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，一系列研究探索了利用图神经网络（GNN）实现群体机器人的去中心化控制。然而，仅依赖相邻智能体的即时状态无法有效模仿集中式控制策略。为突破这一局限，先前研究提出将$L$跳延迟状态纳入计算。尽管该方法展现出潜力，却易导致远距离集群成员间缺乏共识并形成局部子群，最终破坏群体凝聚力。与之不同，本文提出的时空图神经网络（STGNN）融合了空间与时间两个维度的扩展：空间维度收集来自远邻的延迟状态，时间维度整合相邻智能体的历史状态。两者共同获取的更广泛且全面的信息，使预测更高效精准。我们开发了群体机器人控制的专家算法，并基于该算法通过模仿学习训练去中心化STGNN模型。在多种场景下的仿真实验表明，该模型具备模仿全局专家算法的去中心化能力。进一步地，我们在Crazyflie无人机集群上实现了该方法，成功完成群体凝聚、领航跟随与避障任务。STGNN的性能凸显了其作为实现群体凝聚、领航跟随与避障任务的有效可靠方法的潜力。