Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Energy-Constrained Mobile Robots
For robotic monitoring applications like search-and-rescue and precision agriculture, this work provides a data-driven strategy that handles noise and energy constraints, though it is an incremental combination of existing techniques.
This paper addresses spatial classification for monitoring with energy-constrained mobile robots, proposing a bi-level approach using multi-armed bandits for high-level planning and integer programming for low-level path planning. The method achieves anytime guarantees and reduced task completion time, validated in simulations and physical experiments.
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.