Recently a line of researches has delved the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centralized control policy. To address this limitation, prior studies proposed incorporating $L$-hop delayed states into the computation. While this approach shows promise, it can lead to a lack of consensus among distant flock members and the formation of small clusters, consequently resulting in the failure of cohesive flocking behaviors. Instead, our approach leverages spatiotemporal GNN, named STGNN that encompasses both spatial and temporal expansions. The spatial expansion collects delayed states from distant neighbors, while the temporal expansion incorporates previous states from immediate neighbors. The broader and more comprehensive information gathered from both expansions results in more effective and accurate predictions. We develop an expert algorithm for controlling a swarm of robots and employ imitation learning to train our decentralized STGNN model based on the expert algorithm. We simulate the proposed STGNN approach in various settings, demonstrating its decentralized capacity to emulate the global expert algorithm. Further, we implemented our approach to achieve cohesive flocking, leader following and obstacle avoidance by a group of Crazyflie drones. The performance of STGNN underscores its potential as an effective and reliable approach for achieving cohesive flocking, leader following and obstacle avoidance tasks.

翻译：近年来，一系列研究探索了在图神经网络（GNNs）中实现群体机器人去中心化控制的方法。然而，已有研究表明仅依赖直接邻居的状态不足以模仿集中式控制策略。为解决这一局限，先前研究提出将$L$跳延迟状态纳入计算。尽管这一方法具有潜力，但会导致远距离集群成员间共识缺失并形成小规模子群，最终导致粘性集群行为的失败。相比之下，我们的方法利用名为STGNN的时空图神经网络，该网络同时包含空间扩展与时间扩展。空间扩展从远距离邻居处收集延迟状态，而时间扩展则整合直接邻居的历史状态。通过两种扩展获取的更广泛、更全面的信息，实现了更高效、更准确的预测。我们开发了控制机器人集群的专家算法，并采用模仿学习基于该专家算法训练去中心化STGNN模型。我们在多种场景下对提出的STGNN方法进行仿真，证明了其模仿全局专家算法的去中心化能力。此外，我们通过一组Crazyflie无人机实现了粘性集群、领航跟随及避障任务。STGNN的性能突显了其在实现粘性集群、领航跟随及避障任务中作为有效且可靠方法的潜力。