Research on Manipulator Path Planning based on Improved RRT*

Abstract

Aiming at the drawbacks of traditional RRT* algorithm in 3D path planning for manipulators, such as blind random sampling, low search efficiency and redundant initial paths, an improved RRT* path planning method integrating goal-biased sampling, adaptive step-size expansion, path pruning and B-spline smoothing is proposed. Firstly, goal-biased sampling is adopted to drive the search tree to expand toward the target area and avoid random exploration in irrelevant regions. Secondly, an adaptive step size is introduced on the basis of target guidance to further reduce redundant search. Finally, path pruning and B-spline smoothing are applied to optimize path compactness and trajectory continuity. A 3D static obstacle environment is established on MATLAB, and 100 repeated simulation experiments are conducted on RRT, RRT* and the improved RRT* algorithm. The experimental results show that the success rate of the improved algorithm reaches 100% in all three scenarios. In complex environments, its average planning time is 0.039727 s, which is lower than 0.122270 s of RRT and 1.201800 s of RRT*. Meanwhile, the average number of iterations and tree nodes are also significantly reduced. Ablation experiments verify that goal-biased sampling is the main factor for the improvement of search efficiency; the adaptive step size further enhances the expansion efficiency based on target guidance, and path pruning and smoothing effectively improve the quality of the final path.