A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sampling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of autonomous vehicles carrying environmental sensors to efficiently sample the ocean.We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Six of these gliders were coordinated autonomously for 24 days straight using feedback laws that scale with the number of vehicles. These feedback laws were systematically computed using recently developed methodology to produce desired collective motion patterns, tuned to the spatial and temporal scales in the sampled fields for the purpose of reducing statistical uncertainty in field estimates. The implementation was designed to allow for adaptation of coordinated sampling patterns using human-in-theloop decision making, guided by optimization and prediction tools. The results demonstrate an innovative tool for ocean sampling and provide a proof of concept for an important field robotics endeavor that integrates coordinated motion control with adaptive sampling.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications