低空复杂空间下自适应交替双目标偏差RRT<sup>*</sup>无人机三维路径规划

郑振岗; 李新凯; 孟月; 张宏立

doi:10.12382/bgxb.2025.0310

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低空复杂空间下自适应交替双目标偏差RRT^*无人机三维路径规划

更新时间：2026-04-24

- 低空复杂空间下自适应交替双目标偏差RRT^*无人机三维路径规划
- Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT^* in Complex Low-altitude Airspace
- 兵工学报 2026年47卷第1期页码：250310
- 作者机构：
  
  新疆大学智能科学与技术学院，新疆乌鲁木齐 830017
- 作者简介：
  
  *通信作者邮箱：lxk@xju.edu.cn
- 基金信息：
  
  国家自然科学基金项目(62263030);新疆维吾尔自治区自然科学基金项目(2023D01C187);新疆维吾尔自治区“天池英才”引进计划项目(5105240151d);“天山英才”科技创新领军人才项目(2024TSYCLJ0008)
- DOI：10.12382/bgxb.2025.0310
  中图分类号： V279;TP242.6
- 收稿：2025-04-25，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
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郑振岗, 李新凯, 孟月, 等. 低空复杂空间下自适应交替双目标偏差RRT^*无人机三维路径规划[J]. 兵工学报, 2026,47(1):250310.

ZHENG Zhengang, LI Xinkai, MENG Yue, et al. Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT^* in Complex Low-altitude Airspace[J]. Acta Armamentarii, 2026, 47(1): 250310.
郑振岗, 李新凯, 孟月, 等. 低空复杂空间下自适应交替双目标偏差RRT^*无人机三维路径规划[J]. 兵工学报, 2026,47(1):250310. DOI： 10.12382/bgxb.2025.0310.

ZHENG Zhengang, LI Xinkai, MENG Yue, et al. Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT^* in Complex Low-altitude Airspace[J]. Acta Armamentarii, 2026, 47(1): 250310. DOI： 10.12382/bgxb.2025.0310.

摘要

针对低空经济背景下无人机在复杂三维建筑环境中的路径规划需求，提出改进的双向快速搜索树自适应交替双目标偏差搜索（Sampling-Tree Based bidirectional Rapidly-exploring Random Tree

ST-BA-RRT

）算法。该算法在采样阶段采用三维环境下的椭球采样，并配合双目标偏差策略抑制随机树向障碍区扩展，定向引导其向目标生长；扩展阶段运用自适应交替探索与改进人工势场辅助策略，增强算法环境适应性与局部避障能力。碰撞检测环节通过设计新型代价函数减少障碍物检查频次，优化规划时间；连通性处理利用双向随机采样提升规划效率；最后借助

样条平滑路径。实验结果表明，相较于现有算法，ST-BA-RRT

算法生成的路径更短、更平滑，路径规划时间平均减少35%，在路径质量与环境适应性方面优势显著，能够高效生成优化飞行轨迹，满足复杂三维建筑环境下无人机路径规划需求。

Abstract

The path planning requirements of unmanned aerial vehicles (UAVs) in c

omplex three-dimensional building environments under the background of the low-altitude economy are studied. An improved sampling-Tree based bidirectional rapidly-exploring random tree algorithm (ST-BA-RRT

) is proposed. During the sampling stage

the proposed algorithm uses the ellipsoidal sampling in a three-dimensional environment

suppresses the expansion of the random tree into the obstacle area in conjunction with the dual-target bias strategy

and guides it to grow towards the target directionally. During the expansion stage

the adaptive alternating exploration and improved artificial potential field auxiliary strategies are applied to enhance the algorithm's environmental adaptability and local obstacle avoidance ability. During the collision detection stage

a new cost function is designed to reduce the frequency of obstacle inspections and optimize the planning time. For the connectivity processing

the bidirectional random sampling is used to improve the planning efficiency. Finally

the

-spline function is used to smooth the path. The experimental results show that the path generated by the ST-BA-RRT

algorithm is shorter and smoother than those generated by the existing algorithms

and the average path planning time is reduced by 35%. The proposed algorithm has significant advantages in terms of path quality and environmental adaptability

efficiently generate the optimized flight trajectories

and meets the path planning requirements of UAVs in complex three-dimensional building environments.

关键词

Keywords

references

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低空复杂空间下自适应交替双目标偏差RRT*无人机三维路径规划

Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT* in Complex Low-altitude Airspace

DOI：10.12382/bgxb.2025.0310

摘要

Abstract

关键词

Keywords

references

低空复杂空间下自适应交替双目标偏差RRT^*无人机三维路径规划

Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT^* in Complex Low-altitude Airspace