Safe Distributed Control of Multi-Robot Systems with Communication Delays

TL;DR

This paper develops a predictor-based framework for safe distributed control of multi-robot systems operating under communication delays, enhancing safety through graph neural networks and formal safety certificates.

Contribution

It introduces a formal distributed control barrier function and a predictor-based training method to ensure safety despite communication delays in multi-robot systems.

Findings

Predictor-based controllers significantly improve safety under delays

Learning ignoring delays can severely degrade safety

Graph neural networks enable distributed safety certification

Abstract

Safe operation of multi-robot systems is critical, especially in communication-degraded environments such as underwater for seabed mapping, underground caves for navigation, and in extraterrestrial missions for assembly and construction. We address safety of networked autonomous systems where the information exchanged between robots incurs communication delays. We formalize a notion of distributed control barrier function for multi-robot systems, a safety certificate amenable to a distributed implementation, which provides formal ground to using graph neural networks to learn safe distributed controllers. Further, we observe that learning a distributed controller ignoring delays can severely degrade safety. We finally propose a predictor-based framework to train a safe distributed controller under communication delays, where the current state of nearby robots is predicted from received data and age-of-information. Numerical experiments on multi-robot collision avoidance show that our predictor-based approach can significantly improve the safety of a learned distributed controller under communication delays. A video abstract is available at https://youtu.be/Hcu1Ri32Spk.