Multiple UAV cooperative path planning based on PSO-HJ method

被引：0
|
作者：
Shan W. [1 ]
Cui N. [2 ]
Huang B. [1 ]
Wang X. [2 ]
Bai Y. [2 ]
机构：
[1] China Academy of Launch Vehicle Technology, Beijing
[2] Aeronautical Academy, Harbin Institute of Technology, Harbin
来源：
Wang, Xiaogang (wangxiaogang@hit.edu.cn) | 1600年 / Editorial Department of Journal of Chinese Inertial Technology卷 / 28期
关键词：
Cooperative path planning; Cooperative variable; Multiple UAV; Particle swarm optimization;
D O I：
10.13695/j.cnki.12-1222/o3.2020.01.019
中图分类号：
学科分类号：
摘要：
In order to solve the problems of large computational complexity and difficulty in convergence for multi-UAV cooperative path planning, a cooperative path planning algorithm based on a hybrid method combining Particle Swarm Optimization and Hook-Jeeves search algorithm (PSO-HJ Method) is proposed. Firstly, the single UAV path planning model is established. Then the particle diversity and the convergence of path planning algorithm are improved by introducing the Hooke-Jeeves search algorithm for particles with low fitness evaluation function values. The constraint violation function is introduced for particles that do not satisfy the constraint. The new particle evaluation mechanism is proposed to promote the optimal solution of the particle search at the constraint boundary, which accelerates the computational efficiency of the path planning algorithm. The collaborative algorithm is designed to solve the cooperative variable. PSO-HJ algorithm is used to calculate the path parameters of single UAV respectively. Multi-UAV path planning layer coordinated arrival time to calculate simultaneous arrival time. Simulation results show that the accuracy of the PSO-HJ algorithm is 20.85% higher than the accuracy of the Quantum Particle Swarm Optimization algorithm (QPSO), and 58.14% higher than the accuracy of the PSO algorithm. It is more suitable for solving practical complex multi-UAV collaborative planning problems. © 2020, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
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页码：122 / 128
页数：6
相关论文
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