Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called Aη, is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators.

[1]  Gaurav S. Sukhatme,et al.  Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[2]  Olivier Ledoit,et al.  Honey, I Shrunk the Sample Covariance Matrix , 2003 .

[3]  Brian J. Rothschild,et al.  Biological-physical interactions in the sea , 2002 .

[4]  Nicholas M. Patrikalakis,et al.  Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis , 2004, International Conference on Computational Science.

[5]  Cecilia Laschi,et al.  The HydroNet ASV, a Small-Sized Autonomous Catamaran for Real-Time Monitoring of Water Quality: From Design to Missions at Sea , 2015, IEEE Journal of Oceanic Engineering.

[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]  Alberto Alvarez,et al.  Optimum Sampling Designs for a Glider–Mooring Observing Network , 2012 .

[8]  Fabrice Hernandez,et al.  Optimizing a Drifter Cast Strategy with a Genetic Algorithm , 1995 .

[9]  A Alvarez,et al.  Model based decision support for underwater glider operation monitoring , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[10]  Brian Bingham,et al.  Techniques for Deep Sea Near Bottom Survey Using an Autonomous Underwater Vehicle , 2007, Int. J. Robotics Res..

[11]  Michel Rixen,et al.  A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment , 2012, Ocean Dynamics.

[12]  Giuseppe Casalino,et al.  Autonomous underwater vehicle teams for adaptive ocean sampling: a data-driven approach , 2011 .

[13]  J. Cummings,et al.  Operational multivariate ocean data assimilation , 2005 .

[14]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[15]  Gwyn Griffiths,et al.  Biological-physical-acoustical interactions , 2005 .

[16]  Dana R. Yoerger,et al.  A novel trigger-based method for hydrothermal vents prospecting using an autonomous underwater robot , 2010, Auton. Robots.

[17]  J. M. Lewis,et al.  Dynamic Data Assimilation: A Least Squares Approach , 2006 .

[18]  Pierre F. J. Lermusiaux,et al.  Progress and Prospects of U.S. Data Assimilation in Ocean Research , 2006 .

[19]  Pierre F. J. Lermusiaux,et al.  Nonlinear optimization of autonomous undersea vehicle sampling strategies for oceanographic data-assimilation , 2007, J. Field Robotics.

[20]  B. Mourre,et al.  Benefit assessment of glider adaptive sampling in the Ligurian Sea , 2012 .

[21]  Dana R. Yoerger,et al.  Autonomous Search for Hydrothermal Vent Fields with Occupancy Grid Maps , 2008 .

[22]  Alberto Alvarez,et al.  Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the Western Mediterranean sea , 2009 .

[23]  N.M. Patrikalakis,et al.  Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields , 2006, OCEANS 2006.

[24]  M. Kamachi,et al.  Global statistical space-time scales of oceanic variability estimated from the TOPEX/ POSEIDON altimeter data , 2000 .

[25]  Naomi Ehrich Leonard,et al.  Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay , 2009 .

[26]  Beatrice Lazzerini,et al.  Making the optimal sampling of the ocean simpler: An automatic tool for planning glider missions using forecasts downloaded from MyOcean , 2013, 2013 MTS/IEEE OCEANS - Bergen.

[27]  J. Marra,et al.  Chapter 2. EFFECTS OF UPPER OCEAN PHYSICAL PROCESSES (TURBULENCE, ADVECTION AND AIR–SEA INTERACTION) ON OCEANIC PRIMARY PRODUCTION , 2001 .

[28]  Paul J. Martin,et al.  Description of the Navy Coastal Ocean Model Version 1.0 , 2000 .

[29]  Naomi Ehrich Leonard,et al.  Collective Motion, Sensor Networks, and Ocean Sampling , 2007, Proceedings of the IEEE.

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

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

[32]  F. Bretherton,et al.  A technique for objective analysis and design of oceanographic experiments applied to MODE-73* , 2002 .