GSRM: Building Roadmaps for Query-Efficient and Near-Optimal Path Planning Using a Reaction Diffusion System

TL;DR

This paper introduces GSRM, a novel method for constructing well-connected, efficient roadmaps for robot navigation using reaction diffusion systems, outperforming classical approaches in connectivity and path optimality.

Contribution

GSRM is a new approach that leverages reaction diffusion systems and Delaunay triangulation to generate superior roadmaps for robot path planning.

Findings

GSRM produces more connected roadmaps with evenly distributed vertices.

GSRM achieves shorter solution paths compared to classical methods.

GSRM offers higher query efficiency in navigation tasks.

Abstract

Mobile robots frequently navigate on roadmaps, i.e., graphs where edges represent safe motions, in applications such as healthcare, hospitality, and warehouse automation. Often the environment is quasi-static, i.e., it is sufficient to construct a roadmap once and then use it for any future planning queries. Roadmaps are typically used with graph search algorithm to find feasible paths for the robots. Therefore, the roadmap should be well-connected, and graph searches should produce near-optimal solutions with short solution paths while simultaneously be computationally efficient to execute queries quickly. We propose a new method to construct roadmaps based on the Gray-Scott reaction diffusion system and Delaunay triangulation. Our approach, GSRM, produces roadmaps with evenly distributed vertices and edges that are well-connected even in environments with challenging narrow passages. Empirically, we compare to classical roadmaps generated by 8-connected grids, probabilistic roadmaps (PRM, SPARS2), and optimized roadmap graphs (ORM). Our results show that GSRM consistently produces superior roadmaps that are well-connected, have high query efficiency, and result in short solution paths.