Adaptive Sampling Using Feedback Control of an Autonomous Underwater Glider Fleet

Edward Fiorelli, Pradeep Bhatta, Naomi Ehrich Leonard and Igor Shulman

In this paper we present strategies for adaptive sampling using Autonomous Underwater Vehicle (AUV) fleets. The central theme of our strategies is the use of feedback that integrates distributed in-situ measurements into a coordinated mission planner. The measurements consist of GPS updates and estimated gradients of the environmental fields (e.g., temperature) that are used to navigate the AUV fleets enabling effective front tracking and/or feature detection. To this effect these fleets are required to translate to collect and seek good data, expand/contract to effect changes in sensor resolution, and rotate and reconfigure to maximize sensing coverage, all while retaining a prescribed formation. These strategies play a key role in directing a cooperative fleet of autonomous underwater gliders in the first experiment of the Office of Naval Research sponsored Autonomous Ocean Sampling Network II (AOSN-II) project in Monterey Bay, during August-September 2003. We present the coordination framework and investigate the effectiveness of our sampling strategies in the context of AOSN-II via detailed simulations.