A Levy Flight based Narrow Passage Sampling Method for Probabilistic Roadmap Planners

TL;DR

This paper introduces a Levy Flight-based sampling method for probabilistic roadmap planners that enhances the sampling of narrow passages, improving planner completeness and efficiency in high-dimensional robot motion planning.

Contribution

The paper presents a novel Levy Flight technique for better sampling in narrow regions, outperforming existing methods in collision calls and computational efficiency.

Findings

Improved sample quality in narrow passages.

Reduces collision calls compared to state-of-the-art methods.

Robust across different narrow passage parameters.

Abstract

Sampling based probabilistic roadmap planners (PRM) have been successful in motion planning of robots with higher degrees of freedom, but may fail to capture the connectivity of the configuration space in scenarios with a critical narrow passage. In this paper, we show a novel technique based on Levy Flights to generate key samples in the narrow regions of configuration space, which, when combined with a PRM, improves the completeness of the planner. The technique substantially improves sample quality at the expense of a minimal additional computation, when compared with pure random walk based methods, however, still outperforms state of the art random bridge building method, in terms of number of collision calls, computational overhead and sample quality. The method is robust to the changes in the parameters related to the structure of the narrow passage, thus giving an additional generality. A number of 2D & 3D motion planning simulations are presented which shows the effectiveness of the method.