Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high‐value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long‐term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions. © 2011 Wiley Periodicals, Inc.

[1]  Andreas Krause,et al.  Efficient Informative Sensing using Multiple Robots , 2014, J. Artif. Intell. Res..

[2]  G. Sukhatme,et al.  Persistent ocean monitoring with underwater gliders: Towards accurate reconstruction of dynamic ocean processes , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Mac Schwager,et al.  Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments , 2011, IEEE Transactions on Robotics.

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

[5]  Daniela Rus,et al.  Multi-robot monitoring in dynamic environments with guaranteed currency of observations , 2010, 49th IEEE Conference on Decision and Control (CDC).

[6]  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.

[7]  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..

[8]  Clark Rowley,et al.  Comparisons of upwelling and relaxation events in the Monterey Bay area , 2010 .

[9]  Cheng Fang,et al.  Coverage path planning for harbour seabed surveys using an autonomous underwater vehicle , 2010, OCEANS'10 IEEE SYDNEY.

[10]  Gaurav S. Sukhatme,et al.  Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

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

[13]  Burton H. Jones,et al.  Spatiotemporal development of physical, chemical, and biological characteristics of stormwater plumes in Santa Monica Bay, California (USA) , 2010 .

[14]  John P. Ryan,et al.  Interacting physical, chemical and biological forcing of phytoplankton thin-layer variability in Monterey Bay, California , 2010 .

[15]  G. North,et al.  Empirical Orthogonal Functions: The Medium is the Message , 2009 .

[16]  Andrew C. Thomas,et al.  Interannual variability in chlorophyll concentrations in the Humboldt and California Current Systems , 2009 .

[17]  Gaurav S. Sukhatme,et al.  Implementation of an embedded sensor network for the coordination of Slocum gliders for coastal monitoring and observation , 2009, WUWNet.

[18]  B. Jones,et al.  Cross-shelf transport into nearshore waters due to shoaling internal tides in San Pedro Bay, CA. , 2009 .

[19]  Jonathan A. Warrick,et al.  Impacts of stormwater runoff in the Southern California Bight: Relationships among plume constituents , 2009 .

[20]  David A. Siegel,et al.  Rapid downward transport of the neurotoxin domoic acid in coastal waters. , 2009 .

[21]  Andrew M. Fischer,et al.  Influences of upwelling and downwelling winds on red tide bloom dynamics in Monterey Bay, California , 2009 .

[22]  Paul J. Martin,et al.  Impact of glider data assimilation on the Monterey Bay model , 2009 .

[23]  K. Rajan,et al.  Adaptive Control for Autonomous Underwater Vehicles , 2008, AAAI Conference on Artificial Intelligence.

[24]  Andrew M. Fischer,et al.  A coastal ocean extreme bloom incubator , 2008 .

[25]  K. Ide,et al.  A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System , 2008 .

[26]  Timo Oksanen,et al.  Path planning algorithms for agricultural field machines , 2007 .

[27]  Libe Washburn,et al.  River plume patterns and dynamics within the Southern California Bight , 2007 .

[28]  Paul J. Martin,et al.  Modeling of upwelling/relaxation events with the Navy Coastal Ocean Model , 2007 .

[29]  Oscar Schofield,et al.  Slocum Gliders: Robust and ready , 2007, J. Field Robotics.

[30]  Stephen B. Weisberg,et al.  Blooms of Pseudo-nitzschia and domoic acid in the San Pedro Channel and Los Angeles harbor areas of the Southern California Bight, 2003-2004 , 2007 .

[31]  John L. Largier,et al.  Cross-shelf subtidal variability in San Pedro Bay during summer, 2001 , 2006 .

[32]  Stephen P. Boyd,et al.  Convex Optimization , 2004, IEEE Transactions on Automatic Control.

[33]  Benjamin Holt,et al.  Coastal pollution hazards in southern California observed by SAR imagery: stormwater plumes, wastewater plumes, and natural hydrocarbon seeps. , 2004, Marine pollution bulletin.

[34]  Jonathan A. Warrick,et al.  Dispersal scaling from the world's rivers , 2004 .

[35]  Libe Washburn,et al.  Spatial scales and evolution of stormwater plumes in Santa Monica Bay. , 2003, Marine Environmental Research.

[36]  Tommy D. Dickey,et al.  Hydrographic and particle distributions over the Palos Verdes continental shelf: Spatial, seasonal and daily variability , 2002 .

[37]  Naomi Ehrich Leonard,et al.  Model-based feedback control of autonomous underwater gliders , 2001 .

[38]  Alpár Jüttner,et al.  Lagrange relaxation based method for the QoS routing problem , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[39]  Howie Choset,et al.  Coverage of Known Spaces: The Boustrophedon Cellular Decomposition , 2000, Auton. Robots.

[40]  Porter Hoagland,et al.  Estimated Annual Economic Impacts from Harmful Algal Blooms (HABs) in the United States , 2000 .

[41]  Francisco P. Chavez,et al.  Seasonal fluctuations of temperature, salinity, nitrate, chlorophyll and primary production at station H3/M1 over 1989-1996 in Monterey Bay, California , 2000 .

[42]  James C. McWilliams,et al.  Quasi-Monotone Advection Schemes Based on Explicit Locally Adaptive Dissipation , 1998 .

[43]  D. Chelton,et al.  Geographical Variability of the First Baroclinic Rossby Radius of Deformation , 1998 .

[44]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[45]  James J. Simpson,et al.  An empirical orthogonal function analysis of remotely sensed sea surface temperature variability and its relation to interior oceanic processes off Baja California , 1994 .

[46]  A. Bratkovich,et al.  Aspects of the Tidal Variability Observed on the Southern California Continental Shelf , 1985 .

[47]  T. P. Barnett,et al.  Scales of Thermal Variability in the Tropical Pacific , 1980 .

[48]  D. M. Hardy,et al.  Empirical eigenvector analysis of vector observations , 1977 .

[49]  Ingemar Holmström,et al.  Analysis of time series by means of empirical orthogonal functions , 1970 .

[50]  Gaurav S. Sukhatme,et al.  A Communication Framework for Cost-Effective Operation of AUVs in Coastal Regions , 2009, FSR.

[51]  Porter Hoagland,et al.  The Economic Effects of Harmful Algal Blooms , 2006 .

[52]  Alexander F. Shchepetkin,et al.  The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model , 2005 .

[53]  Joshua Grady Graver,et al.  UNDERWATER GLIDERS: DYNAMICS, CONTROL AND DESIGN , 2005 .

[54]  Philippe Souères,et al.  Path Planning for Complete Coverage with Agricultural Machines , 2003, FSR.

[55]  Bal Azs Lagrange Relaxation Based Method for the QoS Routing Problem , 2001 .

[56]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[57]  H. Stommel The Slocum Mission , 1989 .

[58]  David F. Rogers,et al.  THE SOCIETY OF NAVAL ARCHITECTS AND MARINE ENGINEERS , 1977 .

[59]  GEOHAB – Global Ecology and Oceanography of Harmful Algal Blooms , 2022 .