论文标题
使用本地观测值分布式地图分类
Distributed Map Classification using Local Observations
论文作者
论文摘要
我们考虑使用通信机器人团队对地图进行分类的问题。假定所有机器人都具有局部的视觉传感功能，并且可以与相邻的机器人交换其信息。使用图形分解技术，我们提出了一个离线学习结构，该结构使每个机器人都能与邻居进行通信和融合信息，以计划下一步向环境中最有用的部分迈进，以进行地图分类目的。主要思想是将给定的无向图分解为有向星图和训练机器人W.R.T的符合的恒星图的结合。这将大大降低离线培训的计算成本并使学习可扩展（与机器人的数量无关）。我们的方法对于使用大量通信机器人在大环境中的快速地图分类特别有用。我们通过广泛的模拟来验证我们提出的方法的实用性。
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.
