The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.

翻译：多机器人覆盖问题是执行检查或搜索和救援等任务系统的基本构件。 我们将覆盖问题分解为生成位置空间图, 并在图形中将机器人作为节点。 然后, 我们训练一个图形神经网络控制器, 利用任务的空间等同性来模拟专家的开放通道路径解决方案。 这个方法向大得多的地图和对专家来说难以操作的较大团队概括。 特别是, 模型将覆盖问题有效地概括为模拟10个测地器和数十座建筑。 我们还演示GNN控制器在勘探任务中可以超越基于规划的方法。