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

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]  K. Rajan,et al.  An Online Utility-Based Approach for Sampling Dynamic Ocean Fields , 2012, IEEE Journal of Oceanic Engineering.

[2]  Naomi Ehrich Leonard,et al.  Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay , 2010, J. Field Robotics.

[3]  J. G. Bellingham,et al.  Using an Autonomous Underwater Vehicle to Track a Coastal Upwelling Front , 2012, IEEE Journal of Oceanic Engineering.

[4]  Gaurav S. Sukhatme,et al.  USC CINAPS Builds Bridges , 2010, IEEE Robotics & Automation Magazine.

[5]  Gaurav S. Sukhatme,et al.  USC CINAPS Builds bridges : observing and monitoring the southern california bight , 2010 .

[6]  Gaurav S. Sukhatme,et al.  Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions , 2010, OCEANS'10 IEEE SYDNEY.

[7]  Anis Koubaa.  Robot Operating System (ROS): The Complete Reference (Volume 1) , 2016 .

[8]  Gaurav S. Sukhatme,et al.  Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Sutton,et al.  Further Advances in Unmanned Marine Vehicles , 2012 .

[10]  Gaurav S. Sukhatme,et al.  Adaptive Sampling With Multiple Mobile Robots , 2008 .

[11]  Gaurav S. Sukhatme,et al.  Towards the Improvement of Autonomous Glider Navigational Accuracy Through the use of Regional Ocean Models , 2010 .

[12]  Junku Yuh,et al.  Design and Control of Autonomous Underwater Robots: A Survey , 2000, Auton. Robots.

[13]  Franz S. Hover,et al.  Tracking ocean fronts with multiple vehicles and mixed communication losses , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  James G. Bellingham,et al.  Two-dimensional mapping and tracking of a coastal upwelling front by an autonomous underwater vehicle , 2013, 2013 OCEANS - San Diego.

[15]  Gaurav S. Sukhatme,et al.  Persistent ocean monitoring with underwater gliders: Adapting sampling resolution , 2011, J. Field Robotics.

[16]  Oscar Schofield,et al.  Using a fleet of slocum battery gliders in a regional scale coastal ocean observatory , 2002, OCEANS '02 MTS/IEEE.

[17]  John E. O'Reilly,et al.  An algorithm for oceanic front detection in chlorophyll and SST satellite imagery , 2009 .

[18]  Hanumant Singh,et al.  Issues in AUV design and deployment for oceanographic research , 1997, Proceedings of International Conference on Robotics and Automation.

[19]  Peter Cornillon,et al.  Fronts in Large Marine Ecosystems , 2009 .

[20]  Frederic Py,et al.  Towards Deliberative Control in Marine Robotics , 2013 .

[21]  Donald B. Olson,et al.  Life on the edge : marine life and fronts , 1994 .

[22]  Gaurav S. Sukhatme,et al.  Planning and Implementing Trajectories for Autonomous Underwater Vehicles to Track Evolving Ocean Processes Based on Predictions from a Regional Ocean Model , 2010, Int. J. Robotics Res..

[23]  Jnaneshwar Das,et al.  ODSS: A decision support system for ocean exploration , 2013, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW).

[24]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[25]  Gaurav S. Sukhatme,et al.  Towards improving mission execution for autonomous gliders with an ocean model and kalman filter , 2012, 2012 IEEE International Conference on Robotics and Automation.

[26]  Yanwu Zhang,et al.  Using an Autonomous Underwater Vehicle to Track the Thermocline Based on Peak-Gradient Detection , 2012, IEEE Journal of Oceanic Engineering.

[27]  David M. Fratantoni,et al.  Multi-AUV Control and Adaptive Sampling in Monterey Bay , 2006, IEEE Journal of Oceanic Engineering.

[28]  Naomi Ehrich Leonard,et al.  Cooperative Control for Ocean Sampling: The Glider Coordinated Control System , 2008, IEEE Transactions on Control Systems Technology.

[29]  Frederic Py,et al.  T-REX: partitioned inference for AUV mission control , 2012 .

[30]  James G. Bellingham,et al.  Ocean front detection and tracking by an autonomous underwater vehicle , 2011, OCEANS'11 MTS/IEEE KONA.