论文标题
使用空间图神经网络的多机器人覆盖范围和探索
Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks
论文作者
论文摘要
多机器人覆盖范围问题是执行检查或搜救等任务的系统的重要组成部分。我们将覆盖范围问题离散，以诱导位置的空间图，并将机器人表示为图中的节点。然后，我们训练一个图形神经网络控制器，该控制器利用任务的空间模糊性模仿专家开环路由解决方案。这种方法可以很好地推广到更大的地图和对专家棘手的大型团队。特别是，该模型有效地将其概括为对十个二次和数十个建筑物的模拟。我们还证明了GNN控制器可以在探索任务中超过计划的方法。
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.
