融合RRT*和APF的机械臂自适应路径规划

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

摘要/Abstract

摘要：

为解决机械臂三维路径规划中RRT*算法搜索空间大、效率低、收敛慢的问题，提出一种融合RRT*和APF的自适应路径规划算法。采样阶段，采用Sobol序列的障碍物规避策略和APF自适应调整阈值的目标偏向采样方法，提高采样点的质量；扩展阶段，融合采样、引力和斥力3种向量，依据环境信息设计自适应权重，生成合力方向，增强扩展导向性；步长控制方面，将障碍物斥力势场划分为3个特征空间区域，基于区域势场强度自适应调节步长，平衡全局规划和局部避障；剔除冗余点并采用三次B样条插值对路径进行平滑优化，提高机械臂运行的稳定性。仿真结果表明：较传统RRT*算法，该算法在高密度障碍物三维空间避障实验中平均采样点数减少38%，运行时间缩短92%，路径长度下降13%。

关键词: 机械臂, 避障规划, RRT*算法, APF, 路径优化

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

中图分类号:

TP242.2

陈治润,袁杰,加尔肯别克等 . 融合RRT*和APF的机械臂自适应路径规划[J]. 系统仿真学报, 2026, 38(6): 1669-1683.

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.

图/表 21

图1

表1

D-H参数

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

表1

图2

图3

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图5

图6

图7

图8

图9

图10

图11

图12

图13

表2

图14

表3

图15

图16

图17

图18

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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