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Multi-robot Coordination and Planning in Uncertain and Adversarial Environments

  • Group Robotics (M Gini and F Amigoni, Section Editors)
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Abstract

Purpose of Review

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.

Recent Findings

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.

Summary

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 develo** learning-based coordination strategies that are resilient to such attacks and robust to uncertainties is an important open area of research.

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Funding

The authors would like to thank the National Science Foundation (NSF IIS-1637915) and the Office of Naval Research (ONR N00014-18-1-2829) for their supports.

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  1. Electrical, Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA

    Lifeng Zhou

  2. Computer Science, University of Maryland, College Park, MD, 20742, USA

    Pratap Tokekar

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Zhou, L., Tokekar, P. Multi-robot Coordination and Planning in Uncertain and Adversarial Environments. Curr Robot Rep 2, 147–157 (2021). https://doi.org/10.1007/s43154-021-00046-5

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