混合种群RRT无人机航迹规划方法

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

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (1): 101-107.doi: 10.3969/j.issn.1001-506X.2020.01.14

混合种群RRT无人机航迹规划方法

高升(), 艾剑良(), 王之豪()

复旦大学航空航天系, 上海 200082

收稿日期:2019-04-23 出版日期:2020-01-01 发布日期:2019-12-23
作者简介:高升(1994-),男,博士研究生,主要研究方向为无人机任务规划、智能控制。E-mail:17210290012@fudan.edu.cn|艾剑良(1965-),男,教授,博士,主要研究方向为飞行控制、飞行器总体设计。E-mail:aijl@fudan.edu.cn|王之豪(1996-),男,博士研究生,主要研究方向为无人机空战决策、无人机任务规划。E-mail:17110290005@fudan.edu.cn
基金资助:
航空电子系统综合技术重点实验室基金(03010417)

Mixed population RRT algorithm for UAV path planning

Sheng GAO(), Jianliang AI(), Zhihao WANG()

Department of Aeronautics and Astronautics, Fudan University, Shanghai 200082, China

Received:2019-04-23 Online:2020-01-01 Published:2019-12-23
Supported by:
航空电子系统综合技术重点实验室基金(03010417)

摘要/Abstract

摘要：

快速扩展随机树（rapidly-exploring random tree，RRT）无人机航迹规划方法能够快速获得满足约束要求的可行航迹，但是无法获得接近最短航迹的较优航迹。针对航迹的最优性问题，提出了混合种群RRT无人机航迹规划方法。在基于环境势场的RRT算法的基础上，设计了一种种群优化方法，通过引入自优化种群和协同优化种群改善航迹段，使算法同时具有局部和全局寻优能力。在得到航迹节点的基础上，采用B样条曲线的平滑方法生成曲率连续的可跟踪航迹。仿真结果表明，所提算法能够综合考虑无人机航程代价和雷达威胁代价，快速地收敛得到接近最优且满足无人机动力学约束的可行航迹，在不同环境下也能有满意的收敛效率。

关键词: 快速扩展随机树, 无人机, 航迹规划, 混合种群

Abstract:

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.

Key words: rapidly-exploring random tree (RRT), unmanned aerial vehicle (UAV), path planning, mixed population

中图分类号:

V279

高升, 艾剑良, 王之豪. 混合种群RRT无人机航迹规划方法[J]. 系统工程与电子技术, 2020, 42(1): 101-107.

Sheng GAO, Jianliang AI, Zhihao WANG. Mixed population RRT algorithm for UAV path planning[J]. Systems Engineering and Electronics, 2020, 42(1): 101-107.

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权重	航程代价/km	雷达威胁代价
w₁=1, w₂=0	129.73	118.07
w₁=0.8, w₂=0.2	132.51	95.32
w₁=0.5, w₂=0.5	134.08	61.28
w₁=0.2, w₂=0.8	135.87	5.04

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混合种群RRT无人机航迹规划方法

Mixed population RRT algorithm for UAV path planning

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