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

ZHENG Zhengang; LI Xinkai; MENG Yue; ZHANG Hongli

doi:10.12382/bgxb.2025.0310

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

更新时间：2026-04-24

- Three-dimensional Path Planning of UAVs Based on Adaptive Alternating Dual-target Deviation RRT^* in Complex Low-altitude Airspace
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250310(2026)
- 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：
- 基金信息：
- DOI：10.12382/bgxb.2025.0310
  CLC： V279;TP242.6
- Received：25 April 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
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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：

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

CHEN J Q, LI M Y, SU Y S, et al. Direction constraints adaptive extended bidirectional A * algorithm based on random two-dimensional map environments [J ] . Robotics and Autonomous Systems,2023,165:114430-114444.

GONG J J, CHEN J, CAI D S, et al. Disturbance observer-based passivity and impedance control for trajectory tracking in autonomous hydraulic excavators [J]. Automation in Construction, 2025,170:105898.

GONG J J, GUO S J, SHEN H T, et al. Path-tracking cascade control of hydraulic-tracked vehicles based on port-controlled Hamiltonian model[J]. IEEE Transactions on Intelligent Vehicles, 2024,10（1）:654-667.

CHENG X, ZHOU J M, ZHOU Z, et al. An improved RRT-Connect path planning algorithm of robotic arm for automatic sampling of exhaust emission detection in Industry 4. 0 [J]. Journal of Industrial Information Integration,2023,33:100436.

TU H Y, DENG Y Z, LI Q Y, et al. Improved RRT global path planning algorithm based on bridge test [J]. Robotics and Autonomous Systems,2024,171:104570.

GAMMELL J D, BARFOOT T D, SRINIVASA S S. Batch informed trees（BIT * ）: informed asymptotically optimal anytime search [J ] . The International Journal of Robotics Research, 2020, 39（5）:543-567.

WANG B P, JU D Y, XU F Z, et al. Bi-RRT * : an improved bidirectional RRT * path planner for robot in two-dimensional space [J ] . IEEJ Transactions on Electrical and Electronic Engineering,2023,18（10）:1639-1652.

GAMMELL J D, SRINIVASA S S, BARFOOT T D. Informed RRT * :optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C ] ∥Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, US:IEEE,2014:2997-3004.

梁秀满，刘子良，刘振东.基于深度强化学习的改进RRT算法路径规划[J].系统仿真学报，2025,37（10）:2578-2593.

LIANG X M, LIU Z L, LIU Z D. Path planning of improved RRT algorithm based on deep reinforcement learning [J]. Journal of System Simulation,2025,37（10）:2578-2593.（in Chinese）

潘云伟，李敏，曾祥光，等.基于人工势场和改进强化学习的自主式水下潜航器避障和航迹规划[J].兵工学报，2025, 46（4）:72-83.

PAN Y W, LI M, ZENG X G, et al.Obstacle avoidance and trajectory planning for autonomous underwater vehicles based on artificial potential field and improved reinforcement learning [J].Acta Armamentarii,2025,46（4）:72-83.（in Chinese）

YU Z H, XIANG L Y. NPQ-RRT * :an improved RRT * approach to hybrid path planning [J ] . Complexity, 2021, 2021（1）:6633878.

张越，张宁，徐熙平，等.基于GOTDBO算法的复杂约束条件下无人机航迹规划[J].兵工学报，2025,46（8）:240997.

ZHANG Y, ZHANG N, XU X P, et al. UAV trajectory planning under complex constraints based on GOTDBO algorithm [J]. Acta Armamentarii,2025,46（8）:240997.（in Chinese）

LIANG Y M, ZHAO H Y. CCPF-RRT * : an improved path planning algorithm with consideration of congestion [J ] . Expert Systems with Applications,2023,228:120403.

WANG X W, WEI J B, ZHOU X, et al. AEB-RRT * :an adaptive extension bidirectional RRT * algorithm[J ] . Autonomous Robots, 2022,46（6）:685-704.

FAN J M, CHEN X, LIANG X. UAV trajectory planning based on bi-directional APF-RRT * algorithm with goal-biased[J ] . Expert Systems with Applications,2023,213:119137.

POHAN M A R, TRILAKSONO B R, SANTOSA S P, et al. Path planning algorithm using the hybridization of the rapidly-exploring random tree and ant colony systems[J]. IEEE Access,2021,9:153599-153615.

MOHAMMED H, ROMDHANE L, JARADAT M A. RRT * N:an efficient approach to path planning in 3D for Static and dynamic environments [J ] . Advanced Robotics, 2021, 35（3/4）: 168-180.

GUO S J, GONG J J, SHEN H T, et al. DBVSB-P-RRT * :apath planning algorithm for mobile robot with high environmental adaptability and ultra-high speed planning [J ] . Expert Systems with Applications,2025,266:126123.

CAO M L, MAO H W, TANG X H, et al. A novel RRT * -connect algorithm for path planning on robotic arm collision avoidance [J ] . Scientific Repor ts,2025,15（1）:2836-2855.

JIANG L H, LIU S Y, CUI Y M, et al. Path planning for robotic manipulator in complex multi-obstacle environment based on improved_RRT[J]. IEEE/ASME Transactions on Mechatronics, 2022,27（6）:4774-4785.

DAI J, ZHANG Y, DENG H. Novel potential guided bidirectional RRT * with direct connection strategy for path planning of redundant robot manipulators in joint space [J ] . IEEE Transactions on Industrial Electronics,2023,71（3）:2737-2747.

MA G J, DUAN Y L, LI M Z, et al. A probability smoothing Bi-RRT path planning algorithm for indoor robot [J]. Future Generation Computer Systems,2023,143:349-360.

NICKABADI A, EBADZADE M M, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight [J].Applied Soft Computing,2011,11（4）:3658-3670.

SHENG Z K, SONG T Q, SONG J L, et al. Bidirectional rapidly exploring random tree path planning algorithm based on adaptive strategies and artificial potential fields [J].Engineering Applications of Artificial Intelligence,2025,148:110393.

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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

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