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There has been significant interest in recent years in the utility and implementation of autonomous underwater and surface vehicles (AUVs and ASVs) for persistent surveillance of the ocean. Example studies include the dynamics of physical phenomena, e.g., ocean fronts, temperature and salinity profiles, and the onset of harmful algae blooms. For these studies, AUVs are presented with a complex planning and navigation problem to achieve autonomy lasting days and weeks under uncertainties while dealing with resource constraints. We address these issues by adopting motion, sensing, and environment uncertainties via a Partially Observable Markov Decision Process (POMDP) framework. We propose a methodology with a novel extension of POMDPs to incorporate spatiotemporally-varying ocean currents as energy and dynamic obstacles as environment uncertainty. Existing POMDP solutions such as the …