Simulation of Manipulator Path Planning Based on Improved RRT* Algorithm
XueShen Liu, Lijia Cao · 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · 2022
The problem of obstacle avoidance path planning of 6-axis manipulator is studied. After analyzing the disadvantages of large randomness and low algorithm efficiency of RRT*, an improved RRT* algorithm is proposed, which can quickly search the shortest expected path that can effectively avoid obstacles. Firstly, the path pruning process of RRT* is improved to improve the degree of path optimization, and then the global sampling space is reduced, so that the random tree can quickly expand to the target point. The algorithm is verified on the MATLAB simulation platform. The results show that the average time of path search and the number of expansion nodes of the improved RRT* algorithm are greatly reduced, the path length is also better than the traditional RRT*, and can be well applied to the path planning of manipulator.