Integrating asymptotically-optimal path planning with local optimization

TL;DR

This paper presents a novel integration of asymptotically optimal path planning with local optimization to enhance planning speed and path quality in unpredictable environments, demonstrated with RRTConnect* and shortcutting.

Contribution

The paper introduces a new method combining asymptotically optimal planners with local optimizers, improving convergence speed and path quality.

Findings

Paths are 31% faster to execute with the integrated approach.

Significant performance improvement over RRTConnect*.

Effective in unpredictable environments.

Abstract

Many robots operating in unpredictable environments require an online path planning algorithm that can quickly compute high quality paths. Asymptotically optimal planners are capable of finding the optimal path, but can be slow to converge. Local optimisation algorithms are capable of quickly improving a solution, but are not guaranteed to converge to the optimal solution. In this paper we develop a new way to integrate an asymptotically optimal planners with a local optimiser. We test our approach using RRTConnect* with a short-cutting local optimiser. Our approach results in a significant performance improvement when compared with the state-of-the-art RRTConnect* asymptotically optimal planner and computes paths that are 31\% faster to execute when both are given 3 seconds of planning time.