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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.02398 |
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| _version_ | 1866910265517277184 |
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| author | Guidone, Gennaro Monegaglia, Luca Raimondi, Elia Wang, Han Bianchi, Mattia Dörfler, Florian |
| author_facet | Guidone, Gennaro Monegaglia, Luca Raimondi, Elia Wang, Han Bianchi, Mattia Dörfler, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control Guidone, Gennaro Monegaglia, Luca Raimondi, Elia Wang, Han Bianchi, Mattia Dörfler, Florian Machine Learning 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. |
| title | A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.02398 |