A rapidly exploring random tree optimization algorithm for space robotic manipulators guided by obstacle avoidance independent potential field

Abstract

The crucial problem of obstacle avoidance path planning is to realize both reducing the operational cost and improving its efficiency. A rapidly exploring random tree optimization algorithm for space robotic manipulators guided by obstacle avoidance independent potential field is proposed in this article. Firstly, some responding layer factors related to operational cost are used as optimization objective to improve the operational reliability. On this basis, a potential field whose gradient is calculated off-line is established to guide expansion of rapidly exploring random tree. The potential field mainly considers indexes about manipulator itself, such as the minimum singular value of Jacobian matrix, manipulability, condition number, and joint limits of manipulator. Thus, it can stay the same for different obstacle avoidance path planning tasks. In addition, a K-nearest neighbor–based collision detection strategy is integrated for accelerating the algorithm. The strategy use the distance between manipulator and obstacles instead of the collision state of manipulator to estimate the distance between new sample configuration and obstacle. Finally, the proposed algorithm is verified by an 8-degree of freedom manipulator. The comparison between the proposed algorithm and a heuristic exploring–based rapidly exploring random tree indicates that the algorithm can improve the efficiency of path planning and shows better kinematic performance in the task of obstacle avoidance.