Asymptotically-Optimal Multi-Query Path Planning for a Polygonal Robot

TL;DR

This paper introduces the rotation-stacked visibility graph (RVG), an efficient algorithm for multi-query path planning of polygonal robots that supports simultaneous translation and rotation, outperforming existing methods.

Contribution

The paper proposes RVG, a novel stacking approach of reduced visibility graphs for holonomic robots, which is resolution-complete and asymptotically optimal for combined translation and rotation.

Findings

RVG significantly reduces computation time.

RVG produces more optimal paths.

RVG outperforms existing sampling-based methods.

Abstract

Shortest-path roadmaps, also known as reduced visibility graphs, provides a highly efficient multi-query method for computing optimal paths in two-dimensional environments. Combined with Minkowski sum computations, shortest-path roadmaps can compute optimal paths for a translating robot in 2D. In this study, we explore the intuitive idea of stacking up a set of reduced visibility graphs at different orientations for a polygonal holonomic robot to support the fast computation of near-optimal paths, allowing simultaneous 2D translation and rotation. The resulting algorithm, rotation-stacked visibility graph (RVG), is shown to be resolution-complete and asymptotically optimal. Extensive computational experiments show RVG significantly outperforms state-of-the-art single- and multi-query sampling-based methods on both computation time and solution optimality fronts.