Robotic Arm Motion Planning Based on an Improved PRM - You Dazhang; Zhao Hengyi; Song Luwen (Jixie chuandong)
In order to solve the problems such as low efficiency, route redundancy and insufficient smoothness of the traditional probabilistic roadmap method (PRM), an improved PRM was proposed for obstacle avoidance path planning of manipulators. Sobol sequence sampling method was used to improve the connectivity of probabilistic road map and the success rate of the algorithm. Redundant node pruning and progressive route pruning based on dichotomous interpolation were adopted to make the path more approximate to the optimal solution. Finally, the path was smoothed by using the quintic polynomial interpolation method to make the motion of the manipulator more stable and smoother. The simulation results show that the improved PRM can improve the success rate of path planning, optimize the path, reduce the number of nodes and path length, and the manipulator is more uniform and smoother. The experiment proves that the method can effectively and reasonably realize the obstacle avoidance and path planning of the manipulator.