Date of Award
MS in Engineering
This thesis explores the use of an Autonomous Underwater Vehicle (AUV) to track and pursue a tagged shark through the water. A controller was designed to take bearing and range to the shark tag and then control the AUV to pursue it.
First, the ability of a particle filter to provide an accurate estimation of the location of the shark relative to the AUV is explored. Second, the ability of the AUV to follow the shark's path through the water is shown. This ability allows for localized environmental sampling of the shark's preferred path. Third, various path weightings are used to optimize the efficiency of pursuing the shark. This demonstrates that the proposed controller is efficient and effective. Fourth, the benefits of the addition of a second AUV are explored and quantified. The secondary AUV is shown to maintain formation without direct communication from the primary AUV. However, the communication of the AUVs increases the accuracy of all measurements and allows for future expansion in the complexity of the controller. Fifth, the effects of predicting the shark$'$s future movement is explored. Sixth, the effect of noise in the signal from the shark tag is tested and the level of noise at which the AUV can no longer pursue the shark is shown. This investigates the real world ability of the controller to accept noisy inputs and still generate the appropriate response. Finally, the positive results of the previous sections are combined and tested for various noise levels to show the improved controller response even under increased noise levels.
To validate the proposed estimator and controller, seven tests were conducted. All tests were conducted on existing shark path data recorded by a stationary acoustic receiver and a boat mounted acoustic receiver. Tests were conducted on data sets from two different species of sharks, (Shovelnose and White) with two very different swimming behaviors. This shows the solution's flexibility in the species of shark tracked.