Obstacle Avoidance Path Planning for Dual-Arm Robots

Abstract

With the improvement of the complexity of industrial production tasks, the dual-arm robot has the advantages of large working space, strong load capacity and dual-arm coordination work compared with the single robot arm, which makes it develop rapidly in the robot field. Collision-free path planning for dual-arm robots is a key problem in the research of path planning for unmanned robot systems. Firstly, this paper calculates the end workspace of the dual-arm robot based on Monte Carlo algorithm and establishes the collision detection model of the dual-arm robot. On this basis, an improved RRT*FN algorithm is proposed, which firstly adopts the sampling method based on the target bias strategy; secondly, an adaptive step strategy is introduced to shorten the path length; after that, the robot end path is linked with the robot body collision detection to improve the effectiveness of the algorithm; The Bessel curve is used to smooth the path to better meet the needs of the dual-arm robot in practical applications. Finally, the superiority and effectiveness of the collision detection model and the improved RRT*FN algorithm are verified in two-dimensional and three-dimensional simulation scenes.