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.
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. Abstract The purpose of this study is to find a combination of optimal numerical algorithms for time-stepping and mode-splitting suitable for a high-resolution, free-surface, terrain-following coordinate oceanic model. Due to mathematical feedback between the baroclinic momentum and tracer equations and, similarly, between the barotropic momentum and continuity equations, it is advantageous to treat both modes so that, after a time step for the momentum equation, the computed velocities participate immediately in the computation of tracers and continuity, and vice versa, rather than advancing all equations for one time step simultaneously. This leads to a new family of time-stepping algorithms that combine forward–backward feedback with the best known synchronous algorithms, allowing an increased time step due to the enhanced internal stability without sacrificing its accuracy. Based on these algorithms we design a split-explicit hydrodynamic kernel for a realistic oceanic model, which addresses multiple numerical issues associated with mode splitting. This kernel utilizes consistent temporal averaging of the barotropic mode via a specially designed filter function to guarantee both exact conservation and constancy preservation properties for tracers and yields more accurate (up to second-order), resolved barotropic processes, while preventing aliasing of unresolved barotropic signals into the slow baroclinic motions. It has a more accurate mode-splitting due to redefined barotropic pressure-gradient terms to account for the local variations in density field, while maintaining the computational efficiency of a split model. It is naturally compatible with a variety of centered and upstream-biased high-order advection algorithms, and helps to mitigate computational cost of expensive physical parameterization of mixing processes and submodels. 