HAS-RRT: RRT-based Motion Planning using Topological Guidance

TL;DR

HAS-RRT is a hierarchical motion planning algorithm that uses workspace skeleton guidance to significantly reduce runtime and tree size while maintaining path quality, outperforming existing methods especially in complex environments.

Contribution

Introduces HAS-RRT, a novel RRT-based planning method leveraging workspace skeletons for improved efficiency and robustness in motion planning.

Findings

Up to 91% reduction in runtime.

Tree size at least 30% smaller than competitors.

Robust performance even with poor workspace guidance.

Abstract

We present a hierarchical RRT-based motion planning strategy, Hierarchical Annotated-Skeleton Guided RRT (HAS-RRT), guided by a workspace skeleton, to solve motion planning problems. HAS-RRT provides up to a 91% runtime reduction and builds a tree at least 30% smaller than competitors while still finding competitive-cost paths. This is because our strategy prioritizes paths indicated by the workspace guidance to efficiently find a valid motion plan for the robot. Existing methods either rely too heavily on workspace guidance or have difficulty finding narrow passages. By taking advantage of the assumptions that the workspace skeleton provides, HAS-RRT is able to build a smaller tree and find a path faster than its competitors. Additionally, we show that HAS-RRT is robust to the quality of workspace guidance provided and that, in a worst-case scenario where the workspace skeleton provides no additional insight, our method performs comparably to an unguided method.