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This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed.

In this study, underwater recognition technology and a fuzzy control system were adopted to adjust the attitude and revolution speed of a self-developed autonomous underwater vehicle (AUV). To validate the functionality of visual-recognition control, an experiment was conducted in the towing tank at the Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University. An underwater lighting box was towed by a towing carriage at low speed. By adding real-time contour approximation and a circle-fitting algorithm to the image-processing procedure, the relationship between the AUV and the underwater lighting box was calculated. Both rudder plane angles and propeller revolution speeds were determined after the size and location of the lighting box was measured in the image. Finally, AUV performance with visual-recognition control was verified by controlling the target object in the image center during passage.