A Machine Learning Approach to Water Velocity Estimation for Better Navigation of Marine Gliders

Meghan Leone Stuart, Juan Jose Garcia,Sahin Olut,Taksir Hasan, Ahsan Mahmood,Kipp Williams,Parasara Sridhar Duggirala

OCEANS 2021: San Diego – Porto(2021)

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Marine gliders are autonomous underwater vehicles used for data collection by ocean and climate scientists. To conserve energy on months-long trips these gliders use a propulsion system driven mostly by gravity. This variable buoyancy propulsion system, however, is particularly susceptible to error introduced by strong or chaotic ocean currents. For this reason, most navigation systems rely on prediction of local water velocity to plan their paths. Current physics-based simulation models are limited by on-board computation resources. Consequently, we introduce several variations of recurrent neural network based prediction algorithms and compare their performance to a commonly used statistical forecasting method.
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Key words
autonomous underwater vehicles,data collection,climate scientists,variable buoyancy propulsion system,chaotic ocean currents,navigation systems,physics-based simulation models,water velocity estimation,marine gliders,recurrent neural network,on-board computation resources
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