Multi-Robot Multitask Gaussian Process Estimation and Coverage

2026-03-11

AI Summary

This study addresses the challenge of efficiently covering target regions with multiple robots in multi-task scenarios where sensing requirements are initially unknown. To tackle this problem, the authors propose a unified framework that employs federated multi-task coverage when sensing demands are known, and integrates online multi-task Gaussian process regression to learn the unknown demand functions and enable adaptive coverage control when they are not. The main contributions include formalizing a novel multi-task coverage problem, introducing a regret metric tailored to multi-task coverage, and proving that the proposed adaptive algorithm achieves sublinear cumulative regret. Theoretical analysis establishes the convergence of the algorithm, while numerical experiments demonstrate its effectiveness and superiority in multi-robot, multi-task coverage settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• multi-robot
• multitask coverage
• sensory demands
• Gaussian Process
• coverage control

Innovation

• multitask coverage
• Gaussian Process
• federated algorithm
• coverage regret
• adaptive coverage