Research on Collision Avoidance Path Planning of Dual Manipulator Robot Based on Fusion Algorithm

Abstract

When a dual manipulator robot performs handling operations in a complex space, it is important to plan a collision avoidance path to the target point quickly and accurately. In order to solve the problems of local minimum and high sampling randomness of traditional path planning algorithms, a new fusion algorithm is proposed for global path planning of a dual manipulator robot. First, an improved artificial potential field (IAPF) method is mentioned for path planning in the start and target point areas. Then, for the local minimum problem, an improved RRT algorithm (IRRT) based on the ε-greedy sampling target biasing strategy and repeated iterative update strategy is fused to reduce the randomness of random tree growth, and explore an optimal path that grows toward the target as much as possible for jumping out of the local minimum area. The URDF model file of the dual manipulator robot is created, and simulation experiments of path planning are conducted based on the Rviz visualization tool under Ros system, which proves the effectiveness of the fusion algorithm.