Multi-robot active sensing of non-stationary Gaussian process-based environmental phenomena
| Autor: | Ouyang, Ruofei, Low, Kian Hsiang, Chen, Jie, Jaillet, Patrick |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Jaillet, Patrick |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Zdroj: | MIT web domain |
| Popis: | A key challenge of environmental sensing and monitoring is that of sensing, modeling, and predicting large-scale, spatially correlated environmental phenomena, especially when they are unknown and non-stationary. This paper presents a decentralized multi-robot active sensing (DEC-MAS) algorithm that can efficiently coordinate the exploration of multiple robots to gather the most informative observations for predicting an unknown, non-stationary phenomenon. By modeling the phenomenon using a Dirichlet process mixture of Gaussian processes (DPM-GPs), our work here is novel in demonstrating how DPM-GPs and its structural properties can be exploited to (a) formalize an active sensing criterion that trades off between gathering the most informative observations for estimating the unknown, non-stationary spatial correlation structure vs. that for predicting the phenomenon given the current, imprecise estimate of the correlation structure, and (b) support efficient decentralized coordination. We also provide a theoretical performance guarantee for DEC-MAS and analyze its time complexity. We empirically demonstrate using two real-world datasets that DEC-MAS outperforms state-of-the-art MAS algorithms. Singapore-MIT Alliance for Research and Technology (Subaward Agreement 41 R-252-000-527-592) Singapore-MIT Alliance for Research and Technology (Subaward Agreement 47 R-252-000-509-592) |
| Databáze: | OpenAIRE |
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