Self-motion graph in path planning for redundant robots along specified end-effector paths

Abstract

We consider the problem of planning collision-free paths for a redundant robot manipulator whose end-effector must travel along a specified path. A probabilistic method has been proposed for the problem, which does not allow self-motions of the robot as it moves along the end-effector path. In this paper, we propose an enhancement, which allows such self-motions. This is primarily accomplished by explicitly representing self-motions for a certain pose as a self-motion graph, which is explored with probabilistic techniques for closed-chain robots. Computer simulations show that this enhancement improves performance in most cases. Depending on the limits set on the run-time (always needed in practice for probabilistic sampling methods), the planner with self-motion enhancement will find a path where the original algorithm without self-motion may not