This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages the spatial structure of the coverage problem and presents a decentralized strategy where samples are aggregated locally by establishing communications through the boundaries of a Voronoi partition. We present an algorithm whereby each robot runs a local Gaussian process calculated from its own measurements and those provided by its Voronoi neighbors, which are incorporated into the individual robot's Gaussian process only if they provide sufficiently novel information. The performance of the algorithm is evaluated in simulation and compared with centralized approaches.

翻译：本文介绍了一组移动机器人同时在某一域上学习空间场的算法, 并用空间分布来优化空间场的覆盖。 根据以前通过中央集权高斯进程对空间场进行估计的方法, 这项工作利用了覆盖问题的空间结构, 并提出了一个分散化的战略, 通过Voronoi分区的边界建立通信, 将样本集中到本地。 我们提出了一个算法, 让每个机器人运行一个本地高斯过程, 该过程根据自己的测量结果和Voronoi邻居提供的程序计算, 只有当它们提供了足够新颖的信息时, 才能将其纳入个体机器人高斯进程。 算法的性能通过模拟和与集中方法进行比较来评估。