Task Space Motion Planning Decomposition

Authors

Nathan P. Larkin, University of Wollongong, Defence Materials Technology CentreFollow
Andrew Short, University of WollongongFollow
Zengxi Stephen Pan, University of Wollongong, Defence Materials Technology CentreFollow
Stephen van Duin, University of Wollongong, Defence Materials Technology CentreFollow

RIS ID

134560

Publication Details

Larkin, N., Short, A., Pan, Z. & van Duin, S. (2018). Task Space Motion Planning Decomposition. Proceedings - IEEE International Conference on Robotics and Automation (pp. 1688-1694). United States: IEEE.

Abstract

In autonomous robotics there are many situations that require solving a motion planning problem to complete a task. A Task Space (T-Space), composed of parameters that define the task being performed, can be a more effective planning space for these problems, however, planning within a T-Space is often computationally challenging. In this paper, we present a novel method to analyse the relationship between T-Space parameters and the pose of manipulator bodies to create a dependency matrix. We then use this information to decompose the motion planning problem into sequential lower complexity sub-problems. We call this approach Task Space Motion Planning Decomposition (TSMPD). This paper introduces TSMPD and quantifies the improvement to planning efficiency on a challenging maze navigation problem and weld path planning problem.