A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning

TL;DR

This paper introduces a multi-stage probabilistic algorithm combining RRTs and local search to improve dynamic path planning in highly changing environments.

Contribution

It presents a novel multi-stage approach that enhances dynamic path planning by integrating RRTs with informed local search techniques.

Findings

Outperforms RRT variants in dynamic environments

Provides faster re-planning responses

Effective in highly dynamic scenarios

Abstract

Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be efficient in solving high dimensional problems. Even though several RRT variants have been proposed for dynamic replanning, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs for initial planning and informed local search for navigation. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.