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.
@article{arxiv.2511.02398,
title = {A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control},
author = {Gennaro Guidone and Luca Monegaglia and Elia Raimondi and Han Wang and Mattia Bianchi and Florian Dörfler},
journal= {arXiv preprint arXiv:2511.02398},
year = {2026}
}