Abstract
The Regional Ocean Model System (ROMS) is a high dimensional computational model of ocean circulation. The model and data assimilation from various sources can provide a good estimate of ocean circulation variables, but not at a rate that is sufficient to track fast changes. For more frequent updates, we consider the use of an autonomous underwater vehicle (AUV) navigated along a maximally-informative path, i.e., one that maximally reduces uncertainty in ocean circulation variable estimations. The proposed solution deconstructs the problem into a long time-scale deterministic optimization problem for generating waypoints and a short time-scale stochastic optimal control problem for sequentially hitting these waypoints while taking into account the uncertainty of ocean currents. The latter is solved as a feedback control problem that is based on the stochastic Hamilton-Jacobi-Bellman equation and a locally consistent Markov chain approximation. Our results are illustrated by an example using data from the ROMS data assimilation.
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