A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.
We aim at estimating the current state of the environment and providing a nowcast (not forecast or hindcast) of the environment, via navigating the robots to collect the information. To model spatial phenomena, a common approach is to use a rich class of Gaussian Processes @cite_2 @cite_7 @cite_5 in spatial statistics. In this work, we also employ this broadly-adopted approach to build and learn an underlying model of interest.
@cite_5 @cite_7 @cite_2
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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. In many sensing applications, including environmental monitoring, measurement systems must cover a large space with only limited sensing resources. One approach to achieve required sensing coverage is to use robots to convey sensors within this space. Planning the motion of these robots - coordinating their paths in order to maximize the amount of information collected while placing bounds on their resources (e.g., path length or energy capacity) - is aNP-hard problem. In this paper, we present an efficient path planning algorithm that coordinates multiple robots, each having a resource constraint, to maximize the "informativeness" of their visited locations. In particular, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to characterize the amount of information collected. We provide strong theoretical approximation guarantees for our algorithm by exploiting the submodularity property of mutual information. In addition, we improve the efficiency of our approach by extending the algorithm using branch and bound and a region-based decomposition of the space. We provide an extensive empirical analysis of our algorithm, comparing with existing heuristics on datasets from several real world sensing applications.