2023, "publication_date": "2023-02-24", "type": "conference-paper", "language": "en", "authors": [ "Ziyu Sun", "Zihao Jia", "Weihang Han" ], "first_author": "Ziyu Sun", "venue": null, "publisher": null, "abstract": "In order to reduce the impact of emergencies on people's production and life, it is an important issue to study the distribution routes of emergency supplies to quickly improve the efficiency of emergency supplies distribution. At present, with the continuous development of 5G network environment, \"vehicle-drone\" joint delivery will continue to replace the vehicle delivery mode. In this paper By building mathematical model, using k-mean clustering algorithm This paper investigates the route problem of tandem \"vehicle-drone\" distribution, which improves the efficiency of emergency material distribution and effectively solves the problem of adverse effects of complex road conditions on material distribution.", "keywords": [ "Cluster analysis", "Computer science", "Drone", "Mode (computer interface)", "Class (philosophy)", "Distribution (mathematics)", "Transport engineering", "Operations research", "Engineering", "Artificial intelligence", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/eebda56825.2023.10090846" }, { "id": "https://openalex.org/W7153940419", "doi": "10.5281/zenodo.19542478", "title": "Naval Mine Clearance Optimization in the Strait of Hormuz", "year": 2026, "publication_date": "2026-04-12", "type": "article", "language": "en", "authors": [ "Alberto Oliva" ], "first_author": "Alberto Oliva", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Naval mine clearance operations in the Strait of Hormuz constitute one of the most complex and hazardous military tasks in the current geopolitical environment. As the world’s critical maritime chokepoint, the strait carries approximately 20% of global oil production daily. This study develops an advanced Operations Research framework based on Mixed-Integer Linear Programming (MILP), formulated as a Set Covering Problem (SCP). The model divides the area into a realistic grid of 20 geographic zones and aims to minimize the number and cost of clearance interventions — performed by mine countermeasure vessels, UUVs, and surface drones — while ensuring complete coverage of all high-risk zones. The framework incorporates sensitivity analysis, multi-objective Pareto frontiers, and an extension to the Vehicle Routing Problem (VRP) for dynamic sweep path optimization. Numerical results show that the optimized approach reduces the number of missions by 50–60% compared to conventional unstructured strategies, achieving 100% coverage of critical zones with only 5 interventions on a 20-zone grid.", "keywords": [ "Routing (electronic design automation)", "Countermeasure", "Pareto principle", "Linear programming", "Set cover problem", "Drone", "Grid", "Critical path method", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.19542478" }, { "id": "https://openalex.org/W7153831111", "doi": "10.5281/zenodo.19542477", "title": "Naval Mine Clearance Optimization in the Strait of Hormuz", "year": 2026, "publication_date": "2026-04-12", "type": "article", "language": "en", "authors": [ "Alberto Oliva" ], "first_author": "Alberto Oliva", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Naval mine clearance operations in the Strait of Hormuz constitute one of the most complex and hazardous military tasks in the current geopolitical environment. As the world’s critical maritime chokepoint, the strait carries approximately 20% of global oil production daily. This study develops an advanced Operations Research framework based on Mixed-Integer Linear Programming (MILP), formulated as a Set Covering Problem (SCP). The model divides the area into a realistic grid of 20 geographic zones and aims to minimize the number and cost of clearance interventions — performed by mine countermeasure vessels, UUVs, and surface drones — while ensuring complete coverage of all high-risk zones. The framework incorporates sensitivity analysis, multi-objective Pareto frontiers, and an extension to the Vehicle Routing Problem (VRP) for dynamic sweep path optimization. Numerical results show that the optimized approach reduces the number of missions by 50–60% compared to conventional unstructured strategies, achieving 100% coverage of critical zones with only 5 interventions on a 20-zone grid.", "keywords": [ "Routing (electronic design automation)", "Countermeasure", "Pareto principle", "Linear programming", "Set cover problem", "Drone", "Grid", "Critical path method", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.19542477" }, { "id": "https://openalex.org/W4403216159", "doi": "10.24415/9789400604537-012", "title": "CHAPTER 8 Applying GTSP-algorithms in maritime patrolling missions that require mutual support", "year": 2023, "publication_date": "2023-12-31", "type": "book-chapter", "language": "en", "authors": [ "Martijn van Ee", "Geraldo Mulato de Lima Filho", "Herman Monsuur" ], "first_author": "Martijn van Ee", "venue": "Leiden University Press eBooks", "publisher": "Leuven University Press", "abstract": "The goal of a maritime patrol is to detect, locate and identify (opposing) vessels.In some of these missions, the patrol aircraft may need the support of another aircraft to counter a possible threat.This support may consist of using capabilities of the other aircraft, such as its sensors, communications or its weapons.Our work aims to optimise maritime patrol routes for two or more drones, while providing the possibility of mutual support.We show that this routing problem including mutual support can be modelled as a generalised travelling salesman problem (GTSP).We investigate the costs of requiring mutual support and compare it to the costs of using separate drones that detect and identify vessels in the area of operations.", "keywords": [ "Patrolling", "Computer science", "Algorithm", "Geography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.degruyterbrill.com/document/doi/10.24415/9789400604537-012/pdf" }, { "id": "https://openalex.org/W7160913800", "doi": "10.1109/access.2026.3692710", "title": "Enhanced Weighted Target Approximation Using an Improved Particle Swarm Optimization Approach", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": "en", "authors": [ "Gabriel R. Dos Santos", "Guilherme C. Machado", "Alexandre C. B. Ramos", "Douglas Bueno", "Douglas Bressan Riffel", "Alexandre C. B. Ramos" ], "first_author": "Gabriel R. Dos Santos", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This article proposes a two-layer tactical decision architecture for the autonomous allocation of Unmanned Aerial Vehicle (UAV) swarms in Search-and-Rescue (SAR) missions under severe resource constraints. The approach addresses the triage and distributed delivery challenge by integrating aWeighted Target Approximationprotocol enhanced by Particle Swarm Optimization (WTA–PSO) with a Spatial Tactical Evaluation heuristic (Spatial Clustering). In the first layer, WTA–PSO replaces the conventional proximity-based target choice with a low-cost one-dimensional optimization that jointly accounts for flight distance, the victim’s medical urgency, and local drone-to-drone conflict, while preserving WTA’s intention exchange and distributed conflict resolution. In the second layer, spatial clustering enables UAVs, each constrained to a single rescue payload (single-payload drop) to evaluate the disaster topology and focus on victim clusters that lie within the payload coverage radius. Experiments were conducted in a dynamic two-dimensional simulation environment with mobile targets categorized into three urgency levels (green, orange, gray). The results reveal complementary gain profiles: under strict one-to-one engagement, WTA–PSO significantly increases the global Humanitarian Impact (Total_Value) exclusively through intelligent prioritization (emphasizing the criticalgray level), while keeping the service volume near the fleet’s physical limit. However, when spatial clustering is enabled (Layer 2), swarm intelligence enables multiple simultaneous rescues with a single payload, breaking the logistical ceiling imposed by the fleet size (Nuav) and dramatically maximizing mission efficiency. The architecture’s robustness is validated over 50 independent runs (seeds) with high statistical significance (Kruskal–Wallis and paired Wilcoxon tests,pNuav= 15) and supersaturated (Nuav= 60) fleets. The findings show that coupling WTA–PSO’s predictive conflict-mitigation capability with the geometric exploitation of victim distributions consistently maximizes the operational effectiveness of robotic swarms across disasters of different scales.", "keywords": [ "Particle swarm optimization", "Approximation algorithm", "Algorithm design", "Minification", "Approximation theory" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2026.3692710" }, { "id": "https://openalex.org/W7125943815", "doi": "10.19139/soic-2310-5070-2669", "title": "Parenting Fitness in Genetic Algorithms: Empirical Advantages Over NSGA-II in Logistics Optimization", "year": 2026, "publication_date": "2026-01-27", "type": "article", "language": null, "authors": [ "Ouiss Mustapha", "Abdelaziz Ettaoufik", "Abdelaziz Marzak" ], "first_author": "Ouiss Mustapha", "venue": "Statistics Optimization & Information Computing", "publisher": "International Academic Press", "abstract": "Genetic algorithms (GAs) are population-based metaheuristics widely employed to solve complex real-world problems such as networking and resource allocation. These algorithms evolve a population of candidate solutions through iterative processes of selection, crossover, and mutation. The composition of the next generation is determined by either a general approach, where only offspring are retained, or an elitist approach, which selects fit solutions from the current generation. While elitism enhances solution quality, it is susceptible to premature convergence. This article presents a comparative study between the parenting fitness mechanism and the crowding distance approach used in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for solving the Vehicle Routing Problem with Drones (VRPD). Experimental evaluations demonstrate that the proposed parenting fitness method yields consistent improvements in solution quality. Relative improvements range from 27.30% to 43.83% for small problem instances, 6.36% to 11.41% for medium instances, and 0.54% to 1.90% for large instances, with performance variations influenced by the population size. These results validate the effectiveness of parenting fitness as a diversity-preservation strategy in mono-objective optimization.", "keywords": [ "Premature convergence", "Population", "Sorting", "Range (aeronautics)", "Metaheuristic", "Empirical research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.19139/soic-2310-5070-2669" }, { "id": "https://openalex.org/W4405801594", "doi": "10.1080/23249935.2024.2444327", "title": "A learning and searching integration approach for same-day delivery problem with heterogeneous fleets considering uncertainty", "year": 2024, "publication_date": "2024-12-26", "type": "article", "language": "en", "authors": [ "Meng Li", "Peng Zhao", "Yanbo Zhu", "Kaiquan Cai" ], "first_author": "Meng Li", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "Rising customer expectations for fast shipping, has significantly fuelled the widespread popularity of same-day delivery services. To optimise these services for dynamic and stochastic requests, employing heterogeneous fleets that include both vehicles and drones can effectively minimise the resources required for delivery. However, uncertainties in the real world, particularly in vehicle travel, adversely affect the service levels. This paper introduces a same-day delivery problem with heterogeneous fleets considering uncertain travel and service time (SDDPHF-UT). We utilise a route-based Markov decision process to formulate this problem, explicitly incorporating travel and service uncertainty into the state and action spaces. Next, we develop an adaptive optimisation approach integrating learning and searching (AILS) to solve the problem. This approach employs policy proximal optimisation and dynamic variable neighbourhood search, responsible for request assignments and vehicle route planning, respectively. These components are interdependent and collaborate to tackle the problem in a synchronised manner, enhancing adaptability to dynamic and stochastic scenarios. Computational results indicate that the AILS significantly outperforms baseline approaches, exhibiting robust generalisation across various data distributions and fleet sizes, and achieving an average service rate improvement of 7.23%. This underscores AILS' potential as a highly effective tool of optimising same-day delivery operations in the face of uncertainty.", "keywords": [ "Computer science", "Markov decision process", "Operations research", "Interdependence", "Popularity", "Service (business)", "Adaptability", "Markov process", "Mathematical optimization", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23249935.2024.2444327" }, { "id": "https://openalex.org/W7137849459", "doi": "10.11591/ijece.v16i2.pp883-894", "title": "Elitist genetic algorithm improved with parenting fitness parameter", "year": 2026, "publication_date": "2026-03-17", "type": "article", "language": null, "authors": [ "Ouiss Mustapha", "Abdelaziz Ettaoufik", "Abdelaziz Marzak" ], "first_author": "Ouiss Mustapha", "venue": "International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering", "publisher": "Institute of Advanced Engineering and Science (IAES)", "abstract": "In genetic algorithms, the selection of individuals that will be part of future generations is a critical process of the algorithm. Various strategies exist to select these individuals: the general approach and the elitist approach. The general approach involves replacing the whole current population with the offspring generated so far. The elitist approach introduces a competitive element in which both parents and offspring compete for survival, and only fit individuals will be part of the next generation. While selecting fit individuals helps the algorithm to produce better results, the elitism has a major drawback: the premature convergence, which can limit the algorithm's overall performance. In this article, we compared a typical elitist genetic algorithm and an elitist algorithm improved with the parenting fitness parameter in resolving the vehicle routing problem with drones (VRPD). The parenting fitness parameter helps preserving diversity by retaining parents with high offspring potential despite of their personal fitness. The findings from the study demonstrates that integrating the parenting fitness parameter lead to better results in comparison with a typical elitist genetic algorithm, with relative improvement varying from 1.06% to 10.34% according to the dataset’s size.", "keywords": [ "Selection (genetic algorithm)", "Population", "Premature convergence", "Genetic algorithm", "Offspring", "Limit (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ijece.iaescore.com/index.php/IJECE/article/download/39979/18724" }, { "id": "https://openalex.org/W7113114858", "doi": null, "title": "Bir insansiz hava aracının kaydırılmış gözetleme görevleri için rotalanması problemi", "year": 2025, "publication_date": "2025-10-08", "type": "dissertation", "language": null, "authors": [ "Demir, Azize" ], "first_author": "Demir, Azize", "venue": "OpenMETU (Middle East Technical University)", "publisher": "Middle East Technical University", "abstract": "This thesis investigates the routing problem of an unmanned aerial vehicle (UAV) with a particular focus on offset surveillance operations, where the mission is executed from a certain distance and side. The primary objective is to formulate this real-life problem and provide a mathematical model for it. However, the computational results reveal that the exact solution approach becomes impractical for most of the problem sizes due to excessive solution times and memory requirements. Therefore, a metaheuristic approach based on a neighborhood search algorithm with simulated annealing is also designed to provide a solution approach for the management of such a daily operation. The main contribution of this study comes from the introduction of a novel variant of the UAV routing problem that reflects offset surveillance requirements. To the best of our knowledge, an unmanned aerial vehicle routing in offset surveillance has not been studied in the literature before, thereby opening a new line of research in operations research. In addition to that, it demonstrates the effectiveness of metaheuristic approaches for addressing the computational intractability of the exact solution approach in such problems by a practical solution methodology that can be adapted to real-world UAV surveillance applications.", "keywords": [ "Metaheuristic", "Simulated annealing", "Offset (computer science)", "Vehicle routing problem", "Focus (optics)", "Routing (electronic design automation)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11511/116158" }, { "id": "https://openalex.org/W1961924194", "doi": null, "title": "혼합정수 선형계획법과 유전 알고리듬을 이용한 다수 무인항공기 임무할당", "year": 2010, "publication_date": "2010-05-01", "type": "article", "language": "ko", "authors": [ "최현진", "서중보", "김유단" ], "first_author": "최현진", "venue": null, "publisher": null, "abstract": "This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.", "keywords": [ "Mathematical optimization", "Travelling salesman problem", "Integer programming", "Generalized assignment problem", "Weapon target assignment problem", "Assignment problem", "Task (project management)", "Heuristic", "Linear programming", "Computer science", "Computational complexity theory", "Optimization problem" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dspace.kci.go.kr/handle/kci/440068?show=full" }, { "id": "https://openalex.org/W4312441019", "doi": "10.1007/978-3-031-18192-4_32", "title": "Multi-Agent Routing Optimization for Underwater Monitoring", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Emiliano Traversi", "Igor V. Zhilin", "Alya Almarzooqi", "Ahmed Alketbi", "Abdulla Al Saadi Al Mansoori", "G. Masi" ], "first_author": "Emiliano Traversi", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Underwater", "Routing (electronic design automation)", "Computer science", "Motion planning", "Real-time computing", "Vehicle routing problem", "Drone", "Coral reef", "Marine engineering", "Robot", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-18192-4_32" }, { "id": "https://openalex.org/W7127967683", "doi": "10.1080/00207543.2026.2625955", "title": "The multi-type carrier pickup and delivery problem with time windows", "year": 2026, "publication_date": "2026-02-05", "type": "article", "language": "en", "authors": [ "Junhui Zhao", "Kaibo Liang", "Huwei Liu", "Fujun Hou", "Huimin Zhang" ], "first_author": "Junhui Zhao", "venue": "International Journal of Production Research", "publisher": "Taylor & Francis", "abstract": "This paper introduces a novel variant of the Vehicle Routeing Problem with Time Windows (VRPTW) in last-mile logistics. Distinct from the existing literature, our problem formulation innovatively incorporates the collaborative routeing of a heterogeneous fleet comprising a truck, a drone, and an autonomous ground vehicle (AGV). The objective is to minimise the total operational cost while serving customers with simultaneous pickup and delivery demands. The optimisation is subject to the specific payload and operational constraints of each vehicle type. To address this problem, we first present a mathematical model. We then propose a tailored hybrid Particle Swarm Optimisation algorithm, which is specifically designed to handle the problem's inherent complexities, such as its hierarchical decision structure, heterogeneous vehicle constraints, and tight spatio-temporal coupling. The algorithm integrates an adaptive perturbation strategy with a local search mechanism inspired by Variable Neighbourhood Descent, and it is enhanced with several problem-specific operators, including a time-window repair strategy and a cross-carrier task-swapping procedure. Computational experiments demonstrate that the proposed collaborative system achieves an average cost reduction of 36.52% compared to a traditional truck-only system.", "keywords": [ "Pickup", "Production (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/00207543.2026.2625955" }, { "id": "https://openalex.org/W2980915291", "doi": "10.13016/6bmq-arfg", "title": "DATA-DRIVEN OPTIMIZATION AND STATISTICAL MODELING TO IMPROVE DECISION MAKING IN LOGISTICS", "year": 2019, "publication_date": "2019-01-01", "type": "dissertation", "language": "en", "authors": [ "Debdatta Sinha Roy" ], "first_author": "Debdatta Sinha Roy", "venue": "University Libraries (University of Maryland)", "publisher": "University of Maryland, College Park", "abstract": "In this dissertation, we develop data-driven optimization and statistical modeling techniques to produce practically applicable and implementable solutions to real-world logistics problems. First, we address a significant and practical problem encountered by utility companies. These companies collect usage data from meters on a regular basis. Each meter has a signal transmitter that is automatically read by a receiver within a specified distance using radio-frequency identification (RFID) technology. The RFID signals are discontinuous, and each meter differs with respect to the specified distance. These factors could lead to missed reads. We use data analytics, optimization, and Bayesian statistics to address the uncertainty. Second, we focus on an important problem experienced by delivery and service companies. These companies send out vehicles to deliver customer products and provide services. For the capacitated vehicle routing problem, we show that reducing route-length variability while generating the routes is an important consideration to minimize the total operating and delivery costs for a company when met with random traffic. Third, we address a real-time decision-making problem experienced in practice. In one application, routing companies participating in competitive bidding might need to respond to a large number of requests regarding route costs in a very short amount of time. In another application, during post-disaster aerial surveillance planning or using drones to deliver emergency medical supplies, route-length estimation would quickly need to assess whether the duration to cover a region of interest would exceed the drone battery life. For the close enough traveling salesman problem, we estimate the route length using information about the instances. Fourth, we address a practical problem encountered by local governments. These organizations carry out road inspections to decide which street segments to repair by recording videos using a camera mounted on a vehicle. The vehicle taking the videos needs to proceed straight or take a left turn to cover an intersection fully. Right turns and U-turns do not capture an intersection fully. We introduce the intersection inspection rural postman problem, a new variant of the rural postman problem involving turns. We develop two integer programming formulations and three heuristics to generate least-cost vehicle routes.", "keywords": [ "Computer science", "Decision-making models", "Operations research", "Data science", "Engineering", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13016/6bmq-arfg" }, { "id": "https://openalex.org/W1765662443", "doi": null, "title": "Logistics Supply of the Distributed Air Wing", "year": 2014, "publication_date": "2014-09-01", "type": "dissertation", "language": "en", "authors": [ "Chee Siong Ong" ], "first_author": "Chee Siong Ong", "venue": "Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)", "publisher": "Naval Postgraduate School", "abstract": "The use of the aircraft carrier has been the norm for delivering sizable amounts of air power swiftly to any part of the world. A capstone project, conducted by the system engineering curriculum, proposed to distribute the air assets from the aircraft carrier to multiple Expeditionary Airbases (EABs), which are land bases located within the operating theater. This thesis studies the logistical demands of the EABs, and adopts the Marine Aviation Logistics Support Program II (MALSP II) concept for the logistics supply of the Distributed Air Wing. Airship, fixed wing Unmanned Air Vehicle (UAV), and rotary wing UAV are explored as the main cargo transportation means. This thesis develops a vehicle routing optimization model to optimize the transportation fleet size and mix, and a discrete event simulation to analyze the logistics concept. Experiments are conducted to determine the feasibility and cost-effectiveness of using cargo UAVs, using cargo trucks as a reference for comparisons. All platforms achieved the three days’ turnaround time, as stipulated by MALSP II. The airship is found to be the most cost-effective solution. Rotary wing and fixed wing UAVs deliver their supplies much faster, but are more suitable for quick response missions, instead of large cargo deliveries.", "keywords": [ "Aviation", "Fixed wing", "Truck", "Engineering", "Traverse", "Fuel efficiency", "Aeronautics", "Transport engineering", "Systems engineering", "Wing", "Automotive engineering", "Aerospace engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/10945/43969" }, { "id": "https://openalex.org/W4405803567", "doi": "10.1007/978-981-97-7774-7_6", "title": "Coverage Path Planning of Multiple Disconnected Convex Polygons Based on Improved Genetic Algorithm", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Tianmi Hu", "Shuyue Wang", "Yang Lyu", "Xinkai Liang", "Quan Pan" ], "first_author": "Tianmi Hu", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Regular polygon", "Path (computing)", "Computer science", "Genetic algorithm", "Algorithm", "Mathematical optimization", "Combinatorics", "Mathematics", "Geometry", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-981-97-7774-7_6" }, { "id": "https://openalex.org/W4229871090", "doi": "10.1201/b22485-3", "title": "Orienteering and Coverage", "year": 2018, "publication_date": "2018-03-14", "type": "book-chapter", "language": "en", "authors": [ "Yasmina Bestaoui Sebbane" ], "first_author": "Yasmina Bestaoui Sebbane", "venue": null, "publisher": null, "abstract": "In this chapter, operations concern generic aerial robotic problems, such as orienteering , and coverage in applications such as surveillance, search and rescue, geo-location, exploration, monitoring, mapping, etc. The use of UAVs, instead of ground robots, provides several advantages. The capacity to fly allows avoiding obstacles and having a bird’s-eye view. A Minkowski sum of an obstacle expands the obstacle according to the UAV’s size, while simultaneously, the UAV shrinks to a reference point. In large outdoors, the Minkowski sum represents the inaccessible area for the UAV. If the Minkowski sums of different obstacles intersect, then they will be merged into one to form a closed inaccessible area for the UAV. The space outside the union region is regarded as the free space for the UAV, and consequently, the UAV can follow paths in it [ 59 ]. If they have a small size, they can also navigate in narrow outdoor and indoor environments and they represent only a limited invasive impact. Before proceeding further on orienteering and coverage, some basic features of operational research involved in UAV missions are presented. These are the vehicle routing problem, traveling salesperson problem, postperson problem and knapsack problem. The material in this section is not intended to be exhaustive, but rather to provide a sufficient introduction to those who may be unfamiliar with such topics.", "keywords": [ "Orienteering", "Computer science", "Geography", "Cartography" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/b22485-3" }, { "id": "https://openalex.org/W4416237954", "doi": "10.1007/s10586-025-05753-8", "title": "Adaptive particle swarm optimization with Multi-Heuristic initializations for Truck–UGV collaborative routing for emergency material distribution", "year": 2025, "publication_date": "2025-11-15", "type": "article", "language": "en", "authors": [ "Xun Weng", "Fangwen Bu", "Hong-Qiang Fan", "Jingtian Zhang", "Lifen Yun" ], "first_author": "Xun Weng", "venue": "Cluster Computing", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Truck", "Particle swarm optimization", "Heuristic", "Vehicle routing problem", "Convergence (economics)", "Routing (electronic design automation)", "Greedy algorithm", "Swarm behaviour" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10586-025-05753-8" }, { "id": "https://openalex.org/W7163846974", "doi": null, "title": "Fullständig täckningsplanering för flera UAV:er : En kontextmedveten och begränsningstyrd metod med hänsyntill tankning och återbesök", "year": 2026, "publication_date": "2026-01-01", "type": "dissertation", "language": "en", "authors": [ "Jacob Persson", "Adam Mejri" ], "first_author": "Jacob Persson", "venue": "Diva portal (Dalarna University Library)", "publisher": null, "abstract": "Unmanned aerial vehicles (UAVs) are increasingly used in surveillance andsearch missions where large areas must be monitored repeatedly over extendedperiods of time. Such missions require persistent revisitation, fuel-awareoperation, and the ability to adapt when mission conditions change. Thisthesis, conducted in collaboration with UMS Skeldar, develops and evaluates aplanning framework for complete and persistent multi-UAV coverage underdynamic operational constraints. The proposed framework combines area decomposition, motion-constrainedcoverage path generation, fuel-aware routing, and iterative replanning. Themission area is partitioned into coverage regions, feasible sweep paths aregenerated for each region, and the resulting assignment and routing problemis approximated as a vehicle routing problem with time windows. The iterativestructure allows the routing problem to be reconstructed from the currentmission state when replanning is required. The framework is evaluated through solver benchmarking, partitioning studies,refueling experiments, and adaptive replanning under UAV unavailability. Theresults show that solver configuration, decomposition strategy, regiongranularity, and refueling policy all have a substantial impact on missionperformance. In the tested scenarios, PCI+LNS+Tabu provides the mostconsistent solver performance. Boustrophedon-based decomposition improveslocal sweep efficiency, while Voronoi-based partitioning improves routingflexibility and enables explicit control over task granularity. Proactiverefueling strategies preserve persistent coverage more effectively thanreactive refueling. The replanning evaluation shows that unfinished tasks canbe redistributed after UAV unavailability, maintaining near-nominal effectivecovered area at the cost of increased mission time and deadline delay. Overall, the thesis demonstrates that a decomposed, fuel-aware, anditeratively updated framework provides a practical basis for persistentmulti-UAV surveillance planning.", "keywords": [ "Solver", "Routing (electronic design automation)", "Decomposition", "Task (project management)", "Vehicle routing problem", "Path (computing)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-224467" }, { "id": "https://openalex.org/W4388963725", "doi": "10.48550/arxiv.2311.13489", "title": "Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning", "year": 2023, "publication_date": "2023-11-22", "type": "preprint", "language": "en", "authors": [ "Arun Narayanan", "Evangelos Pournaras", "Pedro H. J. Nardelli" ], "first_author": "Arun Narayanan", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Unmanned aerial vehicles (UAVs) have significant practical advantages for delivering packages, and many logistics companies have begun deploying UAVs for commercial package deliveries. To deliver packages quickly and cost-effectively, the routes taken by UAVs from depots to customers must be optimized. This route optimization problem, a type of capacitated vehicle routing problem, has recently attracted considerable research interest. However, few papers have dealt with large-scale deliveries, where the number of customers exceed 1000. We present an innovative, practical package delivery model wherein multiple UAVs deliver multiple packages to customers who are compensated for late deliveries. Further, we propose an innovative methodology that combines a new plan-generation algorithm with a collective-learning heuristic to quickly determine cost-effective paths of UAVs even for large-scale deliveries up to 10000 customers. Specialized settings are applied to a collective-learning heuristic, the Iterative Economic Planning and Optimized Selections (I-EPOS) in order to coordinate collective actions of the UAVs. To demonstrate our methodology, we applied our highly flexible approach to a depot in Heathrow Airport, London. We show that a coordinated approach, in which the UAVs collectively determine their flight paths, leads to lower operational costs than an uncoordinated approach. Further, the coordinated approach enables large-scale package deliveries.", "keywords": [ "Computer science", "Heuristic", "Drone", "Scale (ratio)", "Plan (archaeology)", "Vehicle routing problem", "Routing (electronic design automation)", "Operations research", "Artificial intelligence", "Engineering", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2311.13489" }, { "id": "https://openalex.org/W7131438660", "doi": "10.5281/zenodo.18715260", "title": "Swarm-Steward Benchmark Data: Feature RAG, Swarm Scalability, and LLM Model Evaluation Artifacts", "year": 2026, "publication_date": "2026-02-23", "type": "dataset", "language": null, "authors": [ "Alejandro Jarabo-Peñas", "Juan Bravo Arrabal", "Edouard G. A. Rolland", "Anders Lyhne Christensen" ], "first_author": "Alejandro Jarabo-Peñas", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract Swarm-Steward is a platform-agnostic system for natural-language swarm control that enables a non-expert operator to coordinate many drones by creating and commanding groups, bridging high-level intent to reliable, low-level execution. The system uses a hierarchical LLM-based multi-agent design where planning and context gathering are separated from actuation: a tool-less Coordinator decomposes each request into staged sub-tasks and delegates them to specialised agents — a Spatial agent and a History agent for non-actuating context gathering (map relations, telemetry), and a Swarm agent as the sole authority permitted to modify world state by dispatching deterministic, schema-constrained commands through a safety gate (geofencing, altitude and separation limits), with optional operator preview before execution. To keep grounding scalable, Swarm-Steward applies dual retrieval-augmented generation over both map features (Feature RAG) and telemetry variables (State RAG), injecting only relevant candidates at each step. The system supports both MQTT and ROS 2/DDS communication backends and has been validated in simulation as well as on real DJI Mini 4 Pro drones via WildBridge. This dataset accompanies the paper \"Swarm-Steward: Scalable and Reliable Natural-Language Coordination of Autonomous Aerial and Ground Robots\", submitted to the International Conference on Unmanned Aircraft Systems (ICUAS 2026). It provides the complete experimental evidence for the three benchmarks reported in the paper (§V): Feature RAG retrieval quality (§V-A), swarm scalability from 5 to 500 drones (§V-B), and a 24-model LLM comparison (§V-C), together with demonstration images from simulation-to-real validation with DJI drones (§V-D). Contents The deposit contains 5 structured CSV files, 1 raw JSON trace (21 MB), 2 interactive HTML reports, 14 publication-quality figures (PDF), and 4 demonstration images. Benchmark A — Feature RAG Retrieval Quality (§V-A) Evaluates the Feature RAG module that grounds free-form geographic references by embedding map-feature descriptions and retrieving the top-k candidates most similar to the user query. Four embedding models — two cloud (OpenAI text-embedding-3-small, 1,536-d; Google gemini-embedding-001, 3,072-d) and two local (BAAI/bge-m3, 1,024-d; intfloat/e5-large-v2, 1,024-d) — are benchmarked on three operational scenarios (Madrid, 356 features; Málaga, 108; Stockholm, 272) augmented with in-domain synthetic noise (same-category near-duplicates) up to 10,000 features. Metrics: Hit Rate at k=10 (HR@10) and Mean Reciprocal Rank (MRR), computed over four query difficulty levels (exact name, partial name, category, descriptive) with three repetitions per condition. Principal finding: BGE-M3 (1,024-d, local inference on a commodity laptop GPU with 4 GB VRAM, ~22 ms latency) achieves the highest mean HR@10 of 87% and MRR of 0.79 at the 10,000-feature pool; higher-dimensional cloud models do not confer an advantage. Category queries reach 100% HR@10 across all models; partial-name queries are the primary discriminator. Search latency stays below 21 ms at all pool sizes, and only a fixed top-k set enters prompts, so scaling the map does not inflate LLM context. Data files: rag_results.csv (576 rows) — Aggregate metrics per experimental condition (model × scenario × pool size × query type × repetition) rag_queries.csv (8,112 rows) — Individual query-level results with retrieved feature names and latencies Figures: rag_scalability.pdf — HR@10 vs. pool size (2×2 grid by query type), one line per model rag_model_heatmap.pdf — Model × query-type heatmap at the 10K-feature pool rag_per_scenario.pdf — Per-scenario 3×4 breakdown (scenario × query type) rag_noise_comparison.pdf — Generic vs. in-domain noise robustness comparison (3×4 grid) Benchmark B — Swarm and Group Scalability (§V-B) Evaluates how the multi-agent pipeline scales with fleet size across two orders of magnitude: 5, 10, 25, 50, 100, 200, and 500 simulated drones. Each configuration is executed 4 times (28 sessions, 252 prompts total). Each session executes a fixed sequence of 9 natural-language prompts covering group takeoff + move, dynamic grouping, formation traversals, feature orbits, area coverage, battery-conditional queries (History agent + State RAG), speed-based drone selection, and coordinated return-to-home — exercising all four agent types in the pipeline. The MQTT-based simulator backend is used to isolate LLM orchestration performance from network and DDS transport variability. The LLM backbone is Llama-3.3-70B (4-bit quantised, cloud-hosted via the Groq API). The orchestration pipeline, Docker containers, and MQTT simulator ran on a desktop PC with an AMD Ryzen 9 5950X (16 cores), 32 GB DDR4 RAM, and an NVIDIA RTX 4090 GPU, under Ubuntu 22.04. Principal finding: total token consumption per session remains effectively constant at 546k ± 12% regardless of fleet size, confirming O(1) cost complexity enabled by group-level abstraction — the Swarm agent reasons over k ∈ [2, 40] groups rather than individual drones. 245 of 252 prompts succeed (97.2% overall). All 7 failures are concentrated at fleet sizes N ≥ 100 and share the same signature: the Coordinator produces a correct plan, but the Swarm agent completes zero LLM calls despite being invoked, pointing to external LLM API errors — most likely rate limiting under sustained benchmark load, with potential context-window overruns at N = 500 where Swarm agent input tokens can reach ~137k (exceeding the 128k context window). No failures stem from incorrect LLM reasoning, planning, or tool generation. Mean LLM pipeline latency is 6.8 s across all sizes. Data files: sessions.csv (28 rows) — Session-level summaries (total tokens, success rate, latency, payload size) prompts.csv (252 rows) — Per-prompt measurements (latency breakdown, token counts, action outcomes) agent_executions.csv (777 rows) — Per-agent-per-prompt breakdowns (LLM calls, tool invocations, to", "keywords": [ "Drone", "Scalability", "Swarm behaviour", "Context (archaeology)", "JSON", "Feature (linguistics)", "TRACE (psycholinguistics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18715260" }, { "id": "https://openalex.org/W7155711115", "doi": "10.5281/zenodo.19737360", "title": "Swarm-Steward Benchmark Data: Feature RAG, Swarm Scalability, and LLM Model Evaluation Artifacts", "year": 2026, "publication_date": "2026-04-26", "type": "dataset", "language": null, "authors": [ "Alejandro Jarabo-Peñas", "Juan Bravo Arrabal", "Edouard G. A. Rolland", "Anders Lyhne Christensen" ], "first_author": "Alejandro Jarabo-Peñas", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract Swarm-Steward is a platform-agnostic system for natural-language swarm control that enables a non-expert operator to coordinate many drones by creating and commanding groups, bridging high-level intent to reliable, low-level execution. The system uses a hierarchical LLM-based multi-agent design where planning and context gathering are separated from actuation: a tool-less Coordinator decomposes each request into staged sub-tasks and delegates them to specialised agents — a Spatial agent and a History agent for non-actuating context gathering (map relations and telemetry), and a Swarm agent as the sole authority permitted to modify world state by dispatching deterministic, schema-constrained commands through a safety gate (geofencing, altitude and separation limits), with optional operator preview before execution. To keep grounding scalable, Swarm-Steward applies dual retrieval-augmented generation over both map features (Feature RAG) and telemetry variables (State RAG), injecting only relevant candidates at each step. The system supports both MQTT and ROS 2/DDS communication backends and has been validated in simulation as well as on real DJI Mini 4 Pro drones via WildBridge. This dataset accompanies the paper \"Swarm-Steward: Scalable and Reliable Natural-Language Coordination of Autonomous Aerial and Ground Robots\", submitted to the International Conference on Unmanned Aircraft Systems (ICUAS 2026). It provides the experimental evidence for the three benchmarks reported in the paper (§V): Feature RAG retrieval quality (§V-A), swarm scalability from 5 to 500 drones (§V-B), and a repeated 15-model mid-size LLM benchmark (§V-C), together with demonstration images from simulation-to-real validation with DJI drones (§V-D). Contents The deposit contains 5 structured CSV files, 1 raw JSON trace, 3 interactive HTML reports, 14 publication-quality figures (PDF), and 4 demonstration images. Benchmark A — Feature RAG Retrieval Quality (§V-A) Evaluates the Feature RAG module that grounds free-form geographic references by embedding map-feature descriptions and retrieving the top-k candidates most similar to the user query. Four embedding models — two cloud (OpenAI text-embedding-3-small, 1,536-d; Google gemini-embedding-001, 3,072-d) and two local (BAAI/bge-m3, 1,024-d; intfloat/e5-large-v2, 1,024-d) — are benchmarked across three operational scenarios augmented with in-domain synthetic noise (same-category near-duplicates) up to 10,000 features. Metrics: Hit Rate at k=10 (HR@10) and Mean Reciprocal Rank (MRR), computed over four query difficulty levels (exact name, partial name, category, descriptive) with three repetitions per condition. Principal finding: exact-name, partial-name, and category queries saturate at or near 100% HR@10 across all models, so descriptive queries are the primary discriminator. gemini-embedding-001 achieves the best aggregate score at the 10,000-feature pool (about 97% HR@10 and 0.86 MRR), while the two local models remain close in quality with substantially lower embedding latency. Search latency stays low at all pool sizes, and only a fixed top-k set enters prompts, so scaling the map does not inflate LLM context. Data files:- rag_results.csv — Aggregate metrics per experimental condition (model × scenario × pool size × query type × repetition)- rag_queries.csv — Individual query-level results with retrieved feature names and latencies Figures:- rag_scalability.pdf — HR@10 vs. pool size, one line per model- rag_model_heatmap.pdf — Model × query-type heatmap at the 10,000-feature pool- rag_per_scenario.pdf — Per-scenario breakdown- rag_noise_comparison.pdf — Generic vs. in-domain noise robustness comparison Benchmark B — Swarm and Group Scalability (§V-B) Evaluates how the multi-agent pipeline scales with fleet size across two orders of magnitude: 5, 10, 25, 50, 100, 200, and 500 simulated drones. Each configuration is executed 4 times, yielding 28 sessions and 280 prompts total. Each session executes a fixed sequence of 10 natural-language prompts covering group takeoff and movement, dynamic grouping, feature-referenced traversals and orbits, area coverage, telemetry-conditioned actions through the History agent and State RAG, nearest-feature inspection through the Spatial agent, and coordinated return-to-home. The MQTT-based simulator backend is used to isolate LLM orchestration performance from network and DDS transport variability. The LLM backbone is Llama-3.3-70B, hosted via the Groq API. The orchestration pipeline, Docker containers, and MQTT simulator ran on a desktop PC with an AMD Ryzen 9 5950X, 32 GB RAM, and an NVIDIA RTX 4090 GPU, under Ubuntu 22.04. Principal finding: total token consumption remains approximately constant with respect to fleet size at about 678k ± 15% tokens per session, confirming O(1) cost complexity enabled by group-level abstraction. The Swarm agent reasons over groups rather than individual drones, so the main scaling effect of increasing fleet size is a larger state payload rather than a proportional growth in LLM context. Mean LLM pipeline latency is about 5.1 s across all sizes, rising gradually from 4.4 s at N=5 to 7.9 s at N=500. 260 of 280 prompts succeed (92.9% overall). All configurations up to N=50 remain at or above 97.5% success, and the degradation appears mainly at N ≥ 100. Failures are concentrated in workload-sensitive prompts: multi-group partitioning can exceed the Swarm agent’s single-pass tool-call budget, identifying the southernmost drone becomes harder as more vehicles share similar latitude values, and some prompts at N=500 exceed the 131,072-token context window. These failures do not reflect incorrect Coordinator planning; they arise downstream from execution-layer limits under larger payloads and group counts. Data files:- sessions.csv — Session-level summaries (total tokens, success rate, latency, payload size)- prompts.csv — Per-prompt measurements (latency breakdown, token counts, action outcome", "keywords": [ "Drone", "Scalability", "Swarm behaviour", "Context (archaeology)", "JSON", "Benchmark (surveying)", "Feature (linguistics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.19737360" }, { "id": "https://openalex.org/W7131391825", "doi": "10.5281/zenodo.18715259", "title": "Swarm-Steward Benchmark Data: Feature RAG, Swarm Scalability, and LLM Model Evaluation Artifacts", "year": 2026, "publication_date": "2026-04-26", "type": "dataset", "language": null, "authors": [ "Alejandro Jarabo-Peñas", "Juan Bravo Arrabal", "Edouard G. A. Rolland", "Anders Lyhne Christensen" ], "first_author": "Alejandro Jarabo-Peñas", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "Abstract Swarm-Steward is a platform-agnostic system for natural-language swarm control that enables a non-expert operator to coordinate many drones by creating and commanding groups, bridging high-level intent to reliable, low-level execution. The system uses a hierarchical LLM-based multi-agent design where planning and context gathering are separated from actuation: a tool-less Coordinator decomposes each request into staged sub-tasks and delegates them to specialised agents — a Spatial agent and a History agent for non-actuating context gathering (map relations and telemetry), and a Swarm agent as the sole authority permitted to modify world state by dispatching deterministic, schema-constrained commands through a safety gate (geofencing, altitude and separation limits), with optional operator preview before execution. To keep grounding scalable, Swarm-Steward applies dual retrieval-augmented generation over both map features (Feature RAG) and telemetry variables (State RAG), injecting only relevant candidates at each step. The system supports both MQTT and ROS 2/DDS communication backends and has been validated in simulation as well as on real DJI Mini 4 Pro drones via WildBridge. This dataset accompanies the paper \"Swarm-Steward: Scalable and Reliable Natural-Language Coordination of Autonomous Aerial and Ground Robots\", submitted to the International Conference on Unmanned Aircraft Systems (ICUAS 2026). It provides the experimental evidence for the three benchmarks reported in the paper (§V): Feature RAG retrieval quality (§V-A), swarm scalability from 5 to 500 drones (§V-B), and a repeated 15-model mid-size LLM benchmark (§V-C), together with demonstration images from simulation-to-real validation with DJI drones (§V-D). Contents The deposit contains 5 structured CSV files, 1 raw JSON trace, 3 interactive HTML reports, 14 publication-quality figures (PDF), and 4 demonstration images. Benchmark A — Feature RAG Retrieval Quality (§V-A) Evaluates the Feature RAG module that grounds free-form geographic references by embedding map-feature descriptions and retrieving the top-k candidates most similar to the user query. Four embedding models — two cloud (OpenAI text-embedding-3-small, 1,536-d; Google gemini-embedding-001, 3,072-d) and two local (BAAI/bge-m3, 1,024-d; intfloat/e5-large-v2, 1,024-d) — are benchmarked across three operational scenarios augmented with in-domain synthetic noise (same-category near-duplicates) up to 10,000 features. Metrics: Hit Rate at k=10 (HR@10) and Mean Reciprocal Rank (MRR), computed over four query difficulty levels (exact name, partial name, category, descriptive) with three repetitions per condition. Principal finding: exact-name, partial-name, and category queries saturate at or near 100% HR@10 across all models, so descriptive queries are the primary discriminator. gemini-embedding-001 achieves the best aggregate score at the 10,000-feature pool (about 97% HR@10 and 0.86 MRR), while the two local models remain close in quality with substantially lower embedding latency. Search latency stays low at all pool sizes, and only a fixed top-k set enters prompts, so scaling the map does not inflate LLM context. Data files:- rag_results.csv — Aggregate metrics per experimental condition (model × scenario × pool size × query type × repetition)- rag_queries.csv — Individual query-level results with retrieved feature names and latencies Figures:- rag_scalability.pdf — HR@10 vs. pool size, one line per model- rag_model_heatmap.pdf — Model × query-type heatmap at the 10,000-feature pool- rag_per_scenario.pdf — Per-scenario breakdown- rag_noise_comparison.pdf — Generic vs. in-domain noise robustness comparison Benchmark B — Swarm and Group Scalability (§V-B) Evaluates how the multi-agent pipeline scales with fleet size across two orders of magnitude: 5, 10, 25, 50, 100, 200, and 500 simulated drones. Each configuration is executed 4 times, yielding 28 sessions and 280 prompts total. Each session executes a fixed sequence of 10 natural-language prompts covering group takeoff and movement, dynamic grouping, feature-referenced traversals and orbits, area coverage, telemetry-conditioned actions through the History agent and State RAG, nearest-feature inspection through the Spatial agent, and coordinated return-to-home. The MQTT-based simulator backend is used to isolate LLM orchestration performance from network and DDS transport variability. The LLM backbone is Llama-3.3-70B, hosted via the Groq API. The orchestration pipeline, Docker containers, and MQTT simulator ran on a desktop PC with an AMD Ryzen 9 5950X, 32 GB RAM, and an NVIDIA RTX 4090 GPU, under Ubuntu 22.04. Principal finding: total token consumption remains approximately constant with respect to fleet size at about 678k ± 15% tokens per session, confirming O(1) cost complexity enabled by group-level abstraction. The Swarm agent reasons over groups rather than individual drones, so the main scaling effect of increasing fleet size is a larger state payload rather than a proportional growth in LLM context. Mean LLM pipeline latency is about 5.1 s across all sizes, rising gradually from 4.4 s at N=5 to 7.9 s at N=500. 260 of 280 prompts succeed (92.9% overall). All configurations up to N=50 remain at or above 97.5% success, and the degradation appears mainly at N ≥ 100. Failures are concentrated in workload-sensitive prompts: multi-group partitioning can exceed the Swarm agent’s single-pass tool-call budget, identifying the southernmost drone becomes harder as more vehicles share similar latitude values, and some prompts at N=500 exceed the 131,072-token context window. These failures do not reflect incorrect Coordinator planning; they arise downstream from execution-layer limits under larger payloads and group counts. Data files:- sessions.csv — Session-level summaries (total tokens, success rate, latency, payload size)- prompts.csv — Per-prompt measurements (latency breakdown, token counts, action outcome", "keywords": [ "Drone", "Scalability", "Swarm behaviour", "Context (archaeology)", "JSON", "Feature (linguistics)", "TRACE (psycholinguistics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.18715259" }, { "id": "https://openalex.org/W4387891827", "doi": "10.48550/arxiv.2310.13324", "title": "ColAG: A Collaborative Air-Ground Framework for Perception-Limited UGVs' Navigation", "year": 2023, "publication_date": "2023-10-20", "type": "preprint", "language": "en", "authors": [ "Zhehan Li", "Mao, Rui", "Nanhe Chen", "Chao Xu", "Gao, Fei", "Yanjun Cao" ], "first_author": "Zhehan Li", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It's challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a $\\textit{blind}$ robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named $\\textbf{ColAG}$, to solve the problem of autonomous navigation for a group of $\\textit{blind}$ UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV's trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.", "keywords": [ "Odometry", "Robot", "Computer science", "Perception", "Artificial intelligence", "Routing (electronic design automation)", "Real-time computing", "Plan (archaeology)", "Visual odometry", "Computer vision", "Simulation", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2310.13324" }, { "id": "https://openalex.org/W4230273452", "doi": "10.17077/etd.005857", "title": "Deep Q-learning for same-day delivery", "year": 2021, "publication_date": "2021-08-01", "type": "dissertation", "language": "en", "authors": [ "Xinwei Chen", "Barrett W. Thomas", "Marlin Wolf Ulmer", "Tong Wang", "Ann Melissa Campbell", "Jeffrey W. Ohlmann" ], "first_author": "Xinwei Chen", "venue": null, "publisher": null, "abstract": "Same-day delivery (SDD) changes the way people shop as it combines immediate product availability and the convenience of ordering from electronic devices. The SDD market has seen a fast growth over the past decade. These services have further gained popularity during the COVID-19 pandemic. Yet, even before the surge of demand brought on by the pandemic, providing SDD was a challenge. The timing of requests and the delivery locations are not known until a request is made. In addition, SDD requires tight delivery deadlines and immediate response to customer requests. To address these challenges, this thesis seeks to provide an innovative deep Q-learning approach for studying SDD. Q-learning is a form of reinforcement learning that learns the value of state-action pairs. Deep Q-learning uses neural networks to approximate the state-action values. We will investigate the effectiveness of the approach to two emerging SDD problems. First, to complement the inefficient use of conventional vehicles, companies have begun to use drones for SDD. To effectively exploit the different strengths of vehicles and drones (e.g. capacity, speed, etc.), we first consider the same-day delivery problem with vehicles and drones (SDDPVD). Over the course of a day, customers make delivery requests, and the dispatcher dynamically assigns vehicles and drones to deliver the goods. The objective is to maximize the expected number of customers served. We model the SDDPVD as a sequential decision process and develop a deep Q-learning solution approach. Our method learns the value of assigning a new customer to either drones or vehicles as well as the option to not offer service at all. In a systematic computational analysis, we show the superiority of our policy compared to benchmark policies and the effectiveness of our deep Q-learning approach. We also show that the combination of state and action features is very valuable and that our policy can maintain effectiveness when demand data and the fleet size change moderately. Second, in studying the SDDPVD, we discover that, when the delivery resources are constrained, customers that are farthest away from the depot or from other customers are less likely to receive the service, raising concerns about fairness in customer service. In fact, the industry has been battling with concerns about the fairness in these delivery services. In 2016, Amazon was accused of excluding certain minority neighborhoods from its SDD map. To address the emerging concerns about fairness, we then consider the problem of SDD with fair customer service (SDDFCS). The objective combines the historical measure of maximizing the expected number of requests served by the company (utility) with a measure of the expected minimal regional service rate (fairness). We model the SDDFCS as a multi-objective Markov decision process. We introduce a novel transformation of objective from optimizing rates to number of services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of the deep Q-learning approach in reducing unfairness in customer service. This effectiveness is valid with different depot locations, providing businesses with opportunity to achieve better fairness from any location. Finally, we show that ignoring fairness in services results in a long-term loss in overall revenue.", "keywords": [ "Present day", "Artificial intelligence", "Computer science", "Physics", "Astronomy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.17077/etd.005857" }, { "id": "https://openalex.org/W7163059659", "doi": "10.2139/ssrn.6864657", "title": "Optimal Placement of Multiple Unmanned Aerial Vehicles for Offshore Wind Farms Using a Dynamic Embedded Utilization Evaluator", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Hayder Al-Husseinawi", "Mien Van", "Seán McLoone" ], "first_author": "Hayder Al-Husseinawi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Benchmark (surveying)", "Particle swarm optimization", "Turbine", "Offshore wind power", "Constraint (computer-aided design)", "Function (biology)", "Heuristic", "Key (lock)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6864657" }, { "id": "https://openalex.org/W4402326276", "doi": "10.7302/24059", "title": "Multidisciplinary Design Optimization of Delivery Uncrewed Aerial Vehicles Considering Operations", "year": 2024, "publication_date": "2024-01-01", "type": "dissertation", "language": "en", "authors": [ "Shugo Kaneko" ], "first_author": "Shugo Kaneko", "venue": "Deep Blue (University of Michigan)", "publisher": "University of Michigan", "abstract": "Uncrewed aerial vehicles (UAVs) enable rapid and environmentally friendly delivery of lightweight payloads, such as commercial packages, food, and medical supplies. Designing an energy-efficient UAV is the key to achieving the full potential of UAV delivery and lowering the environmental impact. To do so, multidisciplinary design optimization (MDO) is a powerful tool that assists engineers with design tasks and accelerates design space exploration by automatically finding the optimal design given the requirements and assumptions. This dissertation focuses on conceptual design optimization of delivery UAVs considering flight operations. Incorporating flight operational analysis and optimization in the UAV conceptual design process is important because the expected flight operations drive the design requirements, hence the UAV design. At the same time, the optimal flight operations are dependent on the UAV design parameters, meaning that the optimal UAV design and flight operations are mutually coupled. I apply MDO to capture this design-operation coupling, which ultimately improves the design and performance of delivery UAVs. In the first part of this dissertation, I present design optimization of a UAV fleet considering delivery operations. This problem simultaneously optimizes the fleet size and composition, UAV design variables, and delivery routing to design a fleet that fulfills expected delivery demands. To solve this mixed-integer nonlinear optimization problem, I propose an effective sequential heuristic algorithm that combines gradient-based design optimization and vehicle routing heuristics. I then demonstrate the accuracy, computational efficiency, and scalability of the proposed algorithm by comparing it to a commercial branch-and-cut solver. The results show that simultaneous fleet design and routing optimization reduces the fleet acquisition cost and delivery energy consumption by up to 20% compared to conventional uncoupled optimization. In the second part, I perform UAV conceptual design optimization considering takeoff flight operations, focusing on a single vehicle design in higher resolution. This problem yields simultaneous optimization of UAV design and takeoff trajectory, and I apply gradient-based optimization to solve it efficiently. To reduce the computational cost of gradient-based optimization, I propose new hierarchical linear solution strategies to accelerate derivative computations. I also perform benchmark studies of monolithic MDO architectures and design-trajectory coupling strategies to identify the best approach in terms of computational cost. The results of this dissertation show that simultaneous optimization can find a more energy-efficient UAV design compared to uncoupled optimization. This is achieved by capturing the system-level trade-offs between the efficiency in different flight phases, which uncoupled optimization cannot fully address. This demonstrates the importance of incorporating takeoff trajectory optimization in the conceptual design process of delivery UAVs.", "keywords": [ "Multidisciplinary approach", "Multidisciplinary design optimization", "Computer science", "Engineering", "Sociology" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.7302/24059" } ]