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 (CBF) 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. Our main contribution is 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

翻译：多机器人系统的安全运行至关重要，特别是在通信降级环境中，如海底测绘、地下洞穴导航以及地外任务的组装与建造。我们解决了网络自主系统中机器人之间信息交换存在通信时延的安全问题。我们形式化了多机器人系统分布式控制屏障函数（CBF）的概念——一种适用于分布式实现的安全证书，为使用图神经网络学习安全分布式控制器提供了形式化基础。进一步地，我们观察到忽略时延学习分布式控制器会严重削弱安全性。我们的主要贡献是一个基于预测器的框架，用于在通信时延下训练安全分布式控制器，该框架根据接收数据和信息年龄预测邻近机器人的当前状态。在多机器人避碰数值实验中表明，所提出的基于预测器的方法能够显著提升通信时延下学习型分布式控制器的安全性。