We consider the spatial classification problem for monitoring using data collected by a coordinated team of mobile robots. Such classification problems arise in several applications including search-and-rescue and precision agriculture. Specifically, we want to classify the regions of a search environment into interesting and uninteresting as quickly as possible using a team of mobile sensors and mobile charging stations. We develop a data-driven strategy that accommodates the noise in sensed data and the limited energy capacity of the sensors, and generates collision-free motion plans for the team. We propose a bi-level approach, where a high-level planner leverages a multi-armed bandit framework to determine the potential regions of interest for the drones to visit next based on the data collected online. Then, a low-level path planner based on integer programming coordinates the paths for the team to visit the determined regions subject to the physical constraints. We characterize several theoretical properties of the proposed approach, including anytime guarantees and task completion time. We show the efficacy of our approach in simulation, and further validate these observations in physical experiments using mobile robots.

翻译：我们研究了通过协调移动机器人团队收集数据实现监测任务的空间分类问题。此类分类问题广泛存在于搜索救援、精准农业等应用场景中。具体而言，我们期望利用由移动传感器与移动充电站组成的团队，尽可能快速地实现搜索环境中的区域分类（感兴趣区域与不感兴趣区域）。我们提出了一种数据驱动策略，能够适应传感数据的噪声特性与传感器有限能量容量，并为机器人团队生成无碰撞运动规划。我们采用双层架构：高层规划器基于多臂赌博机框架，依据在线采集数据实时确定无人机需优先探测的潜在感兴趣区域；低层路径规划器则采用整数规划方法，在满足物理约束的前提下协调机器人团队的路径规划。我们阐明了该方法的若干理论性质，包括任意时刻保证与任务完成时间。通过仿真实验验证了方法的有效性，并在移动机器人物理实验中进一步验证了相关结论。