基于改进Informed-RRT*算法的无人机三维路径规划

doi:10.12305/j.issn.1001-506X.2026.02.26

Abstract

Abstract:

To meet the requirements of three-dimensional path planning of unmanned aerial vehicle （UAV）, and solve the problems of long initial feasible path and low efficiency of the informed rapidly-exploring random tree （Informed-RRT^*） algorithm, the dynamic artificial potential field is used to guide the growth of the tree to reduce the length of the initial path. The sampling area is limited to the stratified ellipsoid, and the sampling probability is adjusted according to the density of obstacles. Feedforward neural networks and genetic algorithms are used to optimize the reconnection area radius to reduce the running time. Simulation results show that in both sparse and dense obstacle environments, the path quality obtained by the improved algorithm is better than that of the Informed RRT^* algorithm and the A^* algorithm. The practicability of the improved algorithm in UAV three-dimensional path planning is verified.

Key words: path planning, unmanned aerial vehicle （UAV）, informed rapidly-exploring random tree （Informed-RRT^*）, dynamic artificial potential field

CLC Number:

TP 242

Sen ZHANG, Yan PANG, Fuliang ZHOU. Three-dimensional path planning for UAV based on improved Informed-RRT^* algorithm[J]. Systems Engineering and Electronics, 2026, 48(2): 660-668.

Figures/Tables 16

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算法	初始长度/m	迭代次数	初始时间/s	路径长度/m	最终时间/s
RRT^*	1 930	622	0.341	1640	5.402
Informed-RRT^*	1 910	617	0.314	1510	6.211
低分辨率A^*	—	—	—	1707	4.938
高分辨率A^*	—	—	—	1238	18.681
本文算法	1620	340	0.158	1220	5.585

算法	初始长度/m	迭代次数	初始时间/s	路径长度/m	最终时间/s
RRT^*	2310	866	0.474	1 880	7.262
Informed-RRT^*	2340	842	0.452	1720	7.835
低分辨率A^*	—	—	—	1 832	6.038
高分辨率A^*	—	—	—	1322	19.686
本文算法	1 900	424	0.227	1310	7.392

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Three-dimensional path planning for UAV based on improved Informed-RRT^* algorithm

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Three-dimensional path planning for UAV based on improved Informed-RRT^* algorithm