本研究主要目的在於開發一套影像偵測系統，以輔助自主式水下無人載具(Autonomous Underwater Vehicle , AUV)進行水下檢測任務時之最佳航線規劃。在研究結果中將呈現AUV透過虛擬影像檢測環境，同時也對未知的障礙物進行避碰以確保航行安全。在路徑規劃上，則是使用多目標粒子群演算法(Multi-Objective Particle Swarm algorithm, MOPSO)結合虛擬影像之障礙物資訊，對各待選航向進行迭代搜索，並以時間與電量消耗做為依據，以判斷出最佳航向。在影像的虛擬化過程中，將在有限的影像空間內進行偵測點分佈，並參考立體視覺影像(Stereo Vision)技術的特點，以判斷影像中的環境深度(即影像深度的可視化)，藉此將可清楚判斷障礙物的特徵。綜而言之，本系統除了有利於最佳化路徑的選取外，也可在進行水下檢測任務時快速判斷待測物的特徵，進而達到定位的目的。最後，在模擬過程中將建構不同的水下待測物進行測試，分析結果將在影像偵測與AUV之水下檢測系統之結合上提供一重要參考依據。

In this thesis, the authors describe the development of an image detection mode to support the optimal route plan of Autonomous Underwater Vehicle (AUV) when inspecting offshore wind farms. The program, LABVIEW, is used to simulate the AUV’s trajectory. In addition, a modular structure is applied to program design in this study. The structure is composed of a fuzzy controller module, 6DOF motion module, an image detection module, and a dynamic routing module. In terms of path planning, the optimal route is selected using the Multi-Objective Particle Swarm algorithm (MOPSO) method and virtual images of obstacles. Thus, each feasible route is searched iteratively according to two objectives, namely, sailing time and energy consumption. In the image virtualization process, acquired points are distributed over a limited space, and environmental depths for identifying the features of obstacles are visualized using stereo vision. In summary, the system is not only beneficial for optimizing feasible routes but it can also identify features of obstacles for the purpose of positioning. Eventually, several underwater objects such as wind turbine foundations, cables, and obstacles would be incorporated into and tested in the simulation for combining image detection with underwater AUV inspection.

[1] H. Chen, S. Stavinoha, M. Walker, B. Zhang, and T. Fuhlbrigge, "Exploring robotic applications in offshore oil&gas industry," in Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2014 IEEE 4th Annual International Conference on, 2014, pp. 563-568.
[2] H. A. Kermorgant and D. Scourzic, "Interrelated functional topics concerning autonomy related issues in the context of autonomous inspection of underwater structures," in Oceans 2005-Europe, 2005, pp. 1370-1375.
[3] G. Griffiths, "Ocean Science Applications For Autonomous Underwater Vehicles -The Work Plan For Autosub-1 for 1997-2000 And Beyond," Unmanned Underwater Vehicle Showcase, pp. 24-25, Sept 1997.
[4] L. Paull, S. Saeedi, M. Seto, and H. Li, "AUV navigation and localization: A review," Oceanic Engineering, IEEE Journal of, vol. 39, pp. 131-149, 2014.
[5] B. Armstrong, E. Wolbrecht, and D. Edwards, "AUV navigation in the presence of a magnetic disturbance with an extended Kalman filter," in OCEANS 2010 IEEE-Sydney, 2010, pp. 1-6.
[6] R. Eustice, H. Singh, J. J. Leonard, M. Walter, and R. Ballard, Visually Navigating the RMS Titanic with SLAM Information Filters: In Proceedings of Robotics: Science and Systems (RSS), 2005.
[7] A. Mallios, P. Ridao, D. Ribas, F. Maurelli, and Y. Petillot, "EKF-SLAM for AUV navigation under probabilistic sonar scan-matching," in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, 2010, pp. 4404-4411.
[8] R. Belderson, E. Jones, M. Gorini, and N. Kenyon, "A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the equatorial Atlantic," Marine Geology, vol. 56, pp. 65-78, 1984.
[9] A. Ortiz, M. Simó, and G. Oliver, "A vision system for an underwater cable tracker," Machine vision and applications, vol. 13, pp. 129-140, 2002.
[10] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338-353, 1965.
[11] S. M. Smith, G. J. S. Rae, D. T. Anderson, and A. M. Shein, "Fuzzy-Logic Control of an Autonomous Underwater Vehicle," Control Engineering Practice, vol. 2, pp. 321-331, Apr 1994.
[12] P. A. DeBitetto, "Fuzzy logic for depth control of unmanned undersea vehicles," in Autonomous Underwater Vehicle Technology, 1994. AUV'94., Proceedings of the 1994 Symposium on, 1994, pp. 233-241.
[13] M.-C. Fang, S.-M. Wang, W. Mu-Chen, and Y.-H. Lin, "Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles," Ocean Engineering, vol. 93, pp. 11-24, 1/1/ 2015.
[14] B. Zerr, G. Mailfert, A. Bertholom, and H. Ayreault, "Sidescan sonar image processing for AUV navigation," in Europe Oceans 2005, 2005, pp. 124-130 Vol. 1.
[15] R. H. Belderson, E. J. W. Jones, M. A. Gorini, and N. H. Kenyon, "A long-range side-scan sonar (Gloria) survey of the Romanche active transform in the Equatorial Atlantic," Marine Geology, vol. 56, pp. 65-78, 1984/04/01 1984.
[16] G. L. Foresti, "Visual inspection of sea bottom structures by an autonomous underwater vehicle," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 31, pp. 691-705, 2001.
[17] L.-C. H. Y.-H. Lin, "The Development of Underwater Inspection Technique Applying Image Navigation Mode," Journal of Taiwan Society of Naval Architects and Marine Engineers, vol. 35, pp. 39-47, 2016.
[18] J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 760-766.
[19] J. E. Onwunalu and L. J. Durlofsky, "Application of a particle swarm optimization algorithm for determining optimum well location and type," Computational Geosciences, vol. 14, pp. 183-198, 2010.
[20] Y. Shi and R. C. Eberhart, "Parameter selection in particle swarm optimization," in International Conference on Evolutionary Programming, 1998, pp. 591-600.
[21] P. Fourie and A. A. Groenwold, "The particle swarm optimization algorithm in size and shape optimization," Structural and Multidisciplinary Optimization, vol. 23, pp. 259-267, 2002.
[22] S. Saravanakumar and T. Asokan, "Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space," Intelligent Service Robotics, vol. 6, pp. 211-224, Oct 2013.
[23] Y. Zhang, D.-w. Gong, and J.-h. Zhang, "Robot path planning in uncertain environment using multi-objective particle swarm optimization," Neurocomputing, vol. 103, pp. 172-185, 2013.
[24] M. Ataei and A. Yousefi-Koma, "Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle," Robotics and Autonomous Systems, vol. 67, pp. 23-32, 2015.
[25] M.-C. Fang and Y.-H. Lin, "The optimization of ship weather-routing algorithm based on the composite influence of multi-dynamic elements (II): Optimized routings," Applied Ocean Research, vol. 50, pp. 130-140, 3// 2015.
[26] K. E. Parsopoulos and M. N. Vrahatis, "Recent approaches to global optimization problems through Particle Swarm Optimization," Natural Computing, vol. 1, pp. 235-306, 2002.
[27] J. Castro-Gutiérrez and D. Landa-Silva, "Dynamic lexicographic approach for heuristic multi-objective optimization."
[28] E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," IEEE transactions on Evolutionary Computation, vol. 3, pp. 257-271, 1999.
[29] M. Reyes-Sierra and C. C. Coello, "Multi-objective particle swarm optimizers: A survey of the state-of-the-art," International journal of computational intelligence research, vol. 2, pp. 287-308, 2006.
[30] Y. Jin, M. Olhofer, and B. Sendhoff, "Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how?."
[31] H. Zhang, "Multiple particle swarm optimizers with inertia weight for multi-objective optimization," in Proceedings of the International MultiConference of Engineers and Computer Scientists, 2012.
[32] 方銘川 and 王舜民, "智慧型自主式水下載具之研發," 成大研發快訊, vol. 19-01, 2011.
[33] 於睦程, "應用模糊控制於自主型水下載具之避碰操控," 碩士, 系統及船舶機電工程學系碩博士班, 國立成功大學, 台南市, 2011.
[34] 侯章祥, "臍帶電纜及洋流對潛航器運動之影響," 碩士, 系統及船舶機電工程學系碩博士班, 國立成功大學, 台南市, 2005.
[35] 劉皓翔, "應用多目標粒子群演算法於船舶球形艏最佳化設計之研究," 碩士, 系統及船舶機電工程學系碩博士班, 國立成功大學, 台南市, 2012.
[36] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2002.
[37] M. Caramia and P. Dell'Olmo, Multi-objective Management in Freight Logistics: Springer-Verlag London, 2008.
[38] A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P. N. Suganthan, and Q. Zhang, "Multiobjective evolutionary algorithms: A survey of the state of the art," Swarm and Evolutionary Computation, vol. 1, pp. 32-49, 3// 2011.
[39] Z. Michalewicz, Genetic algorithms + data structures = evolution programs (3rd ed.): Springer-Verlag, 1996.
[40] K. Deb, "Multi-objective genetic algorithms: Problem difficulties and construction of test problems," Evolutionary computation, vol. 7, pp. 205-230, 1999.