Adaptive Path Planning for Tracking Ocean Fronts with an Autonomous Underwater Vehicle

  • Ryan N. Smith
  • Philip Cooksey
  • Frederic Py
  • Gaurav S. Sukhatme
  • Kanna Rajan
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109)


Ocean fronts are productivity hot spots, supporting marine life from plankton to whales. These dynamic systems contain a vast amount of information, and have the potential to significantly expand our knowledge of aquatic ecosystems in relation to climate change. However, ocean fronts and other dynamic features cannot be studied through conventional oceanographic techniques. In this paper, we address the problem of sampling and tracking an ocean front with an Autonomous Underwater Vehicle based on predictions and/or priors provided by a heterogeneous team of assets and ocean models. Specifically, given a prior (that may not be accurate or up-to-date) we present a method for an underwater vehicle to plan a mission and adapt this mission on-the-fly to track a dynamic feature. Results from field trials are presented, and demonstrate that the vehicle is able to adapt its path to follow a desired contour.


  1. 1.
    Olson, D., Hitchcock, G., Mariano, A., Ashjan, C., Peng, G., Nero, R., Podesta, G.: Life on the edge: marine life and fronts. Oceanography 7, 52–60 (1994)CrossRefGoogle Scholar
  2. 2.
    Reed, B., Hover, F.: Tracking ocean fronts with multiple vehicles and mixed communication losses. In: International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ, pp. 3374–3381 (2013)Google Scholar
  3. 3.
    Yuh, J.: Design and control of autonomous underwater robots: a survey. Auton. Robots 8, 7–24 (2000)CrossRefGoogle Scholar
  4. 4.
    Paley, D., Zhang, F., Leonard, N.: Cooperative control for ocean sampling: the glider coordinated control system. IEEE Trans. Control Syst. Technol. 16, 735–744 (2008)CrossRefGoogle Scholar
  5. 5.
    Smith, R.N., Schwager, M., Smith, S.L., Jones, B.H., Rus, D., Sukhatme, G.S.: Persistent ocean monitoring with underwater gliders: adapting sampling resolution. J. Field Robot. 28, 714–741 (2011)CrossRefGoogle Scholar
  6. 6.
    Smith, R.N., Das, J., Heidarsson, H., Pereira, A., Cetinić, I., Darjany, L., Ève Garneau, M., Howard, M.D., Oberg, C., Ragan, M., Schnetzer, A., Seubert, E., Smith, E.C., Stauffer, B.A., Toro-Farmer, G., Caron, D.A., Jones, B.H., Sukhatme, G.S.: USC CINAPS builds bridges: observing and monitoring the southern California bight. IEEE Robot. Autom. Mag. Spec. Issue Mar. Robot. Syst. 17, 20–30 (2010)Google Scholar
  7. 7.
    Smith, R.N., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Robot. Res. 29, 1475–1497 (2010)CrossRefGoogle Scholar
  8. 8.
    Smith, R.N., Das, J., Chao, Y., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Cooperative multi-auv tracking of phytoplankton blooms based on ocean model predictions. In: Proceedings of Oceans ’10—IEEE Sydney, Sydney, Australia, pp. 1–10 (2010)Google Scholar
  9. 9.
    Creed, E.L., Mudgal, C., Glenn, S., Schofield, O., Jones, C., Webb, D.C.: Using a fleet of slocum battery gliders in a regional scale coastal ocean observatory. In: Oceans ’02 MTS/IEEE (2002)Google Scholar
  10. 10.
    Garcia-Olaya, A., Py, F., Das, J., Rajan, K.: An online utility-based approach for sampling dynamic ocean fields. IEEE J. Oceanic Eng. 37, 185–203 (2012)CrossRefGoogle Scholar
  11. 11.
    Singh, H., Yoerger, D., Bradley, A.: Issues in auv design and deployment for oceanographic research. In: Proceedings of the 1997 IEEE International Conference on Robotics and Automation, vol. 3, pp. 1857–1862 (1997) (Invited paper)Google Scholar
  12. 12.
    Fiorelli, E., Leonard, N., Bhatta, P., Paley, D., Bachmayer, R., Fratantoni, D.: Multi-auv control and adaptive sampling in monterey bay. IEEE J. Oceanic Eng. 31, 935–948 (2006)CrossRefGoogle Scholar
  13. 13.
    Zhang, Y., Godin, M., Bellingham, J., Ryan, J.: Ocean front detection and tracking by an autonomous underwater vehicle. In: MTS/IEEE Oceans, pp. 1–4 (2011)Google Scholar
  14. 14.
    Zhang, Y., Godin, M.A., Bellingham, J.G., Ryan, J.P.: Using an autonomous underwater vehicle to track a coastal upwelling front. IEEE J. Oceanic Eng. 37, 338–347 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Bellingham, J., Ryan, J., Kieft, B., Stanway, M.: Two-dimensional mapping and tracking of a coastal upwelling front by an autonomous underwater vehicle. In: MTS/IEEE Oceans—San Diego, pp. 1–4 (2013)Google Scholar
  16. 16.
    Belkin, I.M., O’Reilly, J.E.: An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. J. Mar. Syst. 78, 319–326 (2009) (Special Issue on Observational Studies of Oceanic Fronts)Google Scholar
  17. 17.
    Belkin, I.M., Cornillon, P.C., Sherman, K.: Fronts in large marine ecosystems. Prog. Oceanogr. 81, 223–236 (2009) (Comparative Marine Ecosystem Structure and Function: Descriptors and Characteristics)Google Scholar
  18. 18.
    Zhang, B., Sukhatme, G.S.: Adaptive sampling with multiple mobile robots. In: IEEE International Conference on Robotics and Automation (submitted) (2008)Google Scholar
  19. 19.
    Leonard, N.E., Paley, D.A., Davis, R.E., Fratantoni, D.M., Lekien, F., Zhang, F.: Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in monterey bay. J. Field Robot. 27, 718–740 (2010)CrossRefGoogle Scholar
  20. 20.
    Das, J., Maughan, T., McCann, M., Godin, M., O’Reilly, T., Messie, M., Bahr, F., Gomes, K., Py, F., Bellingham, J., Sukhatme, G., Rajan, K.: Towards mixed-initiative, multi-robot field experiments: design, deployment, and lessons learned. In: Proceedings of the Intelligent Robots and Systems (IROS) Conference, San Francisco (2011)Google Scholar
  21. 21.
    Gomes, K., Cline, D., Edgington, D., Godin, M., Maughan, T., McCann, M., O’Reilly, T., Bahr, F., Chavez, F., Messi, M., Das, J., Rajan, K.: ODSS: a decision support system for ocean exploration. In: Workshop on Data-Driven Decision Guidance and Support Systems (DGSS) at the 29th IEEE International Conference on Data Engineering, Brisbane, Australia (2013)Google Scholar
  22. 22.
    YSI Incorporated: YSI EcoMapper. (2014). Accessed May 2014
  23. 23.
    Willow garage: robot operating system (ROS). (2007)
  24. 24.
    Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: Proceedings of the Open-Source Software Workshop of the International Conference on Robotics and Automation (2009)Google Scholar
  25. 25.
    Rajan, K., Py, F.: T-REX: Partitioned inference for AUV mission control. In: Roberts, G.N., Sutton, R. (eds.) Further Advances in Unmanned Marine Vehicles. The Institution of Engineering and Technology (IET) (2012)Google Scholar
  26. 26.
    Rajan, K., Py, F., Barreiro, J.: Towards deliberative control in marine robotics. In: Seto, M. (ed) Autonomy in Marine Robots. Springer, New York (2012)Google Scholar
  27. 27.
    Smith, R.N., Kelly, J., Chao, Y., Jones, B.H., Sukhatme, G.S.: Towards improvement of autonomous glider navigation accuracy through the use of regional ocean models. In: Proceedings of the 29th International Conference on Offshore Mechanics and Arctic Engineering, pp. 597–606, Shanghai, China (2010)Google Scholar
  28. 28.
    Smith, R.N., Kelly, J., Sukhatme, G.S.: Towards improving mission execution for autonomous gliders with an ocean model and kalman filter. In: Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ryan N. Smith
    • 1
  • Philip Cooksey
    • 2
  • Frederic Py
    • 3
  • Gaurav S. Sukhatme
    • 4
  • Kanna Rajan
    • 3
  1. 1.Physics and Engineering DepartmentFort Lewis CollegeDurangoUSA
  2. 2.California State University, Monterey BayMontereyUSA
  3. 3.Monterey Bay Aquarium Research InstituteMoss LandingUSA
  4. 4.Robotic Embedded Systems Laboratory and the Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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