AbstractIn recent years, collecting scientific data from ocean environments has been increasingly undertaken by underwater gliders. For better navigation performance, the influence of flow on the navigation of underwater gliders may be significantly reduced by estimating flow velocity. However, methods for estimating flow do not always account for spatial and temporal changes in the flow field, leading to poor navigation in complex ocean environments. To improve navigation accuracy in such environmental conditions, this paper studies an approach for the real-time guidance of underwater gliders assisted by predictive ocean models. This study is motivated by glider deployments conducted from January to April 2012 and in February 2013 in Long Bay, South Carolina, where the ocean currents are characterized by strong tides and a stronger alongshore current, the Gulf Stream. The flow speed here often exceeds the forward speed of the glider. To deal with such a challenge, a computationally efficient method of depth-averaged ocean current modeling was developed. The method adjusts the ocean model based on the most recent ocean observations from gliders as feedback, and flow predictions from the model are incorporated into path planning, which produces waypoints. The entire process of flow prediction, path planning, and waypoint computation is performed off-board the gliders in real time by the glider navigation support system, the Glider-Environment Network Information System (GENIoS). This paper presents the setup and method for the glider navigation strategy applied to the Long Bay deployments. For demonstration, the performance of the method described here is compared to that of the default method implemented in the built-in glider navigation system.
Constrained enrichment contributes to hypoxia formation in Long Bay, South Carolina (USA), an open water urbanized coastline