Advertisement

Simultaneous Tracking and Sampling of Dynamic Oceanographic Features with Autonomous Underwater Vehicles and Lagrangian Drifters

  • Jnaneshwar DasEmail author
  • Frédéric Py
  • Thom Maughan
  • Tom O’Reilly
  • Monique Messié
  • John Ryan
  • Kanna Rajan
  • Gaurav S. Sukhatme
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Introduction

Studying ocean processes often requires observations made in a Lagrangian frame of reference, that is, a frame of reference moving with a feature of interest [1]. Often, the only way to understand a process is to acquire measurements at sufficient spatial and temporal resolution within a specific feature while it is evolving. Examples of coastal ocean features whose study requires Lagrangian observations include concentrated patches of microscopic algae (Fig. 1) that are toxic and may have impacts on fisheries, marine life and humans, or a patch of low-oxygen water that may cause marine life mortality depending on its movement and mixing.

Keywords

Pitch Angle Autonomous Underwater Vehicle Regional Ocean Modeling System Drifter Speed Patch Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Davis, R.E.: Lagrangian ocean studies. Annual Review of Fluid Mechanics 23(1), 43–64 (1991)CrossRefGoogle Scholar
  2. 2.
    CANON: Controlled, Agile and Novel Observing Network, http://www.mbari.org/canon/
  3. 3.
    Lumpkin, R., Pazos, M.: Measuring surface currents with surface velocity program drifters: the instruments, its data and some recent results. In: Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics. Cambridge University Press (2006)Google Scholar
  4. 4.
    Fiorelli, E., Leonard, N.E., Member, S., Bhatta, P., Paley, D.A., Member, S., Bachmayer, R., Fratantoni, D.M.: Multi-auv control and adaptive sampling in Monterey Bay. IEEE Journal of Oceanic Engineering, 935–948 (2004)Google Scholar
  5. 5.
    Smith, R.N., Pereira, A.A., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Autonomous underwater vehicle trajectory design coupled with predictive ocean models: A case study. In: IEEE International Conference on Robotics and Automation, pp. 4770–4777 (2010)Google Scholar
  6. 6.
    Franchi, A., Stegagno, P., Rocco, M.D.D., Oriolo, G.: Distributed target localization and encircling with a multi-robot system. In: Proceedings of the 7th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2010) (2010)Google Scholar
  7. 7.
    McGann, C., Py, F., Rajan, K., Ryan, J.P., Henthorn, R.: Adaptive Control for Autonomous Underwater Vehicles. In: Assoc. for the Advancement of Artificial Intelligence, National Conference (AAAI), Chicago, IL (2008)Google Scholar
  8. 8.
    McGann, C., Py, F., Rajan, K., Ryan, J.P., Thomas, H., Henthorn, R., McEwen, R.: Preliminary Results for Model-Based Adaptive Control of an Autonomous Underwater Vehicle. In: Intnl. Symp. on Experimental Robotics (ISER), Athens (2008)Google Scholar
  9. 9.
    Py, F., Rajan, K., McGann, C.: A Systematic Agent Framework for Situated Autonomous Systems. In: 9th International Conf. on Autonomous Agents and Multiagent Systems, Toronto, Canada (May 2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Jnaneshwar Das
    • 1
    Email author
  • Frédéric Py
    • 2
  • Thom Maughan
    • 2
  • Tom O’Reilly
    • 2
  • Monique Messié
    • 2
  • John Ryan
    • 2
  • Kanna Rajan
    • 2
  • Gaurav S. Sukhatme
    • 1
  1. 1.Dept. of Computer ScienceUniv. of Southern CaliforniaLos AngelesUSA
  2. 2.Monterey Bay Aquarium Research InstituteMoss LandingUSA

Personalised recommendations