A large body of work has been devoted to development of control laws for underwater autonomous vehicles to create platforms for ocean sampling. The work led to two large field experiments: AOSN-II in 2003 and ASAP in 2006.
Autonomous Ocean Sampling Network II
AOSN-II was an ONR-sponsored, multi-institutional, collaborative research program with the central objective “to quantify the gain in predictive skill for principal circulation trajectories, transport at critical points and near-shore bioluminescence potential in Monterey Bay as a function of model-guided, remote adaptive sampling using a network of autonomous underwater vehicles”. The overall goals of adaptive sampling are presented below; a most important purpose of adaptive sampling is to provide data for updating and evaluating forecast models.
In AOSN-II, the underwater vehicle network features a fleet of autonomous underwater gliders. Gliders are small, relatively simple and inexpensive, winged, buoyancy-driven submersibles that have high endurance and are strongly influenced by the currents. Adaptive sampling by the glider network should exploit these capabilities (e.g., by taking advantage of current forecasts to steer gliders efficiently) as well as the opportunity to use the glider network itself as a re-configurable, mobile sensor array.
The video below shows a formation of vehicles implementing adaptive gradient climbing during the 2003 field experiment in Monterey Bay:
E. Fiorelli, N.E. Leonard, P. Bhatta, D. Paley, R. Bachmayer, D.M. Fratantoni. “Multi-AUV Control and Adaptive Sampling in Monterey Bay,” IEEE Journal of Oceanic Engineering, 2006.
P. Ogren, E. Fiorelli and N.E. Leonard. “Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment,” IEEE Transactions on Automatic Control, 2004.
Adaptive Sampling and Prediction
ASAP was a multi-institutional, collaborative research program with the central objective to learn how to deploy, direct and utilize autonomous vehicles (and other mobile sensing platforms) most efficiently to sample the ocean, assimilate the data into numerical models in real or near-real time, and predict future conditions with minimal error.
Specific goals included
- Demonstrating ability to provide adaptive sampling and evaluate benefits of adaptive sampling.
- Coordinating multiple assets to optimize sampling at the physical scales of interest.
- Understanding dynamics of 3D upwelling centers.
N.E. Leonard, D.A. Paley, R.E. Davis, D.M. Fratantoni, F. Lekien and F. Zhang. “Coordinated Control of an Underwater Glider Fleet in an Adaptive Ocean Sampling Field Experiment in Monterey Bay.” Journal of Field Robotics, 2010.
D. Paley, F. Zhang and N.E. Leonard. “Cooperative Control for Ocean Sampling: The Glider Coordinated Control System.” IEEE Transactions on Control Systems Technology, 2008.
N.E. Leonard, D. Paley, F. Lekien, R. Sepulchre, D.M. Fratantoni and R. Davis. “Collective Motion, Sensor Networks and Ocean Sampling,” Proceedings of the IEEE, 2007.