Cooperative Path Planning of Dual-arm Robot Based on Attractive Force Self-adaptive Step Size RRT

In the RRT (rapidly-exploring random tree) method, the determination of the step size depends too much on program debugging, and collision detection failure may occur due to the fixed step size. For this problem, an attractive adaptive step size RRT for cooperative path planning of the dual-arm robot is proposed. Firstly, the step size norm inequality between the configuration space and the workspace is established to constrain the step size generated by the dual-arm robot in the workspace, and thus the effective collision detection is guaranteed. Then, a passive growth method of random tree is proposed to reduce the dimension of planning space while ensuring cooperative motion of the dual-arm robot. Finally, the attractive force function is introduced at the nodes of the random tree to speed up the fusion of the algorithm. The simulation results show that the attractive force self-adaptive step size RRT method can constrain the step size in the workspace effectively to ensure the effectiveness of collision detection. On the premise of no collision, the attractive adaptive step size RRT method reduces the number of iterations, the running time and the path length compared with other algorithms. The proposed algorithm is applied to the prototype experiment of the dual-arm robot. The experiment results show that the dual-arm robot can complete the obstacle avoidance motion on the premise of maintaining the position coordination, which verifies the effectiveness of the algorithm.