REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field

TL;DR

REVISE is a novel multi-query algorithm that creates robust belief roadmaps for dynamic systems in Gaussian random fields, significantly improving planning accuracy and cost over existing methods.

Contribution

It introduces a robust covariance steering controller and an edge rewiring technique, enhancing belief roadmap coverage and robustness in probabilistic motion planning.

Findings

10x improvement in median plan accuracy

2.5x reduction in median plan cost

Effective in 6DoF systems

Abstract

This paper presents Robust samplE-based coVarIance StEering (REVISE), a multi-query algorithm that generates robust belief roadmaps for dynamic systems navigating through spatially dependent disturbances modeled as a Gaussian random field. Our proposed method develops a novel robust sample-based covariance steering edge controller to safely steer a robot between state distributions, satisfying state constraints along the trajectory. Our proposed approach also incorporates an edge rewiring step into the belief roadmap construction process, which provably improves the coverage of the belief roadmap. When compared to state-of-the-art methods, REVISE improves median plan accuracy (as measured by Wasserstein distance between the actual and planned final state distribution) by 10x in multi-query planning and reduces median plan cost (as measured by the largest eigenvalue of the planned state covariance at the goal) by 2.5x in single-query planning for a 6DoF system. We will release our code at https://acl.mit.edu/REVISE/.