Abstract
This paper deals with the path planning problem of unmanned aerial vehicle (UAV) swarm in the dense-obstacle environment. A novel hierarchical path planning approach with two-level structure is proposed to obtain collision-free and smooth paths for UAV swarm. In the first level, an improved particle swarm optimization (PSO) method is proposed to generate a collision-free global optimal path to determine the overall movement orientation of UAV swarm. In the second level, the improved artificial potential field (APF) combined with consensus theory is used for local path planning of each UAV in the swarm with the turning points extracted from the global optimal path obtained previously as a series of destinations under the leader-follower formation control framework. Numerical simulations are implemented to prove the validity of our proposed algorithm.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant 61803309, the Fundamental Research Funds for the Central Universities under Grant 3102019ZDHKY02, the Key Research and Development Project of Shaanxi Province under Grant 2020ZDLGY06-02, the Natural Science Foundation of Shaanxi Province under Grant 2019JM-254, the China Postdoctoral Science Foundation under Grant 2018M633574, the Aeronautical Science Foundation of China under Grant 2019ZA053008, the Open Foundation of CETC Key Laboratory of Data Link Technology under Grant CLDL-20202101.
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Li, M., Zhao, C., Hu, J., Xu, Z., Guo, C., Dou, Z. (2022). Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment. 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_11
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DOI: https://doi.org/10.1007/978-981-16-9492-9_11
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