本研究的主要目的著重在視覺辨識及立體影像測深技術之開發，並應用在自主式水下無人載具影像導航系統之建構。在視覺辨識技術上結合霍夫轉換法與光流演算法作為偵測動態影像之直線特徵與移動速度；在立體影像測深技術則是利用Harris角點檢測法推估目標物之距離。經由搭載於艏艙的廣角鏡頭與雙眼視覺裝置所截取的影像作為輸入源，再透過Simscape Multibody所建立的模擬環境，可控制艉艙的後驅動系統(包含水平、垂直舵板及推進器)。在待測目標物的靜態測試上，本研究於國立成功大學系統及船舶機電工程系穩定性能水槽建置待測目標物，並於空氣與水下環境中比較不同亮度、濁度下的分析結果。最後，待測目標物的動態測試結果經結合模糊控制器，也可用以輸出目標物在不同移動速度、距離下，舵板的轉動角度、角速度與推進器的即時反應。

The main purpose of this research is the development of visual identification, stereo matching, and fuzzy control with an imaging module applied to the construction of an AUV navigation system. For the visual identification, the Hough transform method and the optical flow algorithm are combined to detect the linear features and moving speed of the images. In the stereo matching, the triangulation method is used to estimate the distance of the target. In the static test, the target to be tested is established in the stability tank at NCKU to compare the results under different degrees of brightness and turbidity in both air and underwater environments. Finally, the dynamic test results of the object are combined with a fuzzy controller and can also be used to output the rudder and plane angles as well as the rotation speed of the propeller.

Armstrong, B., Wolbrecht, E., Edwards, D., 2010. AUV navigation in the presence of a magnetic disturbance with an extended Kalman filter, OCEANS 2010 IEEE-Sydney. IEEE, pp. 1-6.
Balasuriya, A., Ura, T., 2002. Vision-based underwater cable detection and following using AUVs, OCEANS'02 MTS/IEEE. IEEE, pp. 1582-1587.
Balasuriya, B., Takai, M., Lam, W., Ura, T., Kuroda, Y., 1997. Vision based autonomous underwater vehicle navigation: underwater cable tracking, OCEANS'97. MTS/IEEE Conference Proceedings. IEEE, pp. 1418-1424.
Ballard, D.H., 1987. Generalizing the Hough transform to detect arbitrary shapes, Readings in computer vision. Elsevier, pp. 714-725.
Bradski, G., Kaehler, A., 2008. Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".
Bruno, F., Bianco, G., Muzzupappa, M., Barone, S., Razionale, A., 2011. Experimentation of structured light and stereo vision for underwater 3D reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 66 (4), 508-518.
Cheng, C.-C., Li, H.-T., 2006. Feature-based optical flow computation. International Journal of Information Technology 12 (7), 82-90.
Dony, R., Wesolkowski, S., 1999. Edge detection on color images using RGB vector angles, Electrical and Computer Engineering, 1999 IEEE Canadian Conference on. IEEE, pp. 687-692.
Foresti, G.L., Gentili, S., Zampato, M., 1999. Autonomous underwater vehicle guidance by integrating neural networks and geometric reasoning. International Journal of Imaging Systems and Technology 10 (5), 385-396.
Fossen, T.I., 1994. Guidance and control of ocean vehicles. John Wiley & Sons Inc.
Gao, W., Yang, L., Zhang, X., Zhou, B., Ma, C., 2010. Based on soft-threshold wavelet de-noising combining with Prewitt operator edge detection algorithm, Education Technology and Computer (ICETC), 2010 2nd International Conference on. IEEE, pp. V5-155-V155-162.
Gregory, D., 2000. In situ corrosion studies on the submarine Resurgam: A preliminary assessment of her state of preservation. Conservation and Management of Archaeological Sites 4 (2), 93-100.
Horn, B.K., Schunck, B.G., 1981. Determining optical flow. Artificial intelligence 17 (1-3), 185-203.
Hu, B., Tian, H., Qian, J., Xie, G., Mo, L., Zhang, S., 2013. A fuzzy-PID method to improve the depth control of AUV, Mechatronics and Automation (ICMA), 2013 IEEE International Conference on. IEEE, pp. 1528-1533.
Illingworth, J., Kittler, J., 1988. A survey of the Hough transform. Computer vision, graphics, and image processing 44 (1), 87-116.
Jun, B.-H., Park, J.-Y., Lee, F.-Y., Lee, P.-M., Lee, C.-M., Kim, K., Lim, Y.-K., Oh, J.-H., 2009. Development of the AUV ‘ISiMI’and a free running test in an Ocean Engineering Basin. Ocean engineering 36 (1), 2-14.
Kanopoulos, N., Vasanthavada, N., Baker, R.L., 1988. Design of an image edge detection filter using the Sobel operator. IEEE Journal of solid-state circuits 23 (2), 358-367.
Kiryati, N., Eldar, Y., Bruckstein, A.M., 1991. A probabilistic Hough transform. Pattern recognition 24 (4), 303-316.
Lin, Y.H., Wang, S.M., Huang, L.C., Fang, M.C., 2017. Applying the stereo-vision detection technique to the development of underwater inspection task with PSO-based dynamic routing algorithm for autonomous underwater vehicles. Ocean engineering 139, 127-139.
Litwiller, D., 2001. Ccd vs. cmos. Photonics spectra 35 (1), 154-158.
McAndrew, A., 2004. An introduction to digital image processing with matlab notes for scm2511 image processing. School of Computer Science and Mathematics, Victoria University of Technology 264.
Myring, D., 1976. A theoretical study of body drag in subcritical axisymmetric flow. The Aeronautical Quarterly 27 (3), 186-194.
Negahdaripour, S., Xu, X., Jin, L., 1999. Direct estimation of motion from sea floor images for automatic station-keeping of submersible platforms. IEEE JOURNAL OF OCEANIC ENGINEERING 24 (3), 370-382.
Pararas-Carayannis, G., 1976. Tutle: A Revolutionary Submarine. Sea Froniters 22 (4), 234.
Park, J.-Y., Jun, B.-h., Lee, P.-m., Oh, J., 2009. Experiments on vision guided docking of an autonomous underwater vehicle using one camera. Ocean engineering 36 (1), 48-61.
Prestero, T.T.J., 2001. Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle. Massachusetts institute of technology.
Rizzini, D.L., Kallasi, F., Aleotti, J., Oleari, F., Caselli, S., 2017. Integration of a stereo vision system into an autonomous underwater vehicle for pipe manipulation tasks. Computers & Electrical Engineering 58, 560-571.
Sáez, J.M., Escolano, F., 2005. Entropy minimization SLAM using stereo vision, Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on. IEEE, pp. 36-43.
Sáez, J.M., Hogue, A., Escolano, F., Jenkin, M., 2006. Underwater 3D SLAM through entropy minimization, Robotics and automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on. IEEE, pp. 3562-3567.
Stelzer, R., Proll, T., John, R.I., 2007. Fuzzy logic control system for autonomous sailboats, Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International. IEEE, pp. 1-6.
Sun, B., Zhu, D.Q., Yang, S.X., 2018. An Optimized Fuzzy Control Algorithm for Three-Dimensional AUV Path Planning. International Journal of Fuzzy Systems 20 (2), 597-610.
Wettergreen, D., Gaskett, C., Zelinsky, A., 1998. Development of a visually-guided autonomous underwater vehicle, OCEANS'98 Conference Proceedings. IEEE, pp. 1200-1204.
Yamakawa, T., 2011. Electronic circuits dedicated to fuzzy logic controller. Scientia Iranica 18 (3), 528-538.
Yu, C.Y., Xiang, X.B., Lapierre, L., Zhang, Q., 2017. Nonlinear guidance and fuzzy control for three-dimensional path following of an underactuated autonomous underwater vehicle. Ocean engineering 146, 457-467.
Zhang, T.D., Wan, L., Zeng, W.J., Xu, Y.R., 2012. Object detection and tracking method of AUV based on acoustic vision. China Ocean Engineering 26 (4), 623-636.
何祚镛, 2002. 水下噪聲及其控制技術進展和展望. 應用聲學 21 (1), 26-34.
於睦程, 2011. 應用模糊控制於自主型水下載具之避碰操控. 成功大學系統及船舶機電工程學系學位論文, 1-131.
師樂善, 1996. 水下物體辨識之硏究: 光學成像與音波測距法之應用. National Taiwan University Department of Naval Architecture and Ocean Engineering.
徐偉, 王朔中, 2006. 基於視頻圖像 Harris 角點檢測的車輛測速.
袁國武, 陳志强, 龚健, 徐丹, 廖仁健, 何俊遠, 2013. 一種结合光流法與三幀差分法的運動目標檢測算法.
莊凱強, 2013. 基於立體視覺之即時障礙物追蹤與避障方法. 臺灣大學生物產業機電工程學研究所學位論文, 1-114.
麥耘菁, 2015. 使用單視覺影像定位與建圖方法結合慣性量測元件提升室內定位與導航之研究.
劉岳豪, 2004. 利用影像特徵進行水下檢測影像拼嵌之研究. 成功大學系統及船舶機電工程學系學位論文, 1-47.
謝銘信, 2004. 實海域之水下聲響通訊技術之探討. 成功大學系統及船舶機電工程學系學位論文, 1-116.