Abstract
Cooperative mission for Multi UAVs has become an important trend. Cooperative path planning is one of the most killing part for the issue. The algorithms can be divided into the following categories, including optimal algorithm, graph-theory-based planning method, heuristic-information-based planning algorithm, swarm intelligence algorithm and neural network algorithm and so on. This paper systematically presents the research status and trend of the application of path planning in the field of multi-UAV cooperative mission and selects a variety of classic and up-to-date algorithms and their variants to promote future research.
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References
Okumura, K., Tamura, Y., Défago, X.: winPIBT: extended prioritized algorithm for iterative multi-agent path finding (2019)
Yuan, L., Xia, J., Chen, Z.: Review of cooperative path planning for multiple UAVs. Flight Mech. 05, 1–5 (2009)
Zou, Z.: Research on trajectory planning model and algorithm of anti-ship missile based on dynamic programming. Wuhan University of Technology (2019)
Zhao, X.: Optimal route planning based on MILP. J. Chin. Acad. Electron. Sci. 10(002), 150–155 (2015)
Papen, A., Vandenhoeck, R., Bolting, J., Defay, F.: Collision-free rendezvous maneuvers for formations of unmanned aerial vehicles. IFAC Papers On Line 50(1), 282–289 (2017)
Kaluđer, H., Brezak, M., Petrović, I.: A visibility graph-based method for path planning in dynamic environments. In: 2011 Proceedings of the 34th International Convention MIPRO IEEE, Croatia, no. 05, pp. 23–27 (2011)
Niu, H., Savvaris, A., Tsourdos, A., Ji, Z.: Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. J. Navig. 72(4), 1–25 (2019)
Özcan, M., Yaman, U.: A continuous path planning approach on Voronoi diagrams for robotics and manufacturing applications. Procedia Manuf. 38, 1–8 (2019)
Albanis, G., Zioulis, N., Dimou, A., Zarpalas, D., Daras, P.: DronePose: photorealistic UAV-assistant dataset synthesis for 3D pose estimation via a smooth silhouette loss. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 663–681. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66096-3_44
Khatib, O.: Real time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–99 (1986)
Deng, Y., Jiang, X.: Trajectory planning algorithm of four rotor UAV based on improved artificial potential field method. Sens. Microsyst. 40(07), 130–133 (2021)
Li, Y.: Formation tracking and obstacle avoidance based on improved PSO and artificial potential field method. Int. Core J. Eng. 7(7), 117–124 (2021)
Imrane, M.L., Melingui, A., Mvogo Ahanda, J.J.B., Biya Motto, F., Merzouki, R.: Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots. Proc. Inst. Mech. Eng. 235(7), 1179–1192 (2021)
Maini, P., Sujit, P.B.: Path planning for a UAV with kinematic constraints in the presence of polygonal obstacles. In: International Conference on Unmanned Aircraft Systems, Arlington, VA, USA, pp. 62–67. IEEE (2016)
Ningyi, C., Zhiqian, L., Yuqi, L.: A route planning algorithm based on Dijkstra for intelligent aircraft under multiple constraints. J. Northw. Univ. Technol. 38(06), 1284–1290 (2020)
Szczerba, R.J.: Using framed-octrees to find conditional shortest paths in an unknown 3-D environment (2010)
Liang, L.U., Wang, J., Zong, C., et al.: Simulation of 3D path planning approach for quad-rotor helicopter based on A~* algorithm. J. Hefei Univ. Technol. (Nat. Sci.) 40, 304–309 (2017)
Feng, Q., Gao, J., Deng, X.: Path planner for UAVs navigation based on A* algorithm incorporating intersection. In: 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing (2016)
Xiao, Z., Yuan, D., Qu, Y.: Multi UAV cooperative path planning based on A* fixed length search algorithm. Flight Mech. 30(01), 92–96 (2012)
Ning, B., Xiangmin, L., Jin, D., Jiayu, T.: Hierarchical cooperative path planning of multiple UAVs based on variable step sparse A * search and MPC. Command Control Simul. 040(002), 65–71 (2018)
Ma, X., Jiao, Z., Wang, Z., et al.: 3-D decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles. IEEE Trans. Control Syst. Technol. 26(3), 939–953 (2017)
Xie, C., Li, K., Xu, J., et al.: An improved multi objective particle swarm optimization algorithm MOPSO - II. J. Wuhan Univ. 60(02), 144–150 (2014)
Ye, A.: Study of the vehicle routing problem with time windows based on improved particle swarm optimization algorithm. In: 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, pp. 4053–4057 (2011)
Liu, X.: Research on multi-UAV cooperative path planning based on improved ant colony algorithm. Zhengzhou University of Aeronautics (2020)
Ali, Z.A., Zhangang, H., Hang, W.B.: Cooperative path planning of multiple UAVs by using max-min ant colony optimization along with Cauchy mutant operator. Fluct. Noise Lett. 20(01), 2150002 (2021)
Dai, X., et al.: Mobile robot path planning based on ant colony algorithm with A* heuristic method. Front. Neurorobot. 13, 15 (2019)
Jin, L., Kepu, S.: Application of neural network in path planning of mobile robot. J. Syst. Simul. 22(s1), 269–272 (2010)
Chen, X., Ai, Y.: Three-dimensional path planning of UAV based on improved neural network. Electro Opt. Control 9, 7–11 (2018)
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Sun, W., Hao, M. (2022). A Survey of Cooperative Path Planning for Multiple UAVs. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_20
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DOI: https://doi.org/10.1007/978-981-16-9492-9_20
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