Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Energy-Constrained Mobile Robots

2025-01-14

AI Summary

This work addresses the problem of energy-constrained, multi-robot collaborative environmental monitoring for applications such as search-and-rescue and precision agriculture, focusing on spatial classification (interest vs. non-interest regions) and collision-free motion planning. We propose a two-layer adaptive framework: (i) an upper layer employing a multi-armed bandit (MAB) model for data-driven, online selection of interest regions under sensor noise; and (ii) a lower layer formulating an energy-aware integer linear program (ILP) to jointly optimize path planning and task scheduling, guaranteeing both a theoretical performance lower bound and bounded mission completion time. Evaluated in simulation and on real robotic platforms, our approach achieves significantly higher classification accuracy and speed compared to baseline methods, reduces mission completion time by 37%, and strictly satisfies energy budgets and collision-avoidance constraints throughout execution.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 target 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.

Problem

• Energy-efficient operation
• Environmental monitoring
• Collision avoidance

Innovation

• Mobile Robotics
• Energy-efficient Monitoring
• Real-time Strategy Adjustment