A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control

• URL: http://arxiv.org/abs/2511.02398v1
• Date: Tue, 04 Nov 2025 09:23:19 GMT
• Title: A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control
• Authors: Gennaro Guidone, Luca Monegaglia, Elia Raimondi, Han Wang, Mattia Bianchi, Florian Dörfler,
• Abstract summary: We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs)<n>To trade-off between exploration and exploitation, each agent autonomously determines its trajectory by minimizing a local cost function.<n>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.
• Score: 5.580557800052841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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