Hierarchical Planning with Annotated Skeleton Guidance

TL;DR

This paper introduces a hierarchical skeleton-guided motion planning algorithm that improves planning efficiency and path quality in cluttered environments by adaptively relaxing skeleton guidance during sampling.

Contribution

The proposed hierarchical strategy enhances skeleton-guided planning by gradually reducing reliance on the skeleton, leading to faster roadmap construction without sacrificing path quality.

Findings

Significant reduction in roadmap construction time.

Maintains path quality comparable to standard methods.

Effective in cluttered, multi-query environments.

Abstract

We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast. However, sometimes the skeleton does not closely represent the free c-space, which often misleads current skeleton-guided planners. The hierarchical skeleton-guided planning strategy gradually relaxes its reliance on the workspace skeleton as C space is sampled, thereby incrementally returning a sub-optimal path, a feature that is not guaranteed in the standard skeleton-guided algorithm. Experimental comparisons to the standard skeleton-guided planners and other lazy planning strategies show significant improvement in roadmap construction run time while maintaining path quality for multi-query problems in cluttered environments.