This paper tackles the problem of constructing a communication map of a known environment using multiple robots. A communication map encodes information on whether two robots can communicate when they are at two arbitrary locations and plays a fundamental role for a multi-robot system deployment to reliably and effectively achieve a variety of tasks, such as environmental monitoring and exploration. Previous work on communication map building typically considered only scenarios with a fixed base station and designed offline methods, which did not exploit data collected online by the robots. This paper proposes Gaussian Process-based online methods to efficiently build a communication map with multiple robots. Such robots form a mesh network, where there is no fixed base station. Specifically, we provide two leader-follower online sensing strategies to coordinate and guide the robots while collecting data. Furthermore, we improve the performance and computational efficiency by exploiting prior communication models that can be built from the physical map of the environment. Extensive experimental results in simulation and with a team of TurtleBot 2 platforms validate the approach.

中文翻译：

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用于通信地图构建的多机器人在线传感策略

本文解决了使用多个机器人构建已知环境的通信图的问题。通信图对有关两个机器人在两个任意位置时是否可以通信的信息进行编码，并且对于多机器人系统部署可靠而有效地完成各种任务（例如环境监控和探索）起着基本作用。以前有关通信地图构建的工作通常只考虑具有固定基站和设计的脱机方法的方案，这些方案没有利用机器人在线收集的数据。本文提出了一种基于高斯过程的在线方法，以有效地建立与多个机器人的通信地图。这种机器人形成了一个没有固定基站的网状网络。特别，我们提供两种跟随者的在线传感策略，以在收集数据时协调和指导机器人。此外，我们通过利用可以从环境物理图构建的现有通信模型来提高性能和计算效率。仿真的大量实验结果以及由TurtleBot 2平台组成的团队对该方法进行了验证。