We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to the problem of flocking to demonstrate performance on communication graphs that change as the robots move. We examine how a decreasing communication radius and faster velocities increase the value of multi-hop information.
翻译:我们考虑了为大型移动机器人网络寻找分布式控制器的问题,这些网络具有互动动态和很少可用的通信。我们的方法是通过在培训时间模仿中央控制器的政策,在测试时间学习只需要本地信息和通信的本地控制器。通过将汇总图神经网络扩大到不同的信号和时间支持,我们学到了一个单一的通用的地方控制器,它只利用本地通信交换器利用远方队友的信息。我们用这个方法来在机器人移动时变化的通信图上展示其性能。我们研究了通信半径的缩小和速度的加快如何增加多跳信息的价值。