Multi-robot active sensing of non-stationary Gaussian process-based environmental phenomena
Author(s)Ouyang, Ruofei; Low, Kian Hsiang; Chen, Jie; Jaillet, Patrick
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems (AAMAS '14)
Association for Computing Machinery (ACM)
Ruofei Ouyang, Kian Hsiang Low, Jie Chen, and Patrick Jaillet. 2014. Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems (AAMAS '14). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 573-580.
Author's final manuscript