RRT*former: Environment-Aware Sampling-Based Motion Planning using Transformer

TL;DR

RRT*former introduces a Transformer-enhanced sampling strategy for motion planning, leveraging environmental and historical data to improve path optimality and sampling efficiency in complex environments.

Contribution

It integrates a Transformer network with RRT* to utilize environmental and past sample information, enhancing sampling guidance in motion planning.

Findings

Outperforms RRT* and Neural RRT* in path optimality

Achieves higher sampling efficiency in complex environments

Demonstrates significant improvements through extensive experiments

Abstract

We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called \emph{RRT*former}, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.