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.

This content is only available via PDF.
You do not currently have access to this content.