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.

中文翻译：

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不确定和对抗性环境中的多机器人协调和计划

部署可以仔细协调其动作的机器人团队可以使整个系统对单个故障具有鲁棒性。在本报告中，我们回顾了最近的算法开发，该算法使多机器人系统对环境的不确定性，故障和对抗性攻击具有鲁棒性。在多机器人协调领域的最新研究中，我们发现以下三个趋势：（1）弹性协调以承受故障和/或攻击或从故障/攻击中恢复；（2）风险意识协调，以权衡因环境不确定性而产生风险的权衡风险和报酬；（3）基于图神经网络的协调，以学习分散的多机器人协调策略。这些算法已应用于诸如编队控制，任务分配和计划，搜索和计划，和信息丰富的数据收集。为了使多机器人系统变得可行，我们需要协调算法，这些算法可以扩展到处理动态变化，容易出现故障，竞争和不确定环境的大型机器人团队。最近有大量关于多机器人协调的研究，这些研究为解决这些问题和缩小理论与实践之间的鸿沟提供了具有弹性和风险意识的算法。基于学习的方法被认为是有前途的，特别是因为它们可以学习谁，何时以及如何进行沟通以进行有效的协调。但是，这些算法也已证明容易受到对抗性攻击，因此，开发基于学习的，能够抵御此类攻击且对不确定性具有鲁棒性的协调策略是一个重要的开放研究领域。