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，尤其是在通信条件受限的环境中，例如用于海底测绘的水下环境、用于导航的地下洞穴以及用于装配与建造的地外任务环境中。本文研究网络化自主系统的安全性问题，其中机器人间交换的信息存在通信延迟。我们为多机器人系统形式化了一种分布式控制屏障函数（CBF）的概念——这是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。此外，我们观察到，忽略延迟学习分布式控制器会严重损害安全性。我们的主要贡献是提出了一种基于预测器的框架，用于在通信延迟下训练安全分布式控制器。该框架利用接收数据和信息年龄来预测邻近机器人的当前状态。在多机器人避撞的数值实验中表明，我们基于预测器的方法能显著提升通信延迟下学习所得分布式控制器的安全性。