臺灣博碩士論文加值系統

近年來，因開發中國家人力成本高漲且歐美各國欲將製造業移回本土的主客觀條件，自動化的議題再次受到重視。同時，由於製造業產品逐漸走向客製化「少量多樣」的趨勢，使得產線的彈性應變能力比起以往更為關鍵。在現今的工業機器人應用中，表面加工佔了約三分之一，但若未能事先取得待加工物的三維模型，便很難規劃運動軌跡而影響加工精準度。一般多是透過人工教導的方式解決，但此法不僅耗時，準確度也相當受限。現今雖有基於雷射的逆向工程法可供選擇，但其設備不僅昂貴且系統設計之複雜度也較高，因此本論文規劃整合立體視覺於機械手臂解決上述問題，提升應用之便利性。但不可諱言的，多數之待加工物其明顯之特徵點過少，此特性將使傳統匹配演算法產生誤匹配進而影響三維重建的精度。有鑒於此，本論文發展了一個基於單應性矩陣之立體匹配法來改善此問題。本論文首先藉由結合多種特徵點偵測法以增加待加工物偵測上的特徵點數量，並透過幾個誤匹配排除方法以確保特徵點匹配的正確性。之後由此些特徵點估測一強健的單應性矩陣，並以此單應性矩陣計算匹配結果然後進行深度估測與三維重建。最後，將此精確之三維模型應用於具有高使用彈性的六軸關節型機械手臂並進行一個表面加工作業，以此整合製造業常見的CAD/CAM流程於同一平台進而提高應用之便利性。實驗結果顯示以此方法重建的三維模型之準確性佳，充分驗證此方法應用於表面加工的可行性。

In recent years, with labor costs in developing countries rapidly increasing and a trend in the manufacturing industry of moving back to America and Europe, the topic of automation has once again become a hot issue. In the meantime, the growing trend of customized “small-volume large-variety production” indicates that the flexibility of production line is more critical than ever. Nowadays, surface machining applications account for about one-third of applications in industrial robots. In this field, if a 3D geometry model of objects for machining cannot be obtained in advance, trajectory planning will be achieved only with much difficulty and the machining precision will be diminished. Conventionally, teach-by-showing is a solution, but it is time-consuming and the results are limited. Although laser-based inverse engineering approaches are available, generally these approaches are complicated and their costs are high. Therefore, this thesis plans to integrate the stereo vision into industrial robots to solve those problems. However, it is doubtless that most objects for machining have few features, so traditional stereo matching algorithms will fall short in reconstructing the 3D models of these objects. In order to deal with the aforementioned problem, this thesis develops a homography based stereo matching algorithm. Firstly, this thesis increases the number of features on objects for machining by combining several algorithms and then filters out the miss matches. Based on the correct matches, a robust homography matrix is estimated. Moreover, the stereo matching result is calculated through this matrix. Consequently, depth estimation and 3D reconstruction will be conducted. Several experiments have been conducted to assess the performance of the proposed approach. In the experiment, the reconstructed 3D model is used in a surface machining task performed by a six-axis articulated robot. Experimental results indicate that the approach proposed in this thesis provides good reconstructed results so as to verify the effectiveness of the proposed approach in surface machining.

中文摘要 I
英文摘要　II
誌 謝 IX
目 錄 X
表目錄 XIII
圖目錄 XIV
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 本論文架構 5
第二章 三維重建的基本概念 6
2.1 攝影機模型 6
2.1.1 內部參數 7
2.1.2 外部參數 9
2.2 三維重建 10
2.2.1 影像矯正 11
2.2.2 利用視差估測深度資訊與三維資訊重建 12
第三章 基於單應性矩陣的立體匹配演算法 14
3.1 強健的特徵點偵測與匹配演算法 15
3.1.1 特徵點偵測 16
3.1.1.1 SIFT特徵點偵測演算法 16
3.1.1.2 SURF特徵點偵測演算法 18
3.1.1.3 Harris角點偵測演算法 22
3.1.1.4 FAST角點偵測演算法 24
3.1.2 特徵點描述子 26
3.1.2.1 SIFT特徵點描述子 26
3.1.2.2 SURF特徵點描述子 27
3.2 特徵點誤匹配排除法則 27
3.2.1 RANSAC演算法估測基本矩陣排除離群資料 28
3.2.2 視差向量排除誤匹配結果 31
3.2.3 以深度範圍排除誤匹配結果 32
3.3 估測單應性矩陣 32
3.3.1 相對應點分群 33
3.3.2 相對應點分布情形 34
3.3.3 估測單應性矩陣 36
第四章 座標系轉換與加工姿態 38
4.1 攝影機座標系與機械手臂基底座標系之轉換關係 38
4.2 加工姿態 40
第五章 實驗方法與實驗結果 43
5.1 實驗設備與設置 43
5.1.1 六軸關節型機械手臂 43
5.1.2 攝影機與鏡頭 44
5.1.3 目標加工物 45
5.1.4 實驗環境 45
5.2 實驗方法與結果 46
5.2.1 攝影機校正 46
5.2.2 特徵點分散程度與單應性矩陣估測誤差關係 48
5.2.3 三維重建結果分析與比較 50
5.2.4 座標轉換實驗與結果 53
5.2.5 表面加工實驗 56
5.2.6 加入姿態命令之表面加工實驗 59
第六章 結論與未來建議 61
6.1 結論 61
6.2 未來建議 61
參考文獻 63

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