@inproceedings{b611270e1c7941a5b0ebda3d05e35fd7,
title = "Local Trajectory Planning of Unmanned Aerial Vehicle Formation Based on Time Cooperative Strategy",
abstract = "In this paper, we address the problem of rapid generation of trajectories with time coordination in unmanned aerial vehicle (UAV) formations in complex urban environments. Aiming at the phenomenon that large-scale UAV system trajectory planning is computationally burdensome and fails to generate high time-optimal cooperative trajectories, we propose a time cooperative trajectory planning algorithm based on distributed model predictive control (DMPC). Firstly, a multi-UAV dynamics model based on predictive horizon is established, and a rolling iterative optimization strategy is proposed to ensure the real-time and rapidity of trajectory planning. Furthermore, the time cooperative strategy is proposed so that the UAV formation can arrive at the target position and complete the formation flight mission at the same time to ensure the feasibility of macroscopic task allocation. The simulation results show that the UAV can reach the target location smoothly in the complex environment with random assignment of obstacles, and there is no collision during the flight process, which verifies the reliability of the algorithm.",
keywords = "DMPC, Time cooperative, Trajectory planning, UAV formation",
author = "Keqing Guo and Hui Wang and Xin Dai and Jing Liu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2024 ; Conference date: 08-07-2024 Through 10-07-2024",
year = "2024",
doi = "10.1109/ICARM62033.2024.10715928",
language = "English",
series = "ICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1105--1111",
booktitle = "ICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics",
address = "United States",
}