Multi-Robot Multitask Gaussian Process Estimation and Coverage

TL;DR

This paper presents a novel multitask coverage control framework for multi-robot systems, integrating Gaussian Process learning for unknown sensory demands and establishing theoretical regret bounds.

Contribution

It introduces a new multitask coverage problem, develops algorithms for known and unknown demands, and proves sublinear regret for the adaptive approach.

Findings

The federated multitask coverage algorithm converges reliably.

The adaptive algorithm achieves sublinear cumulative regret.

Numerical results demonstrate effective coverage performance.

Abstract

Coverage control is essential for the optimal deployment of agents to monitor or cover areas with sensory demands. While traditional coverage involves single-task robots, increasing autonomy now enables multitask operations. This paper introduces a novel multitask coverage problem and addresses it for both the cases of known and unknown sensory demands. For known demands, we design a federated multitask coverage algorithm and establish its convergence properties. For unknown demands, we employ a multitask Gaussian Process (GP) framework to learn sensory demand functions and integrate it with the multitask coverage algorithm to develop an adaptive algorithm. We introduce a novel notion of multitask coverage regret that compares the performance of the adaptive algorithm against an oracle with prior knowledge of the demand functions. We establish that our algorithm achieves sublinear cumulative regret, and numerically illustrate its performance.