Path Planning of Robotic Arm Based on Improved RRT Algorithm Combined with A*

doi:10.3778/j.issn.1002-8331.2303-0157

Abstract

Abstract: For the problem that the RRT (rapidly-exploring random tree) path planning algorithm generates a huge number of nodes when planning the obstacle avoidance path of robotic arm in high-dimensional space, resulting in a large burden of algorithm operation, poor obstacle avoidance performance, and easy to fall into local extremes, an improved RRT algorithm combining A* judgment function is proposed. The sampling method of RRT is changed to generate a sequence of multiple randomly sampled points each time, and the improved A* judgment function is used for sorting. Distance judgment is performed on each generated node to prevent it from falling into local search. Finally, a repetitive greedy strategy is used to remove redundant nodes, and cubic B-spline is used to make path smooth. The performance of the algorithm is analyzed in 2D and 3D maps and robot arm simulations and prototype experiments. The improved RRT algorithm can effectively reduce the number of nodes for the robotic arm to reach the target poses, alleviate the local extremes, and avoid obstacles to reach the target poses quickly and stably, which proves the effectiveness and superiority of the improved RRT algorithm.

Key words: robotic arm, path planning, A* judgment function, rapidly-exploring random tree (RTT)

摘要： 针对RRT（rapidly-exploring random tree）路径规划算法在高维空间的机械臂避障路径规划时随机产生巨量节点，导致算法运行负担大、避障性能差、容易陷入局部极值的问题，提出一种结合A*判断函数的改进RRT算法。对RRT的采样方式进行更改，每次生成一个包含多个随机采样点的序列，并利用改进的A*判断函数进行排序；对每次生成节点进行距离判断，防止陷入局部搜索；利用重复贪心策略删除冗余节点，利用三次B样条平滑路径。在二维、三维地图及机械臂仿真与样机实验中进行算法性能分析，改进RRT算法能够大量减少到达目标位姿时产生的节点，缓解了局部极值，快速稳定地避开障碍物并到达目标位姿，证明了改进RRT算法的有效性和优越性。

关键词: 机械臂, 路径规划, A*判断函数, 快速扩展随机树（RTT）

LONG Houyun, LI Guang, TAN Xinxing, XUE Chenkang, YI Jing. Path Planning of Robotic Arm Based on Improved RRT Algorithm Combined with A*[J]. Computer Engineering and Applications, 2024, 60(4): 366-374.

龙厚云, 李光, 谭薪兴, 薛晨慷, 易静. 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.

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