本論文旨在結合腕關節型機器人與立體視覺系統，建構一個以影像為基礎的拍擊系統。選用腕關節型機器人的原因在於它的自由度與構造相似於人類的手腕，運動上有高度的靈巧性，由於對稱且並聯式的架構，使其具有較高的負載承受能力。立體影像視覺系統中，利用兩個影像感測器模擬人類雙眼，依據拋體的色彩資訊進行目標物擷取，利用擴展卡門濾波器(extended Kalman filter)針對拋體的位置與速度進行估測，並利用質點拋體運動學對球體落點與速度進行預測，以供機器人決定拍擊球體的時機、位置、速度與姿態。在實作上，本系統利用以場效可程式邏輯閘陣列(field-programmable gate array, FPGA)為基礎之開發板做為影像處理核心，並使用硬體描述語言Verilog撰寫影像處理演算法，以數位訊號處理器做為控制核心並使用C語言撰寫擴展卡門濾波器、馬達控制器等演算法。最後，影像視覺系統可以準確地估算落點資訊，並由腕關節型機器人完成拍擊的動作，且能持續拍擊使球回彈至固定高度，其連續拍擊成功率可達150下以上。

The main objective of this thesis is to design and implement a vision-based paddle juggling system using a stereo vision system and a carpal wrist robot. The carpal wrist robot is chosen because of its superior dexterity and similarity to the human wrist and it has three degree of freedom. As a result of its symmetric parallel architecture, it has a large payload capacity. In the stereo vision system, two image sensors are used to provide stereo vision. The stereo vision system is able to track the ball according to its color information. An extended Kalman filter is then used to estimate the dynamics of the ball and predict its future trajectory and velocity. Based on the dynamics of the ball estimated by the extended Kalman filter, the carpal wrist robot can determine the timing, velocity, position and posture for paddling the ball. Image processing algorithms are implemented on an FPGA-based board through Verilog hardware description languages. The extended Kalman filter and robot controller are implemented on digital signal processors. Through experiments, the ball can be tracked by the stereo vision system, and the carpal wrist robot can continue to paddle the ball and the ball is maintained at set height with at least 150 successful strokes.

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