本研究提出視覺式泊塢系統(Visual-base Docking System, VDS)，整合視覺導航方法、智慧物件識別方法與深度強化學習控制方法，實現於自主水下載具(Autonomous Underwater Vehicle, AUV)，同時克服自主水下載具與泊塢裝置之商源、預算等高門檻問題，完成自主設計與在地化製作自主水下載具(MateLab AUV)與可變資訊塢架(Variable Information MateLab Dock, MateLab VID)。VDS藉由智慧物件識別(You Only Look Once ver.7, YOLOv7)以及深度強化學習之深度確定性策略梯度(Deep Deterministic Policy Gradient, DDPG)控制方法，將AUV高度耦合之非線性系統轉化成能即時感知、分析以及處理環境信息，具有高適應性、強健性、抗干擾以及高響應速度特性的視覺化運動控制系統。於國立成功大學系統系拖航水槽實施VDS實驗，分別進行模糊控制結合智慧物件識別方法(Mode I)、DDPG控制結合智慧物件識別方法(Mode II)之實驗與分析比較。Mode II實驗結果之潛航與定深控制性能之深度指標IAE_h=52.449、MAE_h=0.403，視覺空間控制性能指標IAE_p=1994.74、MAE_p=76.721，Mode II相較Mode I在深度控制指標MAE_h以及縱搖角性能指標MAE_θ有更好的表現。數據顯示視覺式泊塢系統(Visual-base Docking System, VDS)具有視覺導航控制方法驗證平台之重現性、可控性與高度可靠性。

This study introduces the Visual-based Docking System (VDS) which integrates visual navigation methods, intelligent object recognition techniques, and deep reinforcement learning control methods. The system is implemented on an Autonomous Underwater Vehicle (AUV) and addresses challenges related to the proprietary nature and high budgetary constraints associated with AUVs and docking equipment. As a result, a locally-produced and uniquely designed AUV (MateLab AUV) and a Variable Information MateLab Dock (MateLab VID) have been developed. The VDS employs Intelligent Object Recognition using YOLOv7 (You Only Look Once version 7) and the Deep Deterministic Policy Gradient (DDPG) control method. It transforms the highly-coupled non-linear systems of the AUV into a visual motion control system capable of real-time perception, analysis, and environmental data processing. This system demonstrates high adaptability, robustness, interference resistance, and swift response characteristics. Experiments with the VDS were conducted in the towing tank of SNAME, NCKU. Comparisons were made between the results of Fuzzy Logic Control (FLC) combined with intelligent object recognition (Mode I) and DDPG control combined with intelligent object recognition (Mode II). Compared to Mode I, Mode II displayed better performance in depth control indicators MAE_h and pitch performance indicators MAE_θ. The data suggests that the VDS offers reproducibility, controllability, and high reliability as a platform for verifying visual navigation control methods.

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