臺灣博碩士論文加值系統

本論文主要為發展ㄧ結合機械視覺與兩軸機械臂之人臉軌跡追蹤系統。以個人電腦為基礎，並結合研華的PCI-1784 Encoder卡、PCI-1720U D/A卡和彩色數位CMOS攝影機，並應用Borland C++ Builder 6.0來撰寫系統的控制軟體。首先，利用機械視覺抓取移動中的人臉影像，經由影像前處理之後，再利用人臉辨識系統去找尋人臉中心位置空間座標點，經由逆向運動學計算出兩軸機械臂必須移動之角度，且發展一新的強健控制器以計算出兩軸所要驅動電壓，最後藉由馬達驅動器驅使兩軸機械手臂，達到人臉軌跡跟隨的目的。在控制器設計方面，本文利用一類神經模糊網路系統以估測機械手臂結構和非結構的不確定性量，再設計一監督式控制器抑制近似誤差對軌跡跟隨的影響，並利用Lyapunov理論，推導出適應性的網路參數調整法則，以確保軌跡跟隨的穩定性。最後以實際的兩軸電驅動機械臂，實驗證明本文所提出方法之可行性。

This thesis develops a trajectory tracking control system of robot manipulators including actuators using machine vision. This control system is based on a personal computer equipped with a PCI-1784 encoder card, a PCI-1720U D/A card and a color CMOS. The control program is written by Borland C++. In the image process, the face image is firstly captured by the color CMOS. Nest, we use skin-color identification and an elliptic mask searching method to look for the center of the face in the image. Through reverse kinematics, this center coordinate is transferred to the desired tracking degrees of the two-axis manipulator. Moreover, we develop an adaptive neural fuzzy network controller to complete a good-performance trajectory tracking for the robot manipulator. In this control scheme, a neural fuzzy network (NFN) is constructed to online estimate the system uncertainties with the parameter learning phases. A supervisory controller is used to attenuate the effects of the approximation error on the tracking error, and adaptive tuning laws of network parameters are derived in the sense of the Lyapunov synthesis approach to ensure network convergence. Experiments performed on a two-link robot manipulator driven by DC servomotors demonstrate the effectiveness of our scheme.

封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
致謝 vi
目錄 vii
圖目錄 ix
表目錄 x
符號說明 xi

第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究方法 2
1.4 文獻回顧 3
1.5 論文結構 5
第二章 兩軸機械臂系統硬體架構介紹 6
2.1兩軸機械臂硬體系統架構 6
2.2控制流程 16
第三章 機械視覺軌跡跟隨控制器設計 18
3.1電驅動機械手臂的數學模型 18
3.2問題描述 20
3.3 類神經模糊系統介紹 22
3.4 強健控制器的設計 25
3.5 穩定性證明 35
第四章 機械視覺跟隨控制流程 37
4.1 色彩空間轉換和膚色判別 38
4.2 人臉偵測 40
4.3影像空間與機械臂空間座標的轉換 41
4.4機械臂運動學 43
4.4.1順向運動學 45
4.4.2逆向運動學 47
第五章 控制系統模擬與實驗 49
5.1 機械視覺實驗 49
5.1.1 膚色判別 49
5.1.2 人臉收尋 51
5.2 控制器模擬 53
5.2.1 PD控制器 55
5.2.2類神經模糊控制器 60
5.2.3 模擬結果 65
5.3控制器實驗 66
第六章 結論 74
參考文獻 76

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