Motion Planning for Multi-Link Robots by Implicit Configuration-Space Tiling

TL;DR

This paper introduces a novel motion-planning algorithm for multi-link robots that precomputes a tiling roadmap to efficiently handle self-collision checks, significantly speeding up planning in various scenarios.

Contribution

The work presents a precomputed tiling roadmap approach that encodes self-collision free spaces, reducing online computation and increasing flexibility compared to existing methods.

Findings

Algorithm is over fifty times faster than state-of-the-art in some cases.

Preprocessing enables efficient collision checking with obstacles.

Applicable to open and closed-chain multi-link robots.

Abstract

We study the problem of motion-planning for free-flying multi-link robots and develop a sampling-based algorithm that is specifically tailored for the task. Our work is based on the simple observation that the set of configurations for which the robot is self-collision free is independent of the obstacles or of the exact placement of the robot. This allows to eliminate the need to perform costly self-collision checks online when solving motion-planning problems, assuming some offline preprocessing. In particular, given a specific robot type our algorithm precomputes a tiling roadmap, which efficiently and implicitly encodes the self-collision free (sub-)space over the entire configuration space, where the latter can be infinite for that matter. To answer any query, in any given scenario, we traverse the tiling roadmap while only testing for collisions with obstacles. Our algorithm suggests more flexibility than the prevailing paradigm in which a precomputed roadmap depends both on the robot and on the scenario at hand. We show through various simulations the effectiveness of this approach on open and closed-chain multi-link robots, where in some settings our algorithm is more than fifty times faster than the state-of-the-art.