Combining a Probabilistic Sampling Technique and Simple Heuristics to solve the Dynamic Path Planning Problem

TL;DR

This paper introduces a multi-stage probabilistic algorithm combining RRTs, local search, and greedy optimization to improve dynamic path planning in highly changing environments, outperforming existing RRT variants.

Contribution

It presents a novel combination of simple techniques with RRTs for dynamic path planning, enhancing responsiveness in environments with frequent changes.

Findings

Outperforms existing dynamic RRT variants in highly dynamic environments

Capable of recognizing when local search is stuck and restarting RRT

Provides more reliable path planning in environments with frequent changes

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 very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, 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 as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.