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 profi...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-10, Vol.6 (4), p.6313-6320
Main Authors: RedwanNewaz, Abdullah Al, Alam, Tauhidul, Murad Reis, Gregory, Bobadilla, Leonardo, Smith, Ryan N.
Format: Article
Language:English
Subjects:
Algorithms
autonomous underwater vehicle navigation
Autonomous underwater vehicles
Autonomy
Constraints
Cost analysis
energy constraints
Energy costs
Long-term autonomy
Marine environment
Markov processes
Monte Carlo simulation
Navigation
Ocean currents
Ocean dynamics
Oceans
Planning
Robot sensing systems
Sensors
Surface vehicles
uncertainties
Uncertainty
Vehicle dynamics
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3091697