Adaptive Path Planning for Robotic Arms Integrating RRT* and APF

doi:10.16182/j.issn1004731x.joss.25-0617

Abstract

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.

Key words: robotic arm, obstacle avoidance planning, RRT* algorithm, APF, path optimization

CLC Number:

TP242.2

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

Figures/Tables 21

Fig. 1

Table 1

D-H parameters

连杆i	$θ i / r a d$	$α i / r a d$	$a i / m$	$d i / m$
1	$θ 1$	$π / 2$	0	0.079
2	$θ 2$	0	0.22	0
3	$θ 3$	$- π / 2$	0.06	0
4	$θ 4$	$π / 2$	0	-0.18
5	$θ 5$	$- π / 2$	0	0
6	$θ 6$	$π$	0	-0.05

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Table 2

Fig. 14

Table 3

Fig. 15

Fig. 16

Fig. 17

Fig. 18

References 23

[1]	吴其林, 赵韩, 陈晓飞, 等. 多臂协作机器人技术与应用现状及发展趋势[J]. 机械工程学报, 2023, 59(15): 1-16.
	Wu Qilin, Zhao Han, Chen Xiaofei, et al. Review of Technology, Application Status and Development Trend in Multi-arm Cooperative Robots[J]. Journal of Mechanical Engineering, 2023, 59(15): 1-16.
[2]	Tang Xianxing, Zhou Haibo, Xu Tianying. Obstacle Avoidance Path Planning of 6-DOF Robotic Arm Based on Improved A* Algorithm and Artificial Potential Field Method[J]. Robotica, 2024, 42(2): 457-481.
[3]	Zhou Xiwei, Yan Jingwen, Yan Mei, et al. Path Planning of Rail-mounted Logistics Robots Based on the Improved Dijkstra Algorithm[J]. Applied Sciences, 2023, 13(17): 9955.
[4]	邓冬冬, 许建民, 孟寒, 等. 基于蚁群算法与人工势场法融合的移动机器人路径规划[J]. 仪器仪表学报, 2025, 46(2): 1-16.
	Deng Dongdong, Xu Jianmin, Meng Han, et al. Mobile Robot Path Planning Based on the Fusion of Ant Colony Algorithm and Artificial Potential Field Method[J]. Chinese Journal of Scientific Instrument, 2025, 46(2): 1-16.
[5]	Zhang Chuang, Wang He, Fu Lihua, et al. Three-dimensional Continuous Picking Path Planning Based on Ant Colony Optimization Algorithm[J]. PLoS One, 2023, 18(2): e0282334.
[6]	薛光辉, 刘爽, 王梓杰, 等. 基于改进概率路线图算法的煤矿机器人路径规划方法[J]. 工矿自动化, 2023, 49(6): 175-181.
	Xue Guanghui, Liu Shuang, Wang Zijie, et al. A Path-planning Method for Coal Mine Robot Based on Improved Probability Road Map Algorithm[J]. Journal of Mine Automation, 2023, 49(6): 175-181.
[7]	李子建, 高焕兵, 王雪秋. 改进RRT算法的机械臂避障路径规划研究[J]. 计算机工程与应用, 2025, 61(8): 307-314.
	Li Zijian, Gao Huanbing, Wang Xueqiu. Research on Obstacle Avoidance Path Planning for Robotic Arms Using Improved RRT Algorithm[J]. Computer Engineering and Applications, 2025, 61(8): 307-314.
[8]	谭薪兴, 李光, 易静, 等. 改进RRT算法的机械臂路径规划[J]. 计算机集成制造系统, 2025, 31(3): 1014-1023.
	Tan Xinxing, Li Guang, Yi Jing, et al. Path Planning of Manipulator Based on Improved RRT Algorithm[J]. Computer Integrated Manufacturing Systems, 2025, 31(3): 1014-1023.
[9]	朱敏, 肖阳, 臧昭宇. 基于改进蚁群算法的工业机器人路径规划研究[J]. 合肥工业大学学报(自然科学版), 2023, 46(4): 463-467, 534.
	Zhu Min, Xiao Yang, Zang Zhaoyu. Research on Path Planning of Industrial Robot Based on Improved Ant Colony Algorithm[J]. Journal of Hefei University of Technology(Natural Science), 2023, 46(4): 463-467, 534.
[10]	贾浩铎, 房立金, 王怀震. 融合人工势场和Informed-RRT*算法的机械臂自适应路径规划[J]. 计算机集成制造系统, 2025, 31(4): 1179-1189.
	Jia Haoduo, Fang Lijin, Wang Huaizhen. Adaptive Path Planning of Manipulators Combining Informed-RRT* with Artificial Potential Field[J]. Computer Integrated Manufacturing Systems, 2025, 31(4): 1179-1189.
[11]	刘小松, 康磊, 单泽彪, 等. 基于RTSR-RRT*算法的机械臂路径规划[J]. 仪器仪表学报, 2025, 46(3): 65-73.
	Liu Xiaosong, Kang Lei, Shan Zebiao, et al. Path Planning of Robotic Arm Based on RTSR-RRT* Algorithm[J]. Chinese Journal of Scientific Instrument, 2025, 46(3): 65-73.
[12]	Kang Jingu, Lim Dong-Woo, Choi Yong-Sik, et al. Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning[J]. Sensors, 2021, 21(2): 333.
[13]	Wang Xianpeng, Ma Xinglu, Li Xiaoxu, et al. Target-biased Informed Trees: Sampling-based Method for Optimal Motion Planning in Complex Environments[J]. Journal of Computational Design and Engineering, 2022, 9(2): 755-771.
[14]	谭建豪, 潘豹, 王耀南, 等. 基于改进RRT*FN算法的机器人路径规划[J]. 控制与决策, 2021, 36(8): 1834-1840.
	Tan Jianhao, Pan Bao, Wang Yaonan, et al. Robot Path Planning Based on Improved RRT*FN Algorithm[J]. Control and Decision, 2021, 36(8): 1834-1840.
[15]	龙厚云, 李光, 谭薪兴, 等. 融合A*的改进RRT机械臂路径规划[J]. 计算机工程与应用, 2024, 60(4): 366-374.
	Long Houyun, Li Guang, Tan Xinxing, et al. Path Planning of Robotic Arm Based on Improved RRT Algorithm Combined with A*[J]. Computer Engineering and Applications, 2024, 60(4): 366-374.
[16]	陈丹, 谭钦, 徐哲壮. 基于采样点优化RRT算法的机械臂路径规划[J]. 控制与决策, 2024, 39(8): 2597-2604.
	Chen Dan, Tan Qin, Xu Zhezhuang. Robotic Arm Path Planning Based on Sampling Point Optimization RRT Algorithm[J]. Control and Decision, 2024, 39(8): 2597-2604.
[17]	宋勇, 张蕾, 田荣, 等. 复杂多场景下机械臂避障运动规划方法研究[J]. 西北工业大学学报, 2023, 41(3): 500-509.
	Song Yong, Zhang Lei, Tian Rong, et al. Research on Obstacle Avoidance Motion Planning Method of Manipulator in Complex Multi Scene[J]. Journal of Northwestern Polytechnical University, 2023, 41(3): 500-509.
[18]	崔炜, 朱发证. 机器人导航的路径规划算法研究综述[J]. 计算机工程与应用, 2023, 59(19): 10-20.
	Cui Wei, Zhu Fazheng. Review of Path Planning Algorithms for Robot Navigation[J]. Computer Engineering and Applications, 2023, 59(19): 10-20.
[19]	何庆, 罗仕杭. 混合改进策略的黑猩猩优化算法及其机械应用[J]. 控制与决策, 2023, 38(2): 354-364.
	He Qing, Luo Shihang. Chimp Optimization Algorithm Based on Hybrid Improvement Strategy and Its Mechanical Application[J]. Control and Decision, 2023, 38(2): 354-364.
[20]	代伟, 李创业, 杨春雨, 等. 基于低差异序列与快速扩展随机树融合算法的机械臂路径规划[J]. 控制理论与应用, 2022, 39(1): 130-144.
	Dai Wei, Li Chuangye, Yang Chunyu, et al. Manipulator Path Planning Using Fusion Algorithm of Low Difference Sequence and Rapidly Exploring Random Tree[J]. Control Theory & Applications, 2022, 39(1): 130-144.
[21]	Liang Juanling, Luo Wenguang, Qin Yongxin. Path Planning of Multi-axis Robotic Arm Based on Improved RRT*[J]. Computers, Materials & Continua, 2024, 81(1): 1009-1027.
[22]	齐本胜, 李岩, 苗红霞, 等. 基于改进启发式RRT的AUV路径规划[J]. 系统仿真学报, 2025, 37(1): 245-256.
	Qi Bensheng, Li Yan, Miao Hongxia, et al. Research on Path Planning Method for Autonomous Underwater Vehicles Based on Improved Informed RRT[J]. Journal of System Simulation, 2025, 37(1): 245-256.
[23]	牛金星, 王硕, 赵俊龙, 等. 基于目标引导的多目标苹果采摘路径规划方法[J]. 农业机械学报, 2025, 56(3): 208-215, 226.
	Niu Jinxing, Wang Shuo, Zhao Junlong, et al. Multi-objective Apple Picking Path Planning Method Based on Target Guidance[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025, 56(3): 208-215, 226.

算法	成功率/%	平均路径长度/m	平均运行时间/s	平均采样次数	迭代次数
RRT*	10	65.791	15.382	474	366
Informed-RRT*	70	62.573	5.030	476	476
GB-RRT*	100	64.064	2.560	446	236
PRM	100	68.368	1.670	—	—
文献[16]	95	61.740	7.470	444	448
AAPF-RRT*	100	57.382	1.433	265	198

算法	成功率/%	平均路径长度/m	平均运行时间/s	平均采样次数	迭代次数
RRT*	14	65.136	17.474	954	958
Informed-RRT*	82	59.783	10.512	944	562
GB-RRT*	100	61.907	2.416	924	269
PRM	100	64.698	1.725	—	—
文献[16]	98	58.062	8.320	596	543
AAPF-RRT*	100	56.495	1.330	589	240