Informative Planning and Online Learning with Sparse Gaussian Processes
This addresses the problem of efficient and adaptive ocean monitoring for autonomous systems, though it appears incremental as it builds on existing sparse Gaussian Process and planning techniques.
The paper tackles the challenge of persistent environmental monitoring with spatiotemporal variation by developing a planning and learning method for autonomous marine vehicles, resulting in accurate and efficient performance in simulations with ground-truth ocean data.
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.