@inproceedings{d4ba77d0dffc4a9b87b75e1c40eb0dd2,
title = "Hierarchical Trajectory Planning for Multi-UAVs via Sparse A* Search and Linear Programming",
abstract = "This paper proposes a hierarchical trajectory planning method to generate dynamically-feasible cooperative trajectories rapidly for multi-UAVs. In the first stage, the efficient sparse A* search algorithm (SAS) is adopted to plan geometric path as the initial guess for trajectory iterations. Secondly, based on the geometric path, the nonlinear trajectory optimization problem is cast into linear programming problems (LP), and LP has low computational complexity and appealing convergence performance. Simulation experiment results manifest that the hierarchical trajectory planning has the ability to eliminate the sensitivity to initial guess and improve the convergence. The comparative results show that hierarchical trajectory planning method outperforms distributed sequential convex programming in terms of computation efficiency.",
keywords = "Geometric path planning, Hierarchical trajectory planning, Linear programming, Multiple UAVs",
author = "Guangtong Xu and Yangjie Wang and Xuan Zhang and Jingliang Sun and Teng Long",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2023",
doi = "10.1007/978-981-19-6613-2_509",
language = "English",
isbn = "9789811966125",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "5271--5277",
editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",
address = "Germany",
}