混合种群RRT无人机航迹规划方法

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

Abstract

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

CLC Number:

V279

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

Figures/Tables 8

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References 19

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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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Mixed population RRT algorithm for UAV path planning

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