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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.

For supporting the dynamic routing plan more efficiently, this study has been established by integrating PSO (Particle Swarm Optimization) −based dynamic routing algorithm, self-tuning fuzzy controller, a stereo-vision detection technique and 6-DOF mathematical model into the inspection system of AUV (Autonomous Underwater Vehicle). Specifically, the PSO-based dynamic routing algorithm is modified by adopting the concept of Multi-Objective Particle Swarm Optimization (MOPSO), which is able to handle different weights of objectives in parallel. Therefore, a modular structure is applied to program design of the system by using the graphical language, LabVIEW®, which is composed of 6-DOF motion module, a self-tuning fuzzy control module, a stereo-vision detection module, and a dynamic routing module. Performances resulted from the MOPSO-based dynamic routing algorithm would be discussed by conducting a series of inspection tasks in the imitated offshore wind farm. Additionally, selections of fixed weight and dynamic weight of MOPSO-based dynamic routing algorithm would be compared via Pareto frontiers for feasible solutions of both sailing time and energy consumption. Eventually, it is verified that the MOPSO-based dynamic routing algorithm in our system is not only able to estimate the feasible routes intelligently, but also identify features of underwater structures for the purpose of positioning.

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.

Underwater data collection by using Autonomous Underwater Vehicles (AUVs) is increasing within the oceanographic research community. AUVs are remarkable machines that processed data for gathering local bathymetric information, which have been developed, investigated and utilized for various purposes. In this study, the Iver2 AUV is examined to accomplish location mapping by well-planned trajectory while executing missions. The goal is to collect a large-scale Xingda harbor side-scan data. Therefore, in this study, the Iver2 AUV configured by Taiwan Ocean Research Institute (TORI) and built by OceanServer is discussed. The Iver2 performs routine operations at sea surface; all the components are rated to 100 meters.

In the recent decades, Autonomous Underwater Vehicles (AUVs) are remarkable machines that revolutionized the process of gathering ocean data. AUV systems have been developed, operated, marketed, produced, sold and utilized in water for various purposes. Accordingly, in this study, the IVER2 AUV which configuration by Taiwan Ocean Research Institute (TORI) and built by OceanServer is discussed. The IVER2 performs routine operations sea surface; all the components are rated to 100 m.

A food processor with recognition ability of emotion-related information and emotional signals is disclosed, which comprises: an emotion recognition module and a food processing module. The emotion recognition module is capable of receiving sound signals so as to identify an emotion containing in the received sound signals. The food processing module is capable of producing food products with a taste corresponding to the emotion recognition result of the emotion recognition module.