Adaptive Sampling in Autonomous Marine Sensor Networks

Donald P. Eickstedt, Ph.D., 2006
Henrik Schmidt, Advisor

This thesis describes an innovative architecture for real-time adaptive and cooperative controlof autonomous sensor platforms in a marine sensor network.  This architecture has three major components, a logical sensor that provides abstract state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations.

Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying  the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates.