Multi-Robot Coordination and Planning in Uncertain and Adversarial Environments

TL;DR

This paper reviews recent advances in multi-robot coordination algorithms designed to enhance robustness against failures, environmental uncertainties, and adversarial attacks, emphasizing scalable, risk-aware, and learning-based approaches.

Contribution

It provides a comprehensive overview of recent algorithmic trends in resilient, risk-aware, and graph neural network-based multi-robot coordination, highlighting their applications and open challenges.

Findings

Resilient coordination algorithms improve robustness to failures and attacks.

Risk-aware strategies balance environmental uncertainty and task rewards.

Graph Neural Networks enable decentralized learning of coordination policies.

Abstract

Deploying a team of robots that can carefully coordinate their actions can make the entire system robust to individual failures. In this report, we review recent algorithmic development in making multi-robot systems robust to environmental uncertainties, failures, and adversarial attacks. We find the following three trends in the recent research in the area of multi-robot coordination: (1) resilient coordination to either withstand failures and/or attack or recover from failures/attacks; (2) risk-aware coordination to manage the trade-off risk and reward, where the risk stems due to environmental uncertainty; (3) Graph Neural Networks based coordination to learn decentralized multi-robot coordination policies. These algorithms have been applied to tasks such as formation control, task assignment and scheduling, search and planning, and informative data collection. In order for multi-robot systems to become practical, we need coordination algorithms that can scale to large teams of robots dealing with dynamically changing, failure-prone, contested, and uncertain environments. There has been significant recent research on multi-robot coordination that has contributed resilient and risk-aware algorithms to deal with these issues and reduce the gap between theory and practice. Learning-based approaches have been seen to be promising, especially since they can learn who, when, and how to communicate for effective coordination. However, these algorithms have also been shown to be vulnerable to adversarial attacks, and as such developing learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.