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Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions

Smith, Ryan N., Das, Jnaneshwar, Chao, Yi, Caron, David A., Jones, Burton H., & Sukhatme, Gaurav S. (2010) Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions. In Proceedings of Oceans '10 - IEEE Sydney, Sydney, Australia, pp. 1-10.

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Abstract

In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.

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ID Code: 40122
Item Type: Conference Paper
Keywords: Autonomous Underwater Vehicle, Algal bloom, Ocean modeling, Path Planning, Multi-vehicle control
Subjects: Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Biological Oceanography (040501)
Australian and New Zealand Standard Research Classification > EARTH SCIENCES (040000) > OCEANOGRAPHY (040500) > Physical Oceanography (040503)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Ocean Engineering (091103)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2010 (please consult the authors).
Deposited On: 28 Mar 2011 11:55
Last Modified: 31 Mar 2011 12:32

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