Long-Term Autonomy for AUVs Operating Under Uncertainties in Dynamic Marine Environments

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 Cost-Constrained Partially Observable Monte-Carlo Planner (POMCP) do not account for energy efficiency. Therefore, we present a scalable Energy Cost-Constrained POMCP algorithm utilizing the predicted ocean dynamics that optimizes energy and environment costs along with goal-driven rewards. A theoretical analysis, along with simulation and real-world experiment results is presented to validate the proposed methodology.

Automated summary

There is significant interest in using AUVs and ASVs for persistent ocean surveillance, with studies focusing on understanding physical phenomena and complex planning and navigation problems. A novel POMDP methodology is proposed to address uncertainties in motion, sensing, and environment, with a focus on incorporating energy efficiency and environmental costs optimization. The proposed Energy Cost-Constrained POMCP algorithm is scalable and aims to optimize energy and environment costs while achieving goal-driven rewards.