RRT*-based Robotic Manipulator Path Planning with Artificial Potential Field Guidance and Accumulator-based Adaptive Adjustment

Authors

• Fangzheng Gao, School of Automation, Nanjing Institute of Technology, Nanjing 211167, China; Jiangsu Provincial Engineering Research Center of Bionic Control Technology and Equipment, Nanjing 211167, China
• Xu Guo, School of Automation, Nanjing Institute of Technology, Nanjing 211167, China; Jiangsu Provincial Engineering Research Center of Bionic Control Technology and Equipment, Nanjing 211167, China
• Quan Sun, School of Automation, Nanjing Institute of Technology, Nanjing 211167, China; Jiangsu Provincial Engineering Research Center of Bionic Control Technology and Equipment, Nanjing 211167, China
• Jiacai Huang, School of Automation, Nanjing Institute of Technology, Nanjing 211167, China; Jiangsu Provincial Engineering Research Center of Bionic Control Technology and Equipment, Nanjing 211167, China
• Zhongcai Zhang, School of Engineering, Qufu Normal University, Rizhao 276826, China

Abstract

Abstract: To address the problems of blind sampling, slow convergence, and path redundancy in robotic manipulator path planning in cluttered and unstructured three-dimensional environments, An RRT* algorithm integrating artificial potential field guidance and accumulator-based adaptive adjustment, namely APGA-RRT* (artificial potential field guidance and accumulator adaptive adjustment-RRT*). The proposed algorithm improves the overall planning performance from three aspects: sampling, expansion, and path optimization. In the sampling stage, a potential-field-guided sampling strategy based on target-distance weighting and virtual-target switching is constructed, obstacle suppression and safe-distance gating mechanisms are incorporated to dynamically adjust the guidance intensity, thereby enhancing target directivity and local escape capability. In the expansion stage, an ACC collision feedback mechanism is introduced to jointly adjust the extension step size and rewiring depth, achieving a dynamic balance between fast exploration in open areas and precise obstacle avoidance in cluttered areas. In the path optimization stage, bidirectional redundant-node pruning and cubic B-spline smoothing are adopted to improve path continuity and executability. Experimental results show that, compared with the AAPF-RRT* algorithm, the proposed method reduces path length, planning time, and the number of sampling nodes by 12.0%, 30.2%, and 18.5%, respectively, in simple three-dimensional environments, and by 6.2%, 13.9%, and 15.6%, respectively, in complex three-dimensional obstacle environments. Extensive repeatability experiments with statistical analysis, together with UR5 manipulator physical validation results, collectively demonstrate that the APGA-RRT* algorithm exhibits significant advantages in path quality, planning efficiency, search stability, and engineering executability.

Recommended Citation

Gao, Fangzheng; Guo, Xu; Sun, Quan; Huang, Jiacai; and Zhang, Zhongcai (2026) "RRT*-based Robotic Manipulator Path Planning with Artificial Potential Field Guidance and Accumulator-based Adaptive Adjustment," Journal of System Simulation: Vol. 38: Iss. 9, Article 22.
DOI: 10.16182/j.issn1004731x.joss.26-0240
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss9/22

First Page

2755

Last Page

2772

CLC

TP242.2

Recommended Citation

Gao Fangzheng, Guo Xu, Sun Quan, et al. RRT*-based Robotic Manipulator Path Planning with Artificial Potential Field Guidance and Accumulator-based Adaptive Adjustment[J]. Journal of System Simulation, 2026, 38(9): 2755-2772.

DOI

10.16182/j.issn1004731x.joss.26-0240