Xiaolong Tian, Jin Peng, and Nan Wang
[1] Y. Yan, Z. Lv, J. Yuan, and S. Zhang, Obstacle avoidancefor multi-UAV system with optimized artificial potential fieldalgorithm, International Journal of Robotics and Automation,36(2), 2021,1–7. [2] A. Davies and G. Krame, Integrating body-worn cameras,drones, and AI: A framework for enhancing police readinessand response, Policing: A Journal of Policy and Practice,5(78), 2023, 835–841. [3] E. Babikir, Review on airborne nodes for unmanned aerialvehicles (UAV), IRO Journal on Sustainable Wireless Systems,6(3), 2024, 199–210. [4] Y. Chen, Q. Dong, X. Shang, Z. Wu, and J. Wang, Multi-UAV autonomous path planning in reconnaissance missionsconsidering incomplete information: A reinforcement learningmethod, Drones, 7(1) 2022, 1012–1023. [5] H. Lee, S. Ahmed, and C. Lee, Research on path planningof logistics intelligent unmanned aerial vehicle, InternationalJournal of Robotics and Automation, 39(6), 2024, 450–463.10 [6] C. Huang, Y. Lan, Y. Liu, W. Zhou, H. Pei, L. Yang, Y.Cheng, Y. Hao, and Y. Peng, A new dynamic path planningapproach for unmanned aerial vehicles, Complexity, 18(1),2018, 156–168. [7] J. Berger, A. Boukhtouta, A, Benmoussa, and O. Kettani, Anew mixed-integer linear programming model for rescue pathplanning in uncertain adversarial environment, Computers &Operations Research, 39(12), 2012, 3420–3430. [8] J. Li, C. Liao, W. Zhang, H. Fu, and S. Fu, UAV path planningmodel based on R5DOS model improved A-star algorithm,Applied Sciences, 12(22), 2022, 1138–1144. [9] J. Wang, Y. Li, R. Li, H. Chen, and K. Chu, Trajectoryplanning for UAV navigation in dynamic environments withmatrix alignment Dijkstra, Soft Computing, 26(22), 2022,12599–12610. [10] H. Song, M. Jia, and Y. Lian, UAV path planning based on animproved ant colony algorithm, Journal of Electronic Researchand Application, 6(2), 2022, 10–25. [11] H. Chen, Y. Liang, and X. Meng, A UAV path planning methodfor building surface information acquisition utilizing opposition-based learning artificial bee colony algorithm, Remote Sensing,15(17), 2023, 431–451. [12] J. Shin and H. Bang, UAV path planning under dynamic threatsusing an improved PSO algorithm, International Journal ofAerospace Engineering, 20(1), 2020, 882–897. [13] Y. Pehlivanoglu and P. Pehlivanoglu, An enhanced geneticalgorithm for path planning of autonomous UAV in targetcoverage problems, Applied Soft Computing, 1(12), 2021,1077–1081. [14] X. Xing, Z. Zhou, Y. Li, B. Xiao, and Y. Xun, Multi-UAVadaptive cooperative formation trajectory planning based onan improved MATD3 algorithm of deep reinforcement learning,IEEE Transactions on Vehicular Technology, 73(9), 2024,12484–12499. [15] O. Tutsoy, D. Asadi, K. Ahmadi, S. Nabavi-Chashmi, andJ. Iqbal, Minimum distance and minimum time optimal pathplanning with bioinspired machine learning algorithms forfaulty unmanned air vehicles, IEEE Transactions on IntelligentTransportation Systems, 25(8), 2024, 9069–9077. [16] X. Zhao, R. Yang, L. Zhong, and Z. Hou, Multi-UAV pathplanning and following based on multi-agent reinforcementlearning, Drones, 8(1), 2024, 18. [17] M. Rahman, N. Sarkar, and R. Lutui, A survey on multi-UAV path planning: Classification, algorithms, open researchproblems, and future directions, Drones, 9(4), 2025, 263–286. [18] J. Tang, H. Duan, and S. Lao, Swarm intelligence algorithmsfor multiple unmanned aerial vehicles collaboration: Acomprehensive review, Artificial Intelligence Review, 56(5),2023, 4295–4327. [19] J. Chen, F. Ling, Y. Zhang, T. You, Y. Liu, and X. Du,Coverage path planning of heterogeneous unmanned aerialvehicles based on ant colony system, Swarm and EvolutionaryComputation, 6(9), 2022, 101–115. [20] L.M.S. Bine, A. Boukerche, L.B. Ruiz, and A.A.F. Loureiro, Anovel ant colony-inspired coverage path planning for internetof drones, Computer Networks, 2(5), 2023, 89–95. [21] M. Shafiq, Z. Ali, A. Israr, E.H. Alkhammash, M. Hadjouni,and J.J. Jussila, Convergence analysis of path planning ofmulti-UAVs using max-min ant colony optimization approach,Sensors, 22(14), 2022, 531–541. [22] D. Qi, Z. Zhang, and Q. Zhang, Path planning of multirotorUAV based on the improved ant colony algorithm, Journal ofRobotics, 20(1), 2022, 216–223. [23] S. Lin, F. Li, X. Li, K. Jia, and X. Zhang, Improved artificialbee colony algorithm based on multi-strategy synthesis for UAVpath planning, IEEE Access, 10(2), 2022, 119269–119282. [24] C. Yongbo, M. Yuesong, Y. Jianqiao, S. Xiaolong, and X.Nuo, Three-dimensional unmanned aerial vehicle path planningusing modified wolf pack search algorithm, Neurocomputing,26(6), 2017, 445–457. [25] Z. Guoqi, B. Yu, and K. Huanyin, Multitask assignment ofswarming UAVs based on improved PSO, International Journalof Robotics and Automation, 36(3), 2021, 188–195. [26] S. Shao, Y. Peng, C. He, and Y. Du, Efficient path planning forUAV formation via comprehensively improved particle swarmoptimization, ISA Transactions, 9(7), 2020, 415–430. [27] H. Chu, J. Yi, and F. Yang, Chaos particle swarm optimizationenhancement algorithm for UAV safe path planning, AppliedSciences, 12(18), 2022, 256–263. [28] Y. Yan, Z. Lv, P. Huang, J. Yuan, and H. Long, Rapid selectingUAVs for combat based on three-way multiple attributedecision, International Journal of Robotics and Automation,36(1), 2021, 1–8.
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