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Glider CT: reconstructing flow fields from predicted motion of underwater gliders

Published: 11 November 2013 Publication History

Abstract

The motion of underwater gliders is strongly affected by ocean currents, which would benefit from a precise map of flow velocity with fine resolution. In this paper, we propose a novel method for reconstructing depth-averaged flow fields leveraging a motion prediction system. The method is based on the fact that the difference between the actual GPS location where a glider comes to the surface and the predicted surfacing position is determined by a line integral of depth-averaged ocean currents along the underwater trajectory of the glider. Even though the underwater trajectories of a glider can not be directly observed, the motion prediction system can be employed to estimate such trajectories. When multiple gliders are involved, we are able to formulate the problem of reconstructing a depth-averaged flow field as a nonlinear estimation problem that has strong connections with algorithms in computerized tomography (CT). However, extensions to CT algorithms are necessary due to the nonlinear nature and practical constraints of the flow reconstruction problem. As a first step towards solving this problem, this paper develops iterative algorithms with demonstrated success in simulations.

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Cited By

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  • (2022)Motion tomography via occupation kernelsJournal of Computational Dynamics10.3934/jcd.20210269:1(27)Online publication date: 2022
  • (2020)Regional Ocean Current Field Construction based on an Empirical Bayesian Kriging Algorithm using Multiple Underwater GlidersJournal of Coastal Research10.2112/SI99-006.199:sp1(41)Online publication date: 1-Mar-2020
  • (2019)Distributed Motion Tomography for Reconstruction of Flow Fields*2019 International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2019.8793797(8048-8054)Online publication date: 20-May-2019
  • Show More Cited By

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    cover image ACM Conferences
    WUWNet '13: Proceedings of the 8th International Conference on Underwater Networks & Systems
    November 2013
    374 pages
    ISBN:9781450325844
    DOI:10.1145/2532378
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 11 November 2013

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    Author Tags

    1. flow fields reconstruction
    2. underwater gliders

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    WUWNET '13
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    WUWNET '13: Conference on Underwater Networks and Systems
    November 11 - 13, 2013
    Kaohsiung, Taiwan

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    WUWNet '13 Paper Acceptance Rate 11 of 55 submissions, 20%;
    Overall Acceptance Rate 84 of 180 submissions, 47%

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    Cited By

    View all
    • (2022)Motion tomography via occupation kernelsJournal of Computational Dynamics10.3934/jcd.20210269:1(27)Online publication date: 2022
    • (2020)Regional Ocean Current Field Construction based on an Empirical Bayesian Kriging Algorithm using Multiple Underwater GlidersJournal of Coastal Research10.2112/SI99-006.199:sp1(41)Online publication date: 1-Mar-2020
    • (2019)Distributed Motion Tomography for Reconstruction of Flow Fields*2019 International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2019.8793797(8048-8054)Online publication date: 20-May-2019
    • (2019)A data assimilation framework for data-driven flow models enabled by motion tomographyInternational Journal of Intelligent Robotics and Applications10.1007/s41315-019-00092-53:2(158-177)Online publication date: 12-Jun-2019
    • (2018)An Improved Algorithm for Motion Tomography by Incorporating Vehicle Travel Time2018 Annual American Control Conference (ACC)10.23919/ACC.2018.8431028(1907-1912)Online publication date: Jun-2018
    • (2017)Motion tomographyInternational Journal of Robotics Research10.1177/027836491769874736:3(320-336)Online publication date: 1-Mar-2017
    • (2016)An adaptive control law for controlled Lagrangian particle trackingProceedings of the 11th International Conference on Underwater Networks & Systems10.1145/2999504.3001077(1-5)Online publication date: 24-Oct-2016
    • (2016)Distributed motion tomography for time-varying flow fieldsOCEANS 2016 - Shanghai10.1109/OCEANSAP.2016.7485540(1-7)Online publication date: Apr-2016
    • (2016)Adaptive Learning for Controlled Lagrangian Particle TrackingOCEANS 2016 MTS/IEEE Monterey10.1109/OCEANS.2016.7761393(1-6)Online publication date: Sep-2016
    • (2016)Resolving Temporal Variations in Data-Driven Flow Models Constructed by Motion Tomography**The research work is supported by ONR grants N00014-10-10712 (YIP) and N00014-14-1-0635; and NSF grants OCE-1032285, IIS-1319874, and CMMI-1436284.IFAC-PapersOnLine10.1016/j.ifacol.2016.10.16049:18(182-187)Online publication date: 2016
    • Show More Cited By

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