Sampling-based Tube Following for Redundant, Planar Robotic Manipulators

Abstract

This paper introduces a global, sampling-based motion planning approach to the tube following problem for redundant robot manipulators. Tube following is a motion planning problem where an under-defined end-effector path is given. We introduce a novel combination of existing task space and redundant configuration space sampling techniques. These techniques are applied to the tube following problem, comparing three different sampling techniques: uniform grid, uniform random and Halton sampling. In addition, an incremental sampling technique is proposed, specifically for tube following problems. Eventually, iterative grid refining is used to locally optimise path cost. Experimental results demonstrate the clear potential of these techniques to achieve fully automatic, collision-free motion planning for tube following with redundant robot manipulators. Our planning software is publicly available.