Oceanographic submersibles--Design

Maneuvering thrusters can provide small underwater vehicles with the ability to dynamically control position at low speeds. However, the successful implementation of these thrusters requires an understanding of their dynamic response, as well as a design which meets the specified design requirements. This thesis experimentally investigates the design and dynamic performance of small diameter tunnel thrusters for two small autonomous underwater vehicles. A systematic series of dynamic experiments were conducted with three working tunnel thruster prototypes that fulfill the operating and design constraints of these vehicles. The results from these experiments are shown to provide an accurate representation of the overall performance and thrust capability of the thrusters tested. Experimental data is compared with simulations utilizing a recently proposed thruster model, and the ability of the model to predict the dynamic response is discussed.

Autonomous underwater vehicle (AUV) missions are generally of a multi-tasked nature, i.e., there are usually several criteria which need to be met concurrently during the course of a mission. An example is the bottom altitude tracking mission proposed by the University of South Florida. They have developed a bottom classification and albedance package (BCAP) which will be used to record data to ground-truth oceanographic satellites. Two criteria needed for this mission are vehicle safety and motion stability of the recording sensors. This thesis will respectively compare the results of three bottom altitude tracking controllers: a linear modification of an existing depth controller, a TSK fuzzy logic controller, and a behavior based decision controller. Aspects analyzed for meeting the criteria were the ability of the auv to avoid collisions with bottom, the ability of the auv to maintain a desired altitude above the sea floor, and the ability of the auv to keep the amount of blur in a picture taken by a downward looking camera under one pixel. From simulation and real world testing, final results indicate the behavioral based decision controller was proven to be the most robust and the only controller tested to be able to handle multi-criteria.