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

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (2): 660-668.doi: 10.12305/j.issn.1001-506X.2026.02.26

基于改进Informed-RRT^*算法的无人机三维路径规划

张森¹^,², 庞岩¹^,²^,*, 周福亮³

1. 大连理工大学力学与航空航天学院工业装备结构分析优化与CAE软件全国重点实验室，辽宁大连 116024
2. 大连理工大学辽宁省空天飞行器前沿技术重点实验室，辽宁大连 116024
3. 南京航天国器智能装备有限公司，江苏南京 210031

收稿日期:2025-01-17 修回日期:2025-07-03 出版日期:2025-11-06 发布日期:2025-11-06
通讯作者: 庞岩
作者简介:张　森（2000—），男，硕士研究生，主要研究方向为无人机路径规划、无人机集群控制
周福亮（1981—），男，高级工程师，博士，主要研究方向为飞行器总体设计
基金资助:
国家自然科学基金（61973052）资助课题

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

Sen ZHANG¹^,², Yan PANG¹^,²^,*, Fuliang ZHOU³

1. State Key Laboratory of Structural Analysis，Optimization and CAE Software for Industrial Equipment，School of Mechanics and Aerospace Engineering，Dalian University of Technology，Dalian 116024，China
2. Liaoning Provincial Key Laboratory of Frontier Technology of Aerospace Vehicles，Dalian 116024，China
3. Nanjing Aerospace National IntelligentEquipment Co.，LTD，Nanjing 210031，China

Received:2025-01-17 Revised:2025-07-03 Online:2025-11-06 Published:2025-11-06
Contact: Yan PANG

摘要/Abstract

摘要：

为满足无人机（unmanned aerial vehicle，UAV）的三维路径规划需求，针对基于启发信息的快速扩展随机树（informed rapidly-exploring random tree，Informed-RRT^*）算法初始可行路径较长、优化效率低的问题，本文采用动态人工势场来引导树的生长，降低初始路径的长度；将采样区域限制在分层椭球中，根据障碍物疏密调整采样概率；使用前馈神经网络和遗传算法优化重连区域半径，以降低运行时间。仿真结果显示，在障碍物稀疏和密集环境中，改进算法得到的路径质量相较于Informed-RRT^*算法以及A^*算法更优，验证了本文算法在无人机三维路径规划中的实用性。

关键词: 路径规划, 无人机, Informed-RRT^*, 动态人工势场

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

中图分类号:

TP 242

张森, 庞岩, 周福亮. 基于改进Informed-RRT^*算法的无人机三维路径规划[J]. 系统工程与电子技术, 2026, 48(2): 660-668.

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.

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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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基于改进Informed-RRT^*算法的无人机三维路径规划

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

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基于改进Informed-RRT*算法的无人机三维路径规划

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

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基于改进Informed-RRT^*算法的无人机三维路径规划

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