论文标题
分散的学习，具有有限的通信，用于多机器人的空间领域的多机器人覆盖范围
Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields
论文作者
论文摘要
本文为一组移动机器人提供了一种算法，可以同时在域上学习一个空间字段，并在空间上分发自己以最佳覆盖。从以前通过集中式高斯过程估算空间场的方法，这项工作利用了覆盖范围问题的空间结构，并提出了一种分散的策略，其中样本通过通过Voronoi分区的边界来建立通信在本地汇总。我们提出了一种算法，每个机器人都通过其自身测量值和Voronoi邻居提供的局部高斯过程运行一个局部高斯过程，该过程仅在提供足够新颖的信息时才将其纳入单个机器人的高斯过程中。在模拟中评估算法的性能，并与集中式方法进行比较。
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.
