Abstract: In order to improve the success rate and efficiency of obstacle avoidance motion planning of cooperative robotic arms in complex environments such as cooperative operation and dynamic obstacles,a path planning method based on improved rapidly-exploring random tree(RRT) algorithm is proposed.On the basis of bidirectional RRT(Bi-RRT),the target bias strategy is first introduced.By reducing the randomness of the search process,the sampling efficiency can be improved.Secondly,in the process of random tree expansion,when the target point is not selected as a new node,the target point guidance strategy is adopted to promote the growth of the random tree in the direction of the target point,so as to effectively shorten the search time.Finally,the improved artificial potential field(APF) method is integrated into the improve Bi-RRT.By redesigning the potential field function and introducing the distance influence factor,it is endowed with local planning ability,reduces path redundancy,and further improves planning efficiency.Simulation results in two-dimensional and three-dimensional environments show that the proposed algorithm can generate shorter paths,and the random tree growth is more goal-oriented,and the planning time is significantly reduced.The algorithm is applied to the visual simulation of the robotic arm model,and the results show that it can effectively guide the robotic arm to avoid obstacles and accurately reach the target point.The effectiveness and practicability of the proposed methodare further confirmed by practical verification on the collaborative robotic arm.

Key words: Cooperative robot arm, Rapidly-exploring random tree, Artificial potential field, Goal guidance, Distance influence factor