Novel Potential Guided Bidirectional RRT<sup>*</sup> With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space
Jun Dai, Yi Zhang, Hua Deng
Abstract
To avoid the cumbersome calculation of inverse kinematics and improve the efficiency of obstacle avoidance, a novel potential guided bidirectional rapidly-exploring random tree star with the direct connection strategy for redundant robot manipulators in the joint space is proposed. Firstly, an expansion strategy based on the artificial potential field is designed in the joint space. Then this expansion strategy is combined with the goal-biased bidirectional rapidly-exploring random tree star (GB-RRT*) to improve the ability of obstacle avoidance. Secondly, a direct connection strategy is designed to improve expansion efficiency. Finally, the effectiveness and superiority of the proposed algorithm are verified by simulations and experiments. The results show that, compared with bidirectional RRT and GB-RRT*, the proposed algorithm can plan a shorter path with a wider clearance between the redundant robot manipulator and obstacles, generate fewer invalid nodes that collide with obstacles, and spend less time. Therefore, it can avoid obstacles more effectively and obtain an optimal path.