Adaptive Path Planning for Robotic Arms Integrating RRT* and APF

Authors

• Zhirun Chen, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Jie Yuan, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Erkenbieke Jia, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Ningning Zhang, School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
• Chao Liu, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Yushan Ye, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China

Abstract

To address the issues of large search space, low efficiency, and slow convergence of the RRT* algorithm in 3D path planning of robotic manipulators, an adaptive path planning algorithm integrating RRT* and APF is proposed. In the sampling phase, a Sobol sequence-based obstacle avoidance strategy and an APF adaptive-threshold, goal-biased sampling method are used to improve the quality of sampling points. During the expansion phase, sampling, attractive, and repulsive vectors are integrated, and adaptive weights are designed based on environmental information to generate a resultant force direction, thus enhancing the expansion guidance. For step size control, the obstacle repulsive potential field is divided into three characteristic regions, and the step size is adaptively adjusted based on the regional potential field intensity to balance global planning and local obstacle avoidance. Redundant points are removed, and cubic B-spline interpolation is used to smooth and optimize the path, improving the stability of robotic arm operation. Simulation results show that, compared with the traditional RRT* algorithm, the proposed method reduces the average number of sampling points by 38%, shortens running time by 92%, and decreases path length by 13% in high-density 3D obstacle avoidance experiments.

Recommended Citation

Chen, Zhirun; Yuan, Jie; Jia, Erkenbieke; Zhang, Ningning; Liu, Chao; and Ye, Yushan (2026) "Adaptive Path Planning for Robotic Arms Integrating RRT* and APF," Journal of System Simulation: Vol. 38: Iss. 6, Article 14.
DOI: 10.16182/j.issn1004731x.joss.25-0617
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss6/14

First Page

1669

Last Page

1683

CLC

TP242.2

Recommended Citation

Chen Zhirun, Yuan Jie, Jia Erkenbieke, et al. Adaptive Path Planning for Robotic Arms Integrating RRT* and APF[J]. Journal of System Simulation, 2026, 38(6): 1669-1683.

Corresponding Author

Yuan Jie

DOI

10.16182/j.issn1004731x.joss.25-0617