Controlling buoyancy-driven profiling floats for applications in ocean observation

Smith, Ryan N. & Huynh, Van T. (2014) Controlling buoyancy-driven profiling floats for applications in ocean observation. IEEE Journal of Oceanic Engineering, 39(3), pp. 571-586.

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Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio–temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

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5 citations in Scopus
5 citations in Web of Science®
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ID Code: 61881
Item Type: Journal Article
Refereed: Yes
Keywords: Autonomous underwater vehicle (AUV), Model-predictive control (MPC), Ocean model, Path planning, Profiling float
DOI: 10.1109/JOE.2013.2261895
ISSN: 0364-9059
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 IEEE
Deposited On: 19 Aug 2013 22:02
Last Modified: 19 Jul 2017 02:01

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