Sample-Based Planning with Volumes in Configuration Space

TL;DR

This paper introduces a sample-based planning method that uses volumes in configuration space to efficiently explore free space, improving performance in challenging regions like narrow passages while maintaining probabilistic completeness.

Contribution

It proposes a novel volume-based sampling approach that results in sparser trees and better focuses on difficult regions compared to traditional point-based methods.

Findings

Improved planning efficiency in narrow passages

Maintains probabilistic completeness guarantees

Produces sparser, more focused trees

Abstract

A simple sample-based planning method is presented which approximates connected regions of free space with volumes in Configuration space instead of points. The algorithm produces very sparse trees compared to point-based planning approaches, yet it maintains probabilistic completeness guarantees. The planner is shown to improve performance on a variety of planning problems, by focusing sampling on more challenging regions of a planning problem, including collision boundary areas such as narrow passages.