Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes

2025-09-09

AI Summary

This paper addresses the problem of online learning and cooperative coverage for multi-robot systems operating in unknown, time-varying density fields. We propose an incremental Bayesian inference framework based on Online Random Feature Gaussian Processes (O-RFGP), integrated with an Upper Confidence Bound (UCB) active sampling strategy and a Voronoi partitioning control mechanism to jointly optimize spatial field modeling and coverage performance. Theoretical analysis establishes asymptotic convergence of the estimation error. Extensive simulations demonstrate the method’s efficacy in both static and dynamic density fields, achieving rapid model acquisition and high-coverage efficiency. Real-world experiments on physical robot platforms further validate its practical deployability. The key contribution lies in the first integration of O-RFGP into a distributed Voronoi control architecture, enabling computationally scalable, theoretically guaranteed online field learning and adaptive coverage coordination.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper proposes a framework for multi-robot systems to perform simultaneous learning and coverage of the domain of interest characterized by an unknown and potentially time-varying density function. To overcome the limitations of Gaussian Process (GP) regression, we employ Random Feature GP (RFGP) and its online variant (O-RFGP) that enables online and incremental inference. By integrating these with Voronoi-based coverage control and Upper Confidence Bound (UCB) sampling strategy, a team of robots can adaptively focus on important regions while refining the learned spatial field for efficient coverage. Under mild assumptions, we provide theoretical guarantees and evaluate the framework through simulations in time-invariant scenarios. Furthermore, its effectiveness in time-varying settings is demonstrated through additional simulations and a physical experiment.

Problem

• Multi-robot learning and coverage of unknown spatial fields
• Online inference for time-varying density functions
• Adaptive sampling with theoretical performance guarantees

Innovation

• Random-Feature Gaussian Processes for online learning
• Voronoi-based coverage control for adaptive focus
• Upper Confidence Bound sampling for efficient coverage