Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments due to limited sensing and communication capabilities. The robots must execute a Perception-Action-Communication (PAC) loop -- they perceive their local environment, communicate with other robots, and take actions in real time. A fundamental challenge in decentralized PAC systems is to decide what information to communicate with the neighboring robots and how to take actions while utilizing the information shared by the neighbors. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. Although conceptually, GNN policies are fully decentralized, the evaluation and deployment of such policies have primarily remained centralized or restrictively decentralized. Furthermore, existing frameworks assume sequential execution of perception and action inference, which is very restrictive in real-world applications. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication. In particular, we use aggregated GNNs, which enable the exchange of hidden layer information between robots for computational efficiency and decentralized inference of actions. Furthermore, the modules in the framework are asynchronous, allowing robots to perform sensing, extracting information, communication, action inference, and control execution at different frequencies. We demonstrate the effectiveness of GNNs executed in the proposed framework in navigating large robot swarms for collaborative coverage of large environments.

翻译：大规模机器人群体在大型环境中协同实现共同全局目标，因感知与通信能力受限而极具挑战性。机器人必须执行感知-行动-通信（PAC）循环——实时感知局部环境、与其他机器人通信并采取行动。分散式PAC系统的一个基本难题在于：如何决定向邻接机器人传递何种信息，以及如何在利用邻居共享信息的同时采取行动。近期，该问题已通过图神经网络（GNN）在诸如集群编队和覆盖控制等应用中得到解决。尽管GNN策略在概念上完全去中心化，但其评估与部署仍主要集中于中心化或受限去中心化模式。此外，现有框架假设感知与动作推理以顺序执行，这在现实应用中具有极大局限性。本文提出一种面向机器人群体异步PAC的框架，采用去中心化GNN计算导航动作并生成通信消息。具体而言，我们使用聚合GNN，使机器人间能够交换隐藏层信息以实现计算高效性和去中心化动作推理。此外，框架中的各模块支持异步运行，允许机器人以不同频率执行感知、信息提取、通信、动作推理与控制执行。我们通过实验证明了在所提框架中执行的GNN在引导大规模机器人群体协同覆盖大型环境中的有效性。