本研究針對自主式水下載具（Autonomous Underwater Vehicle, AUV）旨在建構一套具備模組化架構與實體部署能力之智慧型水下控制平台，作為未來應用於近岸環境監測、自主導航與多載具編隊任務之基礎載台。

研究首先基於六自由度運動理論推導載具之數學模型，並設計Planar Motion Mechanism（PMM）拖航試驗以獲取水平面三自由度運動下之水動力反應數據。為克服傳統建模對流場與阻力參數高度依賴之限制，進一步導入徑向基底函數（Radial Basis Function, RBF）神經網路進行水動力係數辨識與動態模型學習，建立一套具泛化能力之資料驅動預測架構。

硬體整合方面，本研究實作雙主控電子系統（Arduino與Raspberry Pi），整合推進器、舵面伺服機、壓力感測器（MS5837）、慣性測量單元（IMU）、多普勒速度儀（DVL）、全球定位系統（GPS），以及Wi-Fi與RFD900等多通訊模組，實現即時訊號接收、指令控制與資料回傳之完整流程。載具採模組化設計，並完成電子艙體、推進裝置與通訊模組之防水與耐壓測試，同時根據浮心與重心原則進行浮力配置與配重調整，確保靜態與動態之姿態穩定性。

系統驗證階段，執行靜水測試、浮沉觀察與姿態回復實驗，並藉由SolidWorks模擬與靜態吊掛試驗驗證重浮心對齊結果。另分析感測器資料輸出之同步性與準確性，確認IMU、DVL、壓力計與GPS之即時性符合水下載具導航控制需求。RBF模型預測結果與實測數據誤差相符，呈現良好之收斂與追蹤能力。

綜合各項驗證結果，本研究所建構之平台具備良好之系統整合度、感測穩定性與控制精度，並可靈活因應不同任務需求進行模組替換與功能擴充，為水下載具自主化與部署應用提供一套具備實用性與可行性之平台架構。

This study presents the design and implementation of a modular and deployable control platform for Autonomous Underwater Vehicles (AUVs), aimed at supporting missions such as environmental monitoring, autonomous navigation, and formation control. The research integrates dynamic modeling, data-driven identification, and hardware realization into a unified system. A six-degree-of-freedom motion model is derived, while Planar Motion Mechanism towing tests are performed to capture hydrodynamic responses in the horizontal plane. To address limitations of traditional empirical approaches, Radial Basis Function neural networks are employed to identify hydrodynamic coefficients and approximate system dynamics. On the hardware side, a dual-controller architecture combining Arduino and Raspberry Pi integrates thrusters, servo actuators, pressure and inertial sensors, a Doppler Velocity Log, GPS, and wireless communication modules. Waterproofing, pressure resistance, and buoyancy balance are tested to ensure stability. Experimental validation includes still-water trials, submergence tests, and attitude recovery assessments, with results confirming reliable sensor synchronization and accurate real-time performance. The RBF-based model achieves strong predictive accuracy with good agreement to measurements. Overall, the developed AUV platform demonstrates robust sensing stability, control precision, and adaptability, providing a practical foundation for future underwater autonomy and mission-oriented applications.

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