Adaptive sampling in autonomous marine sensor networks

Abstract

In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, 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 with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time.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. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks. I would like to thank my adviser Henrik Schmidt for having the faith t o choose me for his group and for letting me have as much responsibility as I could handle. Your patience with me as I struggled with this program at times and your support at several critical times is very appreciated and won't be forgotten. The same goes for Professor John Leonard whose key support at a t least one critical point helped keep me in this program. Your insight and advice through the years is also greatly appreciated.

Cooperative Target Tracking in a Distributed Autonomous Sensor Network

“…From (3) and (4) it is apparent that the uncertainty in the target position estimates will be influenced by three factors:…”

“…Even though this is a nonoptimal estimation technique, good performance was obtained as shown in Section VI. The full derivation of the Kalman filter equations can be found in [3].…”

GOATS 2005: Integrated, Adaptive Autonomous Acoustic Sensing Systems

“…The new autonomy architecture was first tested in a series of tracking experiments using autonomous surface craft with simulated bearing sensors on the Charles River [6]. In the first experiment, a moving target (another autonomous surface craft) was tracked by a single sensor vehicle using only target bearings.…”