Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay

Abstract

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. C

Cooperative Vehicle Environmental Monitoring

“…The fact that sampling performance recovered subsequent to adaptation of the GCT showed how coordinated motion control can improve sampling performance. Details on the ocean conditions, adaptations, and performance of the gliders during the field experiment are described in [3].…”

“…The first of these was the Autonomous Ocean Sampling Network (AOSN) II project [1,2] with its field experiment in Monterey Bay, CA, over the month of August 2003 and the second was the Adaptive Sampling and Prediction (ASAP) project with its field experiment in Monterey Bay, CA, over the month of August 2006 [3].…”

“…The gliders were also used to demonstrate autonomous coordination of their motion to sample along the path of drifters and thus increase measurement density along fronts. For ASAP, a methodology was designed [6] and demonstrated [3] that featured a network of ten gliders (of two types) to optimize dynamic coverage, minimizing uncertainty for estimation of temperature, salinity and currents in a large coastal region just north of Monterey Bay. In both field experiments, three ocean models ran in near real-time, assimilating data collected by the gliders and providing estimates and predictions that were used in the gliders' adaptive motion planning.…”

Dynamic Modeling and Motion Analysis of Unmanned Underwater Gliders with Mass Shifter Unit and Buoyancy Engine

“…이러한 플 랫폼에 여러 가지 음향장비 및 센서를 장착하여 넓은 범위의 해저 지형 또는 해양 환경 자료를 수집하거나, 대서양 횡단 실 험을 수행하는 등 해양생태학 및 해양환경학에 응용할 수 있는 실해역 실험들을 수행하고 있다 (Fiorelli et al, 2006;Leonard et al, 2010;Rudnick et al, 2004;Smith et al, 2011 …”

Persistent ocean monitoring with underwater gliders: Towards accurate reconstruction of dynamic ocean processes

“…As previously mentioned, horizontal variability in this region is not as significant as vertical variability, i.e., it is more important to obtain complete coverage during a vertical profile than it is over a horizontal transect, which implies that complete horizontal coverage is not necessary. Other approaches to surveillance and monitoring with AUVs essentially boil down to achieving the best estimation or recreation of a scalar field via intelligent planning or adaptive sampling (Leonard et al, 2010;McGann, Py, Rajan, Ryan, & Henthorn, 2008;Paley, Zhang, & Leonard, 2008;Singh, Krause, Guestrin, & Kaiser, 2009). These methods can present a biased optimization problem.…”