Collaborative Autonomous Surveys in Marine Environments Affected by Oil Spills

  • Shayok Mukhopadhyay
  • Chuanfeng Wang
  • Mark Patterson
  • Michael Malisoff
  • Fumin Zhang
Part of the Studies in Computational Intelligence book series (SCI, volume 554)


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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  1. 1.
    Geen, M.: Advances in Marine Survey Products and Platforms. In: OCEANS 2007-Europe, vol. 1-3, pp. 984–989 (2007)Google Scholar
  2. 2.
    Hausler, A., Ghabcheloo, R., Kaminer, I., Pascoal, A., Aguiar, A.: Path Planning For Multiple Marine Vehicles. In: OCEANS 2009-Europe, vol. 1 & 2, pp. 423–431 (2009)Google Scholar
  3. 3.
    Pettersen, K., Egeland, O.: Exponential Stabilization of An Underactuated Surface Vessel. In: Proceedings of the 35th IEEE Conference on Decision and Control, vol. 1, pp. 967–972 (1996)Google Scholar
  4. 4.
    Pettersen, K., Lefeber, E.: Way-Point Tracking Control Of Ships. In: Proceedings of the 40th IEEE Conference on Decision and Control, vol. 1, pp. 940–945 (2001)Google Scholar
  5. 5.
    Do, K., Jiang, Z., Pan, J.: Universal Controllers for Stabilization and Tracking of Underactuated Ships. Systems & Control Letters 47(4), 299–317 (2002)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Ghommam, J., Mnif, F., Benali, A., Derbel, N.: Nonsingular Serret-Frenet Based Path Following Control for an Underactuated Surface Vessel. Journal of Dynamic Systems, Measurement, and Control 131(2), 021006(8 pages) (2009)Google Scholar
  7. 7.
    Xiang, X., Lapierre, L., Liu, C., Jouvencel, B.: Path Tracking: Combined Path Following and Trajectory Tracking for Autonomous Underwater Vehicles. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp. 3558–3563 (2011)Google Scholar
  8. 8.
    Do, K., Pan, J.: Robust Path Following of Underactuated Ships Using Serret-Frenet Frame. In: Proceedings of the American Control Conference, vol. 3, pp. 2000–2005 (2003)Google Scholar
  9. 9.
    Malisoff, M., Mazenc, F., Zhang, F.: Stability and Robustness Analysis for Curve Tracking Control Using Input-to-State Stability. IEEE Transactions on Automatic Control 57(5), 1320–1326 (2012)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Pettersen, K., Fossen, T.: Underactuated Dynamic Positioning of A Ship-Experimental Results. IEEE Transactions on Control Systems Technology 8(5), 856–863 (2000)CrossRefGoogle Scholar
  11. 11.
    Zhang, F., Justh, E., Krishnaprasad, P.S.: Boundary Following Using Gyroscopic Control. In: Proceedings of the 43rd IEEE Conference on Decision and Control, vol. 5, pp. 5204–5209 (2004)Google Scholar
  12. 12.
    Zhang, F., O’Connor, A., Luebke, D., Krishnaprasad, P.S.: Experimental Study of Curvature-Based Control Laws for Obstacle Avoidance. In: Proceedings of 2004 IEEE International Conf. on Robotics and Automation, vol. 4, pp. 3849–3854 (2004)Google Scholar
  13. 13.
    Kim, J., Zhang, F., Egerstedt, M.: Curve Tracking Control for Autonomous Vehicles With Rigidly Mounted Range Sensors. Journal of Intelligent and Robotic Systems 56(1-2), 177–197 (2009)CrossRefMATHGoogle Scholar
  14. 14.
    Zhang, F., Fratantoni, D.M., Paley, D., Lund, J., Leonard, N.E.: Control of Coordinated Patterns for Ocean Sampling. International Journal of Control 80(7), 1186–1199 (2007)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Wu, W., Zhang, F.: Robust Cooperative Exploration With A Switching Strategy. IEEE Transactions on Robotics 28(4), 828–839 (2012)CrossRefGoogle Scholar
  16. 16.
    Wu, W., Zhang, F.: Cooperative Exploration of Level Surfaces of Three Dimensional Scalar Fields. Automatica, the IFAC Journal 47(9), 2044–2051 (2011)CrossRefMATHGoogle Scholar
  17. 17.
    Dasgupta, P.: A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 38(3), 549–563 (2008)CrossRefGoogle Scholar
  18. 18.
    Boardman, M., Edmonds, J., Francis, K., Clark, C.: Multi-Robot Boundary Tracking With Phase and Workload Balancing. In: Proc. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3321–3326 (2010)Google Scholar
  19. 19.
    Jin, X., Ray, A.: Coverage Control of Autonomous Vehicles for Oil Spill Cleaning in Dynamic and Uncertain Environments. In: Proc. 2013 American Control Conference (ACC), pp. 2594–2599 (2013)Google Scholar
  20. 20.
    Johnson, B., Hallin, N., Leidenfrost, H., O’Rourke, M., Edwards, D.: Collaborative Mapping With Autonomous Underwater Vehicles in Low-Bandwidth Conditions. In: OCEANS 2009 - EUROPE, pp. 1–7 (2009)Google Scholar
  21. 21.
    Carlési, N., Michel, F., Jouvencel, B., Ferber, J.: Generic Architecture For Multi-AUV Cooperation Based on A Multi-Agent Reactive Organizational Approach. In: Proc. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5041–5047 (2011)Google Scholar
  22. 22.
    Li, H., Popa, A., Thibault, C., Trentini, M., Seto, M.: A Software Framework for Multi-Agent Control of Multiple Autonomous Underwater Vehicles for Underwater Mine Counter-Measures. In: Proc. 2010 International Conference on Autonomous and Intelligent Systems (AIS), pp. 1–6 (2010)Google Scholar
  23. 23.
    Gustavi, T., Dimarogonas, D.V., Egerstedt, M., Hu, X.: Sufficient Conditions for Connectivity Maintenance and Rendezvous in Leader-Follower Networks. Automatica 46(1), 133–139 (2010)CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    Mukhopadhyay, S., Wang, C., Bradshaw, S., Maxon, S., Patterson, M., Zhang, F.: Controller Performance of Marine Robots In Reminiscent Oil Surveys. In: Proc. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamoura, Portugal, pp. 1766–1771 (2012)Google Scholar
  25. 25.
    Liang, X., Wu, W., Chang, D., Zhang, F.: Real-Time Modelling of Tidal Current for Navigating Underwater Glider Sensing Networks. Procedia Computer Science 10, 1121–1126 (2012)CrossRefGoogle Scholar
  26. 26.
    Patterson, M.R., Sias, J.H.: Modular Autonomous Underwater Vehicle System. U.S. Patent 5, 995, 882 (1999)Google Scholar
  27. 27.
    Patterson, M.R., Sias, J.H.: Fetch!® Commercial Autonomous Underwater Vehicle: A Modular, Platform-Independent Architecture Using Desktop Personal Computer Technology. In: Ocean Community Conference 1998 Proceedings, Baltimore, MD, vol. 2, pp. 891–897 (1998)Google Scholar
  28. 28.
    Patterson, M.R.: A Finite State Machine Approach to Layered Command And Control of Autonomous Underwater Vehicles Implemented in G, A Graphical Programming Language. In: Ocean Community Conference 1998 Proceedings, Baltimore, MD, vol. 2, pp. 745–751 (1998)Google Scholar
  29. 29.
    Malisoff, M., Zhang, F.: Adaptive Control for Planar Curve Tracking Under Controller Uncertainty. Automatica 49(5), 1411–1418 (2013)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Malisoff, M., Zhang, F.: Robustness of A Class of Three-Dimensional Curve Tracking Control Laws Under Time Delays and Polygonal State Constraints. In: Proc. 2013 American Control Conference (ACC 2013), Washington D.C., USA, pp. 5710–5715 (2013)Google Scholar
  31. 31.
    Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning. MIT Press (2006)Google Scholar
  32. 32.
    Stachniss, C., Plagemann, C., Lilienthal, A.: Gas Distribution Modeling Using Sparse Gaussian Process Mixtures. Autonomous Robots 26(2-3), 187–202 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Shayok Mukhopadhyay
    • 1
  • Chuanfeng Wang
    • 2
  • Mark Patterson
    • 3
  • Michael Malisoff
    • 4
  • Fumin Zhang
    • 1
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Marine Science CenterNortheastern UniversityNahantUSA
  4. 4.Department of MathematicsLouisiana State UniversityBaton RougeUSA

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