| Summary: | 碩士 === 國立中山大學 === 海下科技暨應用海洋物理研究所 === 102 === Remotely Operated Vehicle (ROV) has flourished in recent years, as evidenced by facilitating the replacement of divers and manned submersibles for the operation of diverse underwater tasks. Currently ROVs are being used worldwide for a variety of underwater tasks, including scientific research, mine hunting, offshore engineering, structural inspection, heavy work construction, and wreck recovery. In Taiwan, ROV technology is well-known in academic circles but rare in the offshore industries. Even though the use of ROV is expected to become more and more attractive in the offshore industries as it becomes increasingly affordable, there are challenges need to be solved to increase its operation efficiency. While operating ROVs for underwater inspection tasks of offshore construction, one of the major problems is the disturbances caused by surface wave and current. Another problem encountered is the limited underwater visibility. Even though nylon ropes are usually laid on the seafloor to aid the divers/ROVs to follow for conducting inspections, the maneuvering of a ROV for underwater pipeline/cable tracking requires experienced operators and continued attention. A good pipeline/cable tracking control of ROV is desired for relieving the burden of operators and increase inspection efficiency. Therefore, we designed and fabricated the ROV Auroree, and a proportional-differential controller is implemented on the ROV Auroree for auto-pilot performing auto-heading, auto-depth, and auto-altitude functions. In addition, considering that an image-based visual servo control of ROV for the pipeline/cable tracking problem can facilitate the efficiency of underwater inspection, the methods of image processing for line detection are developed in this study. The proposed image-processing algorithm is divided into three stages: color space transformation, edge detection, and line detection. At each stage of image processing, the performance of different methods is evaluated and accordingly a procedure suitable for underwater nylon rope tracking is established. The evaluation results indicate that, in the YCbCr color space, the yellow nylon rope has a high blue chrominance (Cb) value, which make it effective to extract the rope from the background. Then, in the edge-detection stage, it is found that applying the Canny detector on the Cb attribute of the underwater image gives sharp thin edge of the nylon rope. Moreover, in the stage of line detection, we founded that the Probabilistic Hough Transform (PHT) is more computationally efficient than the Hough Transform to acquire the line segments of the nylon rope in images.
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