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arXiv · 2511.02398

A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control

Gennaro Guidone·Luca Monegaglia·Elia Raimondi·Han Wang·Mattia Bianchi·Florian Dörfler
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Abstract

We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off between exploration and exploitation, each agent autonomously determines its trajectory by minimizing a local cost function. Inspired by the GP-UCB (Upper Confidence Bound for GPs) acquisition function, the proposed cost combines the expected locational cost with a variance-based exploration term, guiding agents toward regions that are both high in predicted density and model uncertainty. Compared to previous work, our algorithm operates in a fully decentralized fashion, relying only on local observations and communication with neighboring agents. In particular, agents periodically update their inducing points using a greedy selection strategy, enabling scalable online GP updates. We demonstrate the effectiveness of our algorithm in simulation.

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Gennaro Guidone, Luca Monegaglia, Elia Raimondi, Han Wang, Mattia Bianchi, Florian Dörfler. 2025-11-04. A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control. https://arxiv.org/abs/2511.02398

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