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Collaborative Autonomous Surveys in Marine Environments Affected by Oil Spills

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Cooperative Robots and Sensor Networks 2014

Part of the book series: Studies in Computational Intelligence ((SCI,volume 554))

Abstract

This chapter presents results on collaborative autonomous surveys using a fleet of heterogeneous autonomous robotic vehicles in marine environments affected by oil spills. The methods used for the surveys are based on a class of path following controllers with mathematically proven convergence and robustness. Use of such controllers enables easy mission planning for autonomous marine surveys where the paths consist of lines and curves. The control algorithm uses simple dynamic models and simple control laws and thus enables quick deployment of a fleet of autonomous vehicles to collaboratively survey large areas. This enables using a mobile network to survey an area where the different member nodes may have slightly different capabilities. A mapping algorithm used to reconcile data from heterogeneous marine vehicles on multiple different paths is also presented. Vehicles with heterogeneous dynamics are thus used to aid in the reconstruction of a time varying field. The algorithms used were tested, mainly on student-built marine robots that collaboratively surveyed a coastal lagoon in Grand Isle, Louisiana that was polluted by crude oil during the Deepwater Horizon oil spill. The results obtained from these experiments show the effectiveness of the proposed methods for oil spill surveys and also provide guidance for mission designs for future collaborative autonomous environmental surveys.

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA

    Shayok Mukhopadhyay & Fumin Zhang

  2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA

    Chuanfeng Wang

  3. Marine Science Center, Northeastern University, 430 Nahant Road, Nahant, MA, 01908, USA

    Mark Patterson

  4. Department of Mathematics, Louisiana State University, Baton Rouge, LA, 70803, USA

    Michael Malisoff

Authors
  1. Shayok Mukhopadhyay
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  2. Chuanfeng Wang
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  3. Mark Patterson
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  4. Michael Malisoff
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  5. Fumin Zhang
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Corresponding author

Correspondence to Shayok Mukhopadhyay .

Editor information

Editors and Affiliations

  1. Prince Sultan University, CISTER Research Unit, Portugal, Riyadh, Saudi Arabia

    Anis Koubaa

  2. Dept. of Computer Science (Fachbereich Informatik), TU Darmstadt, Darmstadt, Germany

    Abdelmajid Khelil

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Mukhopadhyay, S., Wang, C., Patterson, M., Malisoff, M., Zhang, F. (2014). Collaborative Autonomous Surveys in Marine Environments Affected by Oil Spills. In: Koubaa, A., Khelil, A. (eds) Cooperative Robots and Sensor Networks 2014. Studies in Computational Intelligence, vol 554. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55029-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-55029-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55028-7

  • Online ISBN: 978-3-642-55029-4

  • eBook Packages: EngineeringEngineering (R0)

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