An entropic framework for AUV sensor modelling
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This thesis examines the general task of active sensing by defining a measure of efficiency for sensing in a particular environment. We focus on fine-scale acoustic mapping from an autonomous underwater vehicle (AUV). The constraints on imaging underwater - vehicle power, vehicle hydrodynamics, computational and telemetry requirements, and typical navigational and attitudinal uncertainties along with the underlying physics of the acoustic sensing modality- are considered in defining an entropic measure of sensor efficiency. 675-kHz pencil-beam sonar data acquired using the JASON remotely operated vehicle in a challenging shallow water environment and 200-kHz echo-sounder data acquired using the ABE AUV are used to demonstrate the utility of the en tropic framework. We show the utility of an entropic framework for the following: (i) Optimizing the speed of the AUV for maximizing the information gathered with a particular sensor. (ii) the rate of convergence and the stability of our mapping efforts in the face of typical uncertainties in navigation and attitude; (iii) as a methodology for actual sensor deployment and use on a real vehicle; and (iv) in tasks such as post-mission analysis for applications such as change detection and path planning for subsequent missions.
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Woods Hole Oceanographic Institution and the Massachusetts Institute of Technology May 1995
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