@inproceedings{217f608a07464b9a9b7374f50082074f,
title = "Efficient Path Planning for UAV Swarm Under Dense Obstacle Environment",
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.",
keywords = "Formation control, Improved artificial potential field, Improved particle swarm optimization, Path planning",
author = "Menglei Li and Chunhui Zhao and Jinwen Hu and Zhao Xu and Chubing Guo and Zengfa Dou",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9\_11",
language = "英语",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "101--110",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
}