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A mini review on UAV mission planning

  • *Corresponding author: Yu Ding

    *Corresponding author: Yu Ding

The authors are grateful for the National Key Research and Development Plan (2020YFB1709403); the National Natural Science Foundation of China (12102077); the Fundamental Research Funds for the Central Universities (DUT20YG125, DUT22RC(3)010)

Abstract Full Text(HTML) Figure(2) / Table(2) Related Papers Cited by
  • With the increasing complexity of modern air warfare, an efficient and robust mission planning, which mainly includes task assignment and path planning, becomes the key issue to improve the combat efficiency. This paper reviews recent progress in UAV mission planning. First, basic concepts of UAVs and their mission planning problem are given. And several representative existing mission planning systems are briefly introduced. The constraints and objectives in the task assignment model are reviewed, and the pros and cons of algorithms commonly used are then summarized. After that, the algorithms for path planning are reviewed. Finally, we point out current problems and future research directions. The paper provides a comprehensive review of the field and enables a quick start for those who aim to do related research.

    Mathematics Subject Classification: Primary: 90C59, 90C27; Secondary: 90B70.


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  • Figure 1.  Three typical UAV swarm architectures: centralized architecture, distributed architecture and mixed architecture

    Figure 2.  Development of US military mission planning systems

    Table 1.  Contents in existing review papers on UAV mission planning

    References Task assignment Path planning Re-planning Models Algorithm Analysis of Algorithms Problem analysis
    Zhao $ \surd $ $ \surd $ $ \surd $ $ \times $ $ \surd $ $ \times $ $ \surd $
    Guo $ \surd $ $ \times $ $ \times $ $ \surd $ $ \surd $ $ \surd $ $ \surd $
    Du $ \times $ $ \surd $ $ \times $ $ \surd $ $ \surd $ $ \times $ $ \times $
    Debnath $ \times $ $ \surd $ $ \times $ $ \surd $ $ \surd $ $ \times $ $ \surd $
    Aggarwal $ \surd $ $ \surd $ $ \surd $ $ \surd $ $ \surd $ $ \surd $ $ \surd $
    Jia $ \surd $ $ \surd $ $ \times $ $ \surd $ $ \surd $ $ \times $ $ \surd $
    Pang $ \surd $ $ \surd $ $ \times $ $ \surd $ $ \surd $ $ \surd $ $ \surd $
    Zhang $ \surd $ $ \times $ $ \times $ $ \times $ $ \surd $ $ \times $ $ \surd $
     | Show Table
    DownLoad: CSV

    Table 2.  Capability set of each type of UAV

    Type of UAV Capability set
    Surveillance UAV $\{C,V\}$
    Combat UAV $\{C, A, V\}$
    Munition UAV $\{A\}$
     | Show Table
    DownLoad: CSV
  • [1] I. Mahmud and Y. Z. Cho, Detection avoidance and priority-aware target tracking for UAV group reconnaissance operations, Journal of Intelligent and Robotic Systems, 92 (2018), 381-392.  doi: 10.1007/s10846-017-0745-9.
    [2] P. D. S. Paula, M. F. D. Castro, G. A. L. Paillard and W. W. F.Sarmento, A swarm solution for a cooperative and self-organized team of UAVs to search targets, 2016 8th Euro American Conference on Telematics and Information Systems, (2016). doi: 10.1109/EATIS.2016.7520118.
    [3] G. E. Collins, An automatic UAV search, intercept, and follow algorithm for persistent surveillance, Conference on Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR, (2010). doi: 10.1117/12.852236.
    [4] S. Long, J. H. Huang, W. Tao and J. Kang, Research on intelligent target recognition technology for integrated reconnaissance/strike UAV, Global Intelligent Industry Conference (GIIC), (2018). doi: 10.1117/12.2505024.
    [5] Q. Y. Liu, J. Wang and Y. H. Jun, Damage evaluation of microwave anti swarm attack based on scoring method, IEEE International Conference on Advances in Electrical Engineering and Computer Applications, (2020), 345–350. doi: 10.1109/AEECA49918.2020.9213545.
    [6] M. M. AzariK. C. Chen and S. Pollin, Ultra reliable UAV communication using altitude and cooperation diversity, IEEE Transactions on Communications, 66 (2018), 330-344.  doi: 10.1109/TCOMM.2017.2746105.
    [7] Oh donghanL. Jong and S. Jae, An airborne communication relay UAV model for locating the GPS-Denied crashed UAV, The Journal of Korean Institute of Communications and Information Sciences, 44 (2019), 1163-1172.  doi: 10.7840/kics.2019.44.6.1163.
    [8] Q. FanZ. Yang and T. Fang, Research status of coordinated formation flight control for multi-UAVs, Journal of Aeronautics and Astronautics, 30 (2009), 683-90.  doi: 10.1007/978-0-387-74660-9_12.
    [9] W. Yuan, Research on multi-UAV formation flying and conflict avoidance methods, National University of Defense Technology, 2017.
    [10] P.Zhan, D. W. Casbeer and A. L. Swindlehurst, A centralized control algorithm for target tracking with UAVs, 39th Asilomar Conference on Signals, Systems and Computers, 2005. doi: 10.1109/ACSSC.2005.1599940.
    [11] Q. N. Luo, Distributed UAV flocking control based on homing pigeon hierarchical strategies, Aerospace Science and Technology, 70 (2017), 257-264.  doi: 10.1016/j.ast.2017.08.010.
    [12] W. Sun, Distributed optimal scheduling in UAV swarm network, IEEE 18th Annual Consumer Communications and Networking Conference, 2021. doi: 10.1109/CCNC49032.2021.9369643.
    [13] X. Kun, J. Q. Lu, Y. Nie, L. Ma and G. H. Wang, A benchmark for Multi-UAV task assignment of an extended team orienteering problem, 2020 China Automation Conference (CAC2020) Proceedings, 2020. doi: 10.48550/arXiv.2009.00363.
    [14] S. James, R. Raheb and A. Hudak, UAV swarm path planning, 2020 Integrated Communications Navigation and Surveillance Conference, 2020. doi: 10.1109/ICNS50378.2020.9223005.
    [15] V. Roberge and M. Tarbouchi, Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning, IEEE Transactions on Industrial Informatics, 9 (2013), 132-141.  doi: 10.1109/TII.2012.2198665.
    [16] M. Zhao, T. Li and X. H. Su, Overview of key issues in coordinated mission planning for three-dimensional multi-UAV systems, Intelligent Computers and Applications, 6 2016, 31–5.
    [17] J. GuoH. Zheng and T. Jia, Overview of key technologies for cooperative combat in heterogeneous unmanned systems, Journal of Astronautics, 41 (2020), 686-696. 
    [18] Y. H. DuL. Wang and L. Xing, Intelligent planning technologies for unmanned aerospace system: A literature review, Journal of Systems Engineering, 35 (2020), 416-432. 
    [19] S. S. K. DebnathR. Omar and N. B. A. Latip, A review on energy efficient path planning algorithms for unmanned air vehicles, Lecture Notes in Electrical Engineering, 481 (2019), 523-532.  doi: 10.1007/978-981-13-2622-6_51.
    [20] S. Aggarwal and N. Kumar, Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges, Computer Communications, 149 (2020), 741-748.  doi: 10.1016/j.comcom.2019.10.014.
    [21] G. W. Jia and J. Wang, Research review of UAV swarm mission planning method, Systems Engineering and Electronics, 43 (2021), 99-111. 
    [22] Q. W. Pang and Y. J. Hu, A review of research on multi-UAV cooperative reconnaissance mission planning methods, Telecommunications Technology, 59 2019,741–748.
    [23] J. Zhang and J. H. Xing, Cooperative task assignment of multi-UAV system, Chinese Journal of Aeronautics, 33 2020, 2825–2827. doi: 10.1016/j.cja.2020.02.009.
    [24] D. Bailey, D. Frye and W. Woodbury, Computer aided mission planning system (CAMPS), Computer Aided Mission Planning System, 1982.
    [25] J. G. Tang, Development status and key technologies of MPS in US army, Fire Control and Command Control, 29 (2004), 4. 
    [26] Air form missing support system (AFMSS) [EB/OL]. (1999-01-09) [2012-11-07].
    [27] L. C. ShenJ. Chen and N. Wang, Summary of aircraft mission planning technology, Journal of Aeronautics, 35 (2014), 593-606. 
    [28] X. ChenX.D. Sun and J. J. Yan, Development of foreign mission planning systems, Journal of Command and Control, 4 (2018), 14-23. 
    [29] S. H. Huang and Z. C. Zhang, Research on the development of manned/unmanned distributed combat system in US army, Chinese Command and Control Society, 2018.
    [30] F. L. Li and H. Lu, Development of intelligent warFARE and unmanned systems technology, Unmanned Systems Technology, 1 (2018), 14-23. 
    [31] L. LiuX. Dong and J. H. Ge, Combat mode and key technology of unmanned system in rejection environment, Tactical Missile Technology, 4 (2020), 167-174. 
    [32] X. LiuX. Li and W. Chen, Research on multi-target allocation method based on NSGA-ó algorithm, Aerospace Defense, 4 (2021), 109-114. 
    [33] D. J. DengY. MaJ. Gong and J. Jie, Cooperative mission planning of multiple UAVs based on parallel GAPSO algorithm, Electronics Optics and Control, 23 (2017), 18-22. 
    [34] F. YeJ. Chen and Q. Sun, Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints, The Journal of Supercomputing, 77 (2020), 111-132.  doi: 10.1007/s11227-020-03264-4.
    [35] Y. Y. Zhao, D. Y. Zhou and H.Y. Piao, Cooperative multiple task assignment problem with target precedence constraints using a waitable path coordination and modified genetic algorithm, IEEE Access, 9 (2021), 39392-39410. doi: 10.1109/ACCESS.2021.3063263.
    [36] J. TurnerQ. Q. Meng and G. Schaefer, Distributed task rescheduling with time constraints for the optimization of total task assignments in a multirobot system, IEEE Transactions on Cybernetics, 48 (2018), 2583-2597.  doi: 10.1109/TCYB.2017.2743164.
    [37] Z. Y. JiaJ. Q. YuX. Xu and D. Yang, Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm, Aerospace Science and Technology, 76 (2018), 112-125.  doi: 10.1016/j.ast.2018.01.025.
    [38] N. PanH. Liu and K. Chen, Study on the multi-base and multi-target UAV cooperative mission planning algorithm, Modern Defense Technology, 49 (2002), 49-56. 
    [39] C. LiuW. Xie and P. Zhang, Multi-base multi-UAV flight path obstacle avoidance mission planning, Computer Engineering, 45 (2019), 275-280. 
    [40] K. Deng, Z. Lian and D. Zhou, Multi-UAV task assignment based on improved quantum particle swarm algorithm, Command Control and Simulation, 40 2019, 32–36.
    [41] H. S. Ryoo and N. V. Sahinidis, Global optimization of multiplicative programs, Journal of Global Optimization, 26 (2003), 387-418.  doi: 10.1023/A:1024700901538.
    [42] R. Bellman, Dynamic programming and Lagrange multipliers, Proceedings of the National Academy of Sciences of the United States of America, 42 (1956), 767-769.  doi: 10.1073/pnas.42.10.767.
    [43] J. C. LinG. W. Jia and Z. X. Hou, Research on task assignment of heterogeneous UAV formation in the anti-radar combat, Systems Engineering and Electronics, 40 (2018), 1986-1892.  doi: 10.1109/CCDC.2018.8407459.
    [44] J. R. Ding, Research on multi-UAV task assignment and path planning algorithm, Zhejiang University, 2016.
    [45] Y. MaZ. Jing and D. Zhou, A fast pruning optimization algorithm for task assignment problems, Journal of Northwestern Polytechnical University, 31 (2013), 40-3. 
    [46] C. Q. HuangK. X. Zhao and B. J. Han, Maneuvering decision-making method of UAV based on approximate dynamic programming, Journal of Electronics and Information Technology, 40 (2018), 2447-2452.  doi: 10.11999/JEIT180068.
    [47] M. A. Azam, S. Dey and H. D. Mittelmann, Decentralized UAV swarm control for multitarget tracking using approximate dynamic programming, 2021 IEEE World AI IoT Congress (AIIoT) (2021), 0457–0461. doi: 10.1109/AIIoT52608.2021.9454229.
    [48] R. G. S. Asthana, Evolutionary algorithms and neural networks, Soft Computing and Intelligent Systems, (2019). doi: 10.1016/B978-012646490-0/50009-3.
    [49] G. Bello-OrgazC. Ramirez-AtenciaJ. Fradera-Gil and D. Camacho, GAMPP: Genetic algorithm for UAV mission planning problems, 9th International Symposium on Intelligent Distributed Computing (IDC), 616 (2016), 167-176.  doi: 10.1007/978-3-319-25017-5_16.
    [50] Y. Qiu, H. Jiang, X. W. Dong and Z. Ren, Application of an adapted genetic algorithm on task allocation problem of multiple UAVs, 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), 2018. doi: 10.1109/GNCC42960.2018.9018985.
    [51] M. YaoH. Jiang and M. Zhao, Research on the method of UAV group cooperative combat task assignment, Journal of University of Electronic Science and Technology of China, 5 (2013), 727-7. 
    [52] S. W. Soliday, A genetic algorithm model for mission planning and dynamic resource allocation of airborne sensors, 1999 IRIS National Symposium on Sensor and Data Fusion, 2000.
    [53] C. Ramirez-Atencia, G. Bello-Orgaz and R-Moreno, A hybrid MOGA-CSP for multi-UAV mission planning, Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, 2015. doi: 10.1145/2739482.2768481.
    [54] W. N. Wu and N. G. Cui, A distributed and integrated method for cooperative mission planning of multiple heterogeneous UAVs, Aircraft Engineering and Aerospace Technology, 90 (2018), 1403-1412.  doi: 10.1108/AEAT-05-2017-0124.
    [55] Z. Y. JiaJ. Q. Yu and X. Xu, Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm, Aerospace Science and Technology, 76 (2018), 112-125.  doi: 10.1016/j.ast.2018.01.025.
    [56] C. Maurice, Particle Swarm Optimization, 2006. doi: 10.1002/9780470612163.ch16.
    [57] J. W. Wang and H. N. Li, Summary of particle swarm optimization algorithm, Modern Computer, 2009.
    [58] A. SalmanI. Ahmad I and S. Al-Madani, Particle swarm optimization for task assignment problem, Microprocessors and Microsystems, 26 (2002), 363-371.  doi: 10.1016/S0141-9331(02)00053-4.
    [59] G. Q. ZengY. BaiC. L. Liu and H. Y. Yue, Multitask assignment of swarming UAVs based on improved PSO, International Journal of Robotics & Automation, 36 (2021), 188-95.  doi: 10.2316/J.2021.206-0483.
    [60] R. Zhang, Y. X. Feng and Y. K. Yang, Hybrid particle swarm algorithm for multi-UAV cooperative task allocation, Chinese Journal of Aeronautics, (2021), 1–15.
    [61] W. Tian, L. Liu and Q. Wang, Cooperative multiple task assignment using cluster method and bidirectional particle swarm optimization, 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), (2021), 797–802. doi: 10.1109/IMCEC51613.2021.9482226.
    [62] C. Y. WeiZ. Ji and B. L. Cai, Particle swarm optimization for cooperative multi-robot task allocation: A multi-objective approach, IEEE Robotics and Automation Letters, 5 (2020), 2530-2537.  doi: 10.1109/LRA.2020.2972894.
    [63] G. PengY. FangS. ChenW. S. Peng and D. Yang, A hybrid multiobjective discrete particle swarm optimization algorithm for cooperative air combat DWTA, Journal of Optimization, 2017 (2017), 1-12.  doi: 10.1155/2017/8063767.
    [64] V. RobergeM. Tarbouchi and G. Labonte, Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning, IEEE Trans on Industrial Informatics, 9 (2013), 132-141.  doi: 10.1109/TII.2012.2198665.
    [65] U. Cekmez, M. Ozsiginan and O. K. Sahingoz, A UAV path planning with parellel ACO algorithm on CUDA platform, International Conference on Unmanned Aircraft Systems, 2014. doi: 10.1109/ICUAS.2014.6842273.
    [66] J. YanX. Liu and B. Liu, Cooperative task allocation of multi-UAVs with mixed DPSO-GT algorithm, Journal of National University of Defense Technology, 37 (2015), 165-171. 
    [67] Y. T. LuY. F. Ma and J. Y. Wang, Task assignment of UAV swarm based on wolf pack algorithm, Applied Science, 10 (2020), 1-17.  doi: 10.3390/app10238335.
    [68] N. LeiteF. Melicio and C. R. Agostinho, A fast simulated annealing algorithm for the examination timetabling problem-ScienceDirect, Expert Systems with Application, 122 (2019), 137-151.  doi: 10.1016/j.eswa.2018.12.048.
    [69] X. Yao and G. Chen, Simulated annealing algorithm and its application, Journal of Computer Research and Development, 7 (1990), 1-6. 
    [70] L. S. HuoJ. H. ZhuG. Wu and Z. M. Li, A novel simulated annealing based strategy for balanced UAV task assignment and path planning, Sensors, 20 (2020), 4769.  doi: 10.3390/s20174769.
    [71] D. A. Zorin and V. A. Kostenko, Simulated annealing algorithm in problems of multiprocessor scheduling, Automation and Remote Control, 75 (2014), 1790-1801.  doi: 10.1134/S0005117914100063.
    [72] Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat, Acta Aeronautica et Astronautica, 31 (2010), 626–631.
    [73] Y. ZhuC. Zhou and Y. Cui, An improved primary/backup scheduling algorithm based on simulated annealing algorithm, Computer Engineering and Science, 41 (2019), 1534-1540. 
    [74] B. HuangW. Xia and Y. Zhang, Task assignment algorithm based on particle swarm optimization and simulated annealing optimization in ad-hoc mobile cloud, Journal of Southeast University, 34 (2018), 430-438. 
    [75] Y. KimG. Oh and H. Choi, Market-Based task assignment for cooperative timing missions in dynamic environments, Journal of Intelligent and Robotic Systems: Theory and Application, 87 (2017), 97-123.  doi: 10.1007/s10846-017-0493-x.
    [76] X. W. Fu, P. Feng, X. Gao, Swarm UAVs task and resource dynamic assignment algorithm based on task sequence mechanism, IEEE Access, 99 (2019), 41090-41100. doi: 10.1109/ACCESS.2019.2907544.
    [77] Z. J. Guo and Y. Mi, The application of improved contract net agreement in air defense weapon target allocation, Modern Defense Technology, 45 (2017), 104–111+148.
    [78] Z. Y. ZhenL. D. WenB. L. WangZ. Hu and D. M. Zhang, Improved contract network protocol algorithm based cooperative target allocation of heterogeneous UAV swarm, Aerospace Science and Technology, 119 (2021), 107054.  doi: 10.1016/j.ast.2021.107054.
    [79] F. YanX. P. Zhu and Z. Zhou, Real-time task allocation for a heterogeneous multi-UAV simultaneous attack, Science in China: Information Science, 49 (2019), 555-569.  doi: 10.1360/N112018-00338.
    [80] Y. Zhang Y and W. Gang, Research on air and missile defense task allocation based on extended contract net protocol, 2nd International Conference on Materials Science, Resource and Environmental Engineering (MSREE), 2017. doi: 10.1063/1.5005268.
    [81] L. H. Tang, C. Zhu, W. M. Zhang and Z. Liu, Robust mission planning based on nested genetic algorithm, Fourth International Workshop on Advanced Computational Intelligence, 2011. doi: 10.1109/IWACI.2011.6159972.
    [82] L. Lin, Q. Sun and S. Wang, Research on PSO based multiple UAVs real-time task assignment, 25th Chinese Control and Decision Conference, (2013), 195–201. doi: 10.1109/CCDC.2013.6561171.
    [83] X. Chen and Y. Hu, Multi-UAV task allocation method based on PSO algorithm under uncertain environment, Ordnance Industry Automation, 32 (2013), 11-16. 
    [84] C. Ramirez-Atenci, G. Bello-Orgaz, M. D. R-Moreno and D. Camacho, MOGAMR: A multi-objective genetic algorithm for real-time mission replanning, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016. doi: 10.1109/SSCI.2016.7850235.
    [85] X. QiB. Li and Y. Fan, A review of mission planning for multiple UAV under multiple constraints, Journal of Intelligent Systems, 15 (2020), 204-217. 
    [86] T. Dang, F. Mascarich, S. Khattak, C. Papachristos and K. Alexisn, Graph-based path planning for autonomous robotic exploration in subterranean environments, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2020). doi: 10.1109/IROS40897.2019.8968151.
    [87] L. Shen LX. Wang and H. Zhu, UAV cluster and reconfiguration control based on mimic physics method, Scientia Sinica (Technologica), 47 (2017), 266-285. 
    [88] R. G. BaiX. Sun and Q. Chen, Multiple UAV cooperative trajectory planning based on Gauss pseudospectral method, Journal of Astronautics, 35 (2014), 1022-1029.  doi: 10.3873/j.issn.1000-1328.2014.09.006.
    [89] P. RenH. Wang and G. Zhou, Hypersonic vehicle reentry trajectory optimization based on adaptive pseudo-spectrum method, Journal of Beijing University of Aeronautics and Astronautics, 45 (2019), 2257-2265. 
    [90] A. Kosari and M. M. Teshnizi, Optimal trajectory design for conflict resolution and collision avoidance of flying robots using Radau-Pseudo spectral approach, 6th RSI International Conference on Robotics and Mechatronics, 2018. doi: 10.1109/ICRoM.2018.8657506.
    [91] G. Liu, B. Li and Y. Ji, A modified HP-adaptive pseudospectral method for multi-UAV formation reconfiguration, ISA transactions (2022). doi: 10.1016/j.isatra.2022.01.015.
    [92] B. LiJ. ZhangL. DaiK. L. Teo and S. Wang, A hybrid offline optimization method for reconfiguration of multi-UAV formations, IEEE Transactions on Aerospace and Electronic Systems, 57 (2020), 506-520.  doi: 10.1109/TAES.2020.3024427.
    [93] B. Li, Q. Li, Y. Zeng, Y. Rong and R. Zhang, 3D trajectory optimization for energy-efficient UAV communication: A control design perspective, IEEE Transactions on Wireless Communications, (2021). doi: 10.1109/TWC.2021.3131384.
    [94] J. GuoB. Li and Y. Ji, A control parametrization based path planning method for the quad-rotor uavs, Journal of Industrial and Management Optimization, 18 (2022), 1079-1100.  doi: 10.3934/jimo.2021009.
    [95] B. Li, Z. Yin, Y. Ouyang, Y. Zhang, X. Zhong and S. Tang, Online trajectory replanning for sudden environmental changes during automated parking: A parallel stitching method, IEEE Transactions on Intelligent Vehicles, 2022. doi: 10.1109/TIV.2022.3156429.
    [96] B. Li, Y. Ouyang, L. Li and Y. M. Zhang, Autonomous driving on curvy roads without reliance on frenet frame: A cartesian-based trajectory planning method, IEEE Transactions on Intelligent Transportation Systems, 2022. doi: 10.1109/TITS.2022.3145389.
    [97] H. Ai and B. Zhang, A graphic approach to path planning problem based on topological method, Robot, 12 (1990), 20-24. 
    [98] M. Noto and H. Sato, A method or the shortest pathsearch by extended Dijkstra algorithm, IEEE International Conference on Systems, Man and Cybernetics, 2000. doi: 10.1109/ICSMC.2000.886462.
    [99] Y. P. Lu, X. Xu and Y. Z. Luo, Path planning for rolling locomotion of polyhedral tensegrity robots based on Dijkstra algorithm, 60th Anniversary Symposium of the International Association for Shell and Spatial Structures, (2019), 2965–2972. doi: 10.20898/j.iass.2019.202.037.
    [100] T. Bi, P. Ye and Y. H. Xu, Route planning of unmanned aerial vehicle based on sparse A* algorithm, 2019 International Conference on Informatics, Control and Robotics, 2019. doi: 10.12783/dtetr/icicr2019/30543.
    [101] C. Xia and X. Chen, The UAV dynamic path planning algorithm research based on Voronoi diagram, 26th Chinese Control and Decision Conference, (2014). doi: 10.1109/CCDC.2014.6852323.
    [102] V. Boor, M. H. Overmars and A. F. Stappen, The Gaussian sampling strategy for probabilistic roadmap planners, 1999 IEEE International Conference on Robotics and Automation, (1999), 1018–1023. doi: 10.1109/ROBOT.1999.772447.
    [103] J. Zhong and J. Su, Robot path planning in narrow passages based on probabilistic roadmaps, Control and Decision, 25 (2010), 1831-1836.  doi: 10.2316/Journal.206.2013.3.206-3598.
    [104] R. A. Conn and M. Kam, Robot motion planning on N-dimensional star worlds among moving obstacles, IEEE Transactions on Robotics and Automation, 14 (1998), 320-325.  doi: 10.1109/70.681250.
    [105] M. Zhao M and J. Zhou, Algorithm of 4D real-time path planning based on A* algorithm, Fire Control and Command Control, 33 (2008), 98-101. 
    [106] D. MandloiR. Arya and A. K. Verma, Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3d environment, International Journal of Systems Assurance Engineering and Management, 12 (2021), 990-1000.  doi: 10.1007/s13198-021-01186-9.
    [107] C. J. Zhang and X. Y. Meng, Spare A* search approach for UAV route planning, IEEE International Conference on Unmanned Systems, (2017).
    [108] J. Wu, Y. Yu and J. Zhou, Variable step A* algorithm in UAV route planning, Electronics Optics and Control, 18, 2011.
    [109] X. Song and S. Hu, Multi-UAV path planning based on dubins path A* algorithm, Electro-Optics and Control, 25 (2018), 25-29. 
    [110] R. W. BeardT. W. Mclain and M. Goodrich, Coordinated target assignment and intercept for unmanned air vehicles, IEEE Transactions on Robotics and Automation, 18 (2003), 911-922.  doi: 10.1109/ROBOT.2002.1013620.
    [111] M. Mozaffari, W. Saad, M. Bennis and M. Debbah, Wireless communication using unmanned aerial vehicles (UAVs): Optimal transport theory for hover time optimization, IEEE Transactions on Wireless Communications, (2017), 8052-8066. doi: 10.1109/TWC.2017.2756644.
    [112] T. P. Huang, D. Q. Huang and N. Qin, Path planning and control of a quadrotor UAV based on an improved APF using parallel search, International Journal of Aerospace Engineering, (2021). doi: 10.1155/2021/5524841.
    [113] Y.Q. Zhao, K. Liu, G. H. Lu and S. W. Yuan, Path planning of UAV delivery based on improved APF-RRT* algorithm, Journal of Physics: Conference Series, 1624 (2021), 042004, 8 pp. doi: 10.1088/1742-6596/1624/4/042004.
    [114] Y. B. ChenG. C. Luo and Y. S. Mei, UAV path planning using artificial potential field method updated by optimal control theory, International Journal of Systems Science, 47 (2016), 1407-1420.  doi: 10.1080/00207721.2014.929191.
    [115] L. Yu, F. Shi and H. Wang, Analysis of 30 Cases of MATLAB Intelligent Algorithms, 2$^{nd}$ edition, Beijing University of Aeronautics and Astronautics, 1994.
    [116] U. Cekmez, M. Ozsiginan and O. K. Sahingoz, A UAV path planning with parallel ACO algorithm on CUDA platform, International Conference on Unmanned Aircraft Systems, (2014). doi: 10.1109/ICUAS.2014.6842273.
    [117] U. Cekmez, M. Ozsiginan and O. K. Sahingoz, Multi colony ant optimization for UAV path planning with obstacle avoidance, International Conference on Unmanned Aircraft Systems (ICUAS), 2016. doi: 10.1109/ICUAS.2016.7502621.
    [118] Perez-CarabazaSara and Besada-Portas, Ant colony optimization for multi-UAV minimum time search in uncertain domains, Applied Soft Computing, 62 (2018), 789-806.  doi: 10.1016/j.asoc.2017.09.009.
    [119] S. Konatowski and P. Pawlowski, Application of the ACO algorithm for UAV path planning, Przeglad Elektrotechniczny, 95 (2019), 115-119.  doi: 10.15199/48.2019.07.24.
    [120] Y. R. Guan, M. S. Gao and Y. F. Bai, Double-ant colony based UAV path planning algorithm, 11th International Conference on Machine Learning and Computing (ICMLC), 2019. doi: 10.1145/3318299.3318376.
    [121] X. GaoJ. Li and Z. Zhao, Research on route planning method based on simulated annealing algorithm, Microelectronics and Computer, 17 (2000), 10-14. 
    [122] B. Basbous, 2D UAV path planning with radar threatening areas using simulated annealing algorithm for event detection, 2018 International Conference on Artificial Intelligence and Data Processing, (2018). doi: 10.1109/IDAP.2018.8620881.
    [123] L. Fan, Research on genetic simulated annealing algorithm for path planning, Chongqing University, (2010).
    [124] X. YueC. Zhang and W. Zhang, Path planning based on A-Star and improved simulated annealing algorithm, Control Engineering, 8 (2020), 1365-1371. 
    [125] T. H. Xu, N. Wang and H. Lin, UAV autonomous reconnaissance route planning based on deep reinforcement learning, 2019 IEEE International Conference on Unmanned Systems, (2019). doi: 10.1109/ICUS48101.2019.8995935.
    [126] A. A. Maw, M. Tyan and J. W. Lee, iADA*-RL: Anytime graph-based path planning with deep reinforcement learning for an autonomous UAV, Applied Sciences, 11 (2021). doi: 10.3390/app11093948.
    [127] B. H. WangT. Y. Wu and D. Huang, Large-scale UAVs confrontation based on multi-agent reinforcement learning, Journal of System Simulation, 33 (2021), 1753-1769.  doi: 10.1007/978-3-030-60990-0_12.
    [128] R. L. XieZ. Meng and L. Wang, Unmanned aerial vehicle path planning algorithm based on deep reinforcement learning in large-scale and dynamic environments, IEEE Access, 9 (2021), 24884-24900.  doi: 10.1109/ACCESS.2021.3057485.
    [129] T. GuoN. JiangB. Y. Li and X. Zhu, UAV navigation in high dynamic environments: A deep reinforcement learning approach, Chinese Journal of Aeronautics, 34 (2021), 479-489.  doi: 10.1016/j.cja.2020.05.011.
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