Sparser Sparse Roadmaps

TL;DR

This paper introduces a hybrid sampling method for offline generation of sparse roadmap spanners that significantly reduces graph size while maintaining path quality, enhancing planning efficiency in high-dimensional robotic scenarios.

Contribution

The paper proposes a novel hybrid sampling approach and optimization techniques for sparse roadmap generation, improving size and efficiency over existing methods like SPARS2.

Findings

Graphs are 79% smaller than existing approaches.

Solutions maintain equivalent path quality.

Enhanced planning speed in high-dimensional spaces.

Abstract

We present methods for offline generation of sparse roadmap spanners that result in graphs 79% smaller than existing approaches while returning solutions of equivalent path quality. Our method uses a hybrid approach to sampling that combines traditional graph discretization with random sampling. We present techniques that optimize the graph for the L1-norm metric function commonly used in joint-based robotic planning, purposefully choosing a $t$-stretch factor based on the geometry of the space, and removing redundant edges that do not contribute to the graph quality. A high-quality pre-processed sparse roadmap is then available for re-use across many different planning scenarios using standard repair and re-plan methods. Pre-computing the roadmap offline results in more deterministic solutions, reduces the memory requirements by affording complex rejection criteria, and increases the speed of planning in high-dimensional spaces allowing more complex problems to be solved such as multi-modal task planning. Our method is validated through simulated benchmarks against the SPARS2 algorithm. The source code is freely available online as an open source extension to OMPL.