Motion Planning for Multi-Link Robots by Implicit Configuration-Space Tiling
This addresses motion planning efficiency for multi-link robots, offering a flexible approach that is incremental over existing sampling-based methods.
The paper tackles motion planning for multi-link robots by precomputing a tiling roadmap that encodes self-collision-free configurations offline, eliminating costly online self-collision checks. It demonstrates effectiveness in simulations, achieving over fifty times faster performance than state-of-the-art methods in some settings.
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.