Belief Space Planning: a Covariance Steering Approach
Dongliang Zheng, Jack Ridderhof, Panagiotis Tsiotras, Ali-akbar Agha-mohammadi
Abstract
A new belief space planning algorithm, called covariance steering Belief RoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion planning of dynamical systems under simultaneous motion and observation uncertainties. CS-BRM extends the probabilistic roadmap (PRM) approach to belief spaces and is based on the recently developed theory of covariance steering (CS) that enables guaranteed satisfaction of terminal belief constraints in finitetime. The CS-BRM algorithm allows the sampling of non-stationary belief nodes, and thus is able to explore the velocity space and find efficient motion plans. We evaluate CS-BRM in different planning problems and demonstrate the benefits of the proposed approach.
BibTeX
@inproceedings{icra2022_beliefspaceplann,
title = {Belief Space Planning: a Covariance Steering Approach},
author = {Dongliang Zheng and Jack Ridderhof and Panagiotis Tsiotras and Ali-akbar Agha-mohammadi},
booktitle = {ICRA 2022},
year = {2022}
}