Quotient-Space Motion Planning
Abstract
A motion planning algorithm computes the motion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotient-space decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).
Cite
@article{arxiv.1807.09468,
title = {Quotient-Space Motion Planning},
author = {Andreas Orthey and Adrien Escande and Eiichi Yoshida},
journal= {arXiv preprint arXiv:1807.09468},
year = {2018}
}