We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique, we proposed an offline learning structure that makes every robot capable of communicating with and fusing information from its neighbors to plan its next move towards the most informative parts of the environment for map classification purposes. The main idea is to decompose a given undirected graph into a union of directed star graphs and train robots w.r.t a bounded number of star graphs. This will significantly reduce the computational cost of offline training and makes learning scalable (independent of the number of robots). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations.

翻译：我们考虑使用一组通信机器人对地图进行分类的问题。 假设所有机器人都具有局部视觉遥感能力,可以与相邻机器人交流信息。 使用图形分解技术,我们建议建立一个离线学习结构,使每个机器人能够与其邻居通信并阻断信息,为地图分类目的计划下一个向环境中信息最丰富的部分移动。 主要的想法是将一个未定向的图形分解成一个有定向恒星图的组合,并训练一个有条不紊的恒星图。 这将大大降低离线培训的计算成本,并使学习变得可扩展(取决于机器人的数量 ) 。 我们的方法对于使用大量通信机器人在大环境中快速进行地图分类特别有用。 我们通过广泛的模拟验证我们拟议方法的效用。