Mixed population RRT algorithm for UAV path planning

被引：8

作者：

Gao, Sheng ^{[1
]}

Ai, Jianliang ^{[1
]}

Wang, Zhihao ^{[1
]}

机构：

[1] Department of Aeronautics and Astronautics, Fudan University, Shanghai,200082, China

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 01期

关键词：

Trees (mathematics) - Antennas - Unmanned aerial vehicles (UAV);

D O I：

10.3969/j.issn.1001-506X.2020.01.14

中图分类号：

学科分类号：

摘要：

The unmanned aerial vehicle (UAV) path planning algorithm based on the rapidly-exploring random tree (RRT) can only quickly get a feasible path, but cannot obtain a near shortest path. To solve the path optimization problem, a mixed population RRT algorithm is proposed on the basis of the environmental potential field based RRT. The algorithm optimizes the path section to shorten the initial path with self-optimizing colony and synergy-optimizing colony. Meanwhile, the self-optimizing colony will search globally on the mission space, which makes the algorithm obtain a global optimal path. Afterward the B-spline is used to smooth the path node for a trackable path that meets the UAV dynamic constraints. Simulation results demonstrate that the proposed method can get a near optimal path with the fast convergence rate considering radar thread and the length of path, and the convergence efficiency is satisfactory in different mission environments. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：101 / 107