隨著自主水下載具在海洋資源勘探、海底地形測繪、海洋生態研究及軍事應用中的廣泛使用，自主式水下載具(Autonomous Underwater Vehicle，AUV)避障技術的重要性日益凸顯。在海洋環境中的應用廣泛，存在碰撞風險且其製造成本不低，因此本研究探討了如何利用先進的感知技術達到避障的效果，進而提高AUV在複雜水下環境中的自主性和工作效率。
本研究致力於開發一套水平避障與地形建構系統，並在避障途中把所經過的環境建立出來，實驗設備自行運用SolidWorks 電腦輔助繪圖軟體設計載具的機構及相關隔艙平台，並利用3D列印技術製作零件。感測器方面使用前向掃描聲納(Forward Scan Sonar)掃描水下環境，結合都卜勒測速儀(Doppler Velocity Log, DVL)，將障礙物座標點補償至大地座標，以構建精確的水下環境。避障方法透過人工勢場法，將目標點視為吸引力，障礙物視為排斥力，調整參數迭代產生最佳路徑點，並利用PID控制器計算航向角誤差，控制推進器的工作週期，實現自主避障。本研究實際運用前向掃描聲納結合人工勢場，並利用DVL與壓力感測器回授進行速度與深度控制，應用於AUV自主避障與環境建構。

With the widespread use of Autonomous Underwater Vehicles (AUVs) in marine resource exploration, seabed topography mapping, marine ecological research, and military applications, the importance of AUV obstacle avoidance technology has become increasingly prominent. This study explores advanced sensing technology for effective obstacle avoidance to enhance the autonomy and operational efficiency of AUVs in complex underwater environments.The research focuses on developing a horizontal obstacle avoidance and terrain construction system. Using SolidWorks for design and 3D printing for manufacturing, the vehicle mechanism and compartment platforms were created. Forward Scan Sonar scans the underwater environment, while a Doppler Velocity Log (DVL) compensates the obstacle coordinates to geodetic coordinates, constructing an accurate underwater map. The artificial potential field method is used for obstacle avoidance, treating the target as an attractive force and obstacles as repulsive forces. Iterative parameter adjustments generate the optimal path, and a PID controller calculates heading angle errors to control the thruster's duty cycle, achieving autonomous obstacle avoidance. This study integrates forward scanning sonar with the artificial potential field method, using DVL and miniIPS feedback for speed and depth control, effectively applying this to AUV autonomous obstacle avoidance and environmental construction.

[1]Barbalata, C., E. Iscar, and M. Johnson-Roberson, "Experimental evaluation of depth controllers for a small-size AUV," in 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), 2018: IEEE, pp. 1-6.
[2]Cheng, C., D. Zhu, B. Sun, Z. Chu, J. Nie, and S. Zhang, "Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis," in 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), 2015: IEEE, pp. 717-721.
[3]Chengqing, L., M. H. Ang, H. Krishnan, and L. S. Yong, "Virtual obstacle concept for local-minimum-recovery in potential-field based navigation," in Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2000, vol. 2: IEEE, pp. 983-988.
[4]Das, B., B. Subudhi, and B. B. Pati, "Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment," (in English), Journal of Marine Engineering & Technology, vol. 15, no. 3, pp. 135-151, 2016.
[5]Eskinja, Z., Z. Fabekovic, and Z. Vukic, "Localization of autonomous underwater vehicles by sonar image processing," in ELMAR 2007, 2007: IEEE, pp. 103-106.
[6]Fan, X., Y. Guo, H. Liu, B. Wei, and W. Lyu, "Improved artificial potential field method applied for AUV path planning," (in English), Mathematical Problems in Engineering, vol. 2020, pp. 1-21, 2020.
[7]Ge, S. S. and Y. J. Cui, "New potential functions for mobile robot path planning," (in English), IEEE Transactions on robotics and automation, vol. 16, no. 5, pp. 615-620, 2000.
[8]Guo, Y., H. Liu, X. Fan, and W. Lyu, "Research progress of path planning methods for autonomous underwater vehicle," (in English), Mathematical Problems in Engineering, vol. 2021, pp. 1-25, 2021.
[9]Hu, B., H. Tian, J. Qian, G. Xie, L. Mo, and S. Zhang, "A fuzzy-PID method to improve the depth control of AUV," in 2013 IEEE International Conference on Mechatronics and Automation, 2013: IEEE, pp. 1528-1533.
[10]Khatib, O., "Real-time obstacle avoidance for manipulators and mobile robots," (in English), The international journal of robotics research, vol. 5, no. 1, pp. 90-98, 1986.
[11]Koren, Y. and J. Borenstein, "Potential field methods and their inherent limitations for mobile robot navigation," in Icra, 1991, vol. 2, no. 1991, pp. 1398-1404.
[12]Li, J.-H., H. Kang, G.-H. Park, and J.-H. Suh, "Real time path planning of underwater robots in unknown environment," in 2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2017: IEEE, pp. 312-318.
[13]Li, Q., L. Wang, B. Chen, and Z. Zhou, "An improved artificial potential field method for solving local minimum problem," in 2011 2nd International Conference on Intelligent Control and Information Processing, 2011, vol. 1: IEEE, pp. 420-424.
[14]Liang, X., X. Qu, N. Wang, Y. Li, and R. Zhang, "A novel distributed and self-organized swarm control framework for underactuated unmanned marine vehicles," (in English), Ieee Access, vol. 7, pp. 112703-112712, 2019.
[15]Liu, C., L. Zhai, and X. Zhang, "Research on local real-time obstacle avoidance path planning of unmanned vehicle based on improved artificial potential field method," in 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), 2022: IEEE, pp. 1-6.
[16]Mallios, A., P. Ridao, E. Hernández, D. Ribas, F. Maurelli, and Y. Petillot, "Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar," in Oceans 2009-Europe, 2009: IEEE, pp. 1-6.
[17]Mallios, A., P. Ridao, D. Ribas, F. Maurelli, and Y. Petillot, "EKF-SLAM for AUV navigation under probabilistic sonar scan-matching," in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010: IEEE, pp. 4404-4411.
[18]Neves, G., M. Ruiz, J. Fontinele, and L. Oliveira, "Rotated object detection with forward-looking sonar in underwater applications," (in English), Expert Systems with Applications, vol. 140, p. 112870, 2020.
[19]Noguchi, Y. and T. Maki, "Path planning method based on artificial potential field and reinforcement learning for intervention AUVs," in 2019 IEEE Underwater Technology (UT), 2019: IEEE, pp. 1-6.
[20]Solari, F. J., A. F. Rozenfeld, S. A. Villar, and G. G. Acosta, "Artificial potential fields for the obstacles avoidance system of an AUV using a mechanical scanning sonar," in 2016 3rd IEEE/OES South American International Symposium on Oceanic Engineering (SAISOE), 2016: IEEE, pp. 1-6.
[21]Tang, L., S. Dian, G. Gu, K. Zhou, S. Wang, and X. Feng, "A novel potential field method for obstacle avoidance and path planning of mobile robot," in 2010 3rd international conference on computer science and information technology, 2010, vol. 9: IEEE, pp. 633-637.
[22]Wang, S.-M., M.-C. Fang, and C.-N. Hwang, "Vertical obstacle avoidance and navigation of autonomous underwater vehicles with H∞ controller and the artificial potential field method," (in English), The Journal of Navigation, vol. 72, no. 1, pp. 207-228, 2019.
[23]Wu, T.-H., S.-M. Wang, and W.-W. Chang, "Design and Image Tracking Control of Underwater Manipulator," in 2023 5th International Conference on Control and Robotics (ICCR), 2023: IEEE, pp. 210-214.
[24]Zhu, D., C. Tian, B. Sun, and C. Luo, "Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm," (in English), Journal of Intelligent & Robotic Systems, vol. 94, pp. 237-249, 2019.
[25]黃邦毓,應用 LabVIEW 進行前向聲納影像重建與環境建構之研究, 國立成功大學系統與船舶機電工程學系碩士論文,2022.
[26]穆凌吉, "基於人工勢場法發展操船模擬器之自主避碰模組," (in Chinese), 中國造船暨輪機工程學刊, vol. 39, no. 4, pp. 7-14, 2020.