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研究生： 羅國益
Luo, Guor-Yieh
論文名稱： 基於視覺之工業用機械手臂物件取放作業研究
Study on Vision-Based Object Pick-and-Place Tasks of Industrial Manipulator
指導教授： 鄭銘揚
Cheng, Ming-Yang
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 113
中文關鍵詞： 機器視覺系統 、視覺系統校正 、雙眼立體視覺 、物體識別
外文關鍵詞： Machine vision system, Calibration, Stereo vision, Object recognition
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    • 現今許多先進國家逐漸受到人口老化與人口衰減衝擊，面臨勞工短缺以及勞動力成本上漲等問題。因此為了解決上述問題，產業界陸續採用機械手臂與工業自動化系統來減少成本並增加生產率。然而在進行較複雜的加工任務或物件取放任務中，機械手臂經常難以獨自完成。解決前述困難之可能解決方案為導入機器視覺系統以提供機械手臂視覺功能，因此本論文主要目的為研究與開發基於視覺之自動化物件取放系統。普遍上，用於自動化取放系統之機器視覺可分為兩種不同的組態－眼在手攝影機組態與眼對手攝影機組態。兩者差異在於，眼在手攝影機組態使用單一攝影機，且安裝於機械手臂的末端工具中心點上；而眼對手攝影機組態使用雙眼立體攝影機，並擺置在一固定位置上。此兩組態之架構可分為三個主要部份進行深入探討－機器視覺系統之校正、物體識別、以及物件座標系的轉換。實驗結果顯示本論文所開發之眼在手自動化物件取放系統與眼對手自動化物件取放系統都可成功地完成物件取放任務。此外，實驗結果也顯示眼在手系統擁有較高的精準度，但卻不易成功識別三維物件；相反地，眼對手系統較容易進行三維物件識別，但精準度卻相對較低，且需事先進行較複雜之系統校正與加工物件模型建立。

    Nowadays, because of population aging and population decline, labor force has become insufficient and labor cost keeps rising up. Thus, most manufacturers adopt industrial automation systems with robotic arms in order to reduce costs and increase productivities. However, when dealing with complicated tasks, such as object pick-and-place, it is harder for robotic arms alone to complete them. One of the possible solutions to overcoming the aforementioned difficulties is to introduce machine vision into the robotic arm system. This thesis focuses on the development of vision-based automatic pick-and-place systems. Generally, there are two types of machine vision configuration – eye-in-hand and eye-to-hand. For the eye-in-hand configuration, a camera is attached on the tool center point of a robotic arm. For the eye-to-hand configuration, a stereo camera is placed on a fixed position. The structures of both configurations are similar and can be divided into three main sections – the calibration of machine vision system, object recognition, and transformations of object coordinates. Experimental results indicate that both the systems with eye-in-hand and eye-to-hand configurations can successfully perform automatic pick-and-place tasks. Furthermore, experimental results also indicate that the system with eye-in-hand has a higher accuracy, but is harder to recognize 3D objects. On the contrary, the system with eye-to-hand is easier to recognize 3D objects, but has a lower accuracy and requires more training and calibration.

    ENGLISH ABSTRACT I 中文摘要 II ACKNOWLEDGEMENT III TABLE OF CONTENTS IV LIST OF TABLES VIII LIST OF FIGURES IX CHAPTER 1 INTRODUCTION 1 1.1 RESEARCH MOTIVATION AND PURPOSE 1 1.2 LITERATURE REVIEW 2 1.3 STRUCTURE OF THE THESIS 6 CHAPTER 2 PINHOLE CAMERA MODEL AND BINOCULAR STEREO VISION 7 2.1 PINHOLE CAMERA 7 2.1.1 Structure of Camera 7 2.1.2 Pinhole Camera Model 8 2.1.3 Intrinsic Matrix 10 2.1.4 Extrinsic Matrix 13 2.1.5 Distortion Coefficients 16 2.2 BINOCULAR STEREO VISION 19 2.2.1 Structure of Binocular Stereo Vision 19 2.2.2 Image Rectification 20 2.2.3 Depth Estimation Based on Disparity 21 CHAPTER 3 CALIBRATION OF MACHINE VISION SYSTEM 24 3.1 DEFINITION OF TRANSFORMATION MATRIX 24 3.2 CALIBRATION RIG 27 3.3 CAMERA CALIBRATION 29 3.3.1 Basic Concept of Camera Calibration 29 3.3.2 The Procedure of Camera Calibration 31 3.4 STEREO CALIBRATION 32 3.4.1 Basic Concept of Stereo Calibration 32 3.4.2 The Procedure of Stereo Calibration 34 3.5 HAND-EYE CALIBRATION 35 3.5.1 Eye-in-Hand Calibration 36 3.5.2 Eye-to-Hand Calibration 37 CHAPTER 4 OBJECT RECOGNITION AND TRANSFORMATIONS OF OBJECT COORDINATES 39 4.1 OBJECT RECOGNITION 39 4.1.1 Interest Point Detection 40 4.1.1.1 SIFT 41 4.1.1.2 SURF 43 4.1.1.3 Canny 46 4.1.1.4 Harris 48 4.1.1.5 FAST 51 4.1.2 Interest Point Descriptor 52 4.1.2.1 SIFT Interest Point Descriptor 53 4.1.2.2 SURF Interest Point Descriptor 54 4.1.3 Match Point Error Elimination 55 4.1.3.1 Eliminate Match Point Error by RANSAC 55 4.1.3.2 Eliminate Match Point Error by Disparity Vector 57 4.1.3.3 Eliminate Match Point Error by Depth 58 4.1.4 Recognition of the Model 58 4.2 TRANSFORMATIONS OF OBJECT COORDINATES 59 4.2.1 Coordinate Transformations for Eye-in-Hand Configuration 60 4.2.2 Coordinate Transformations for Eye-to-Hand Configuration 62 CHAPTER 5 VSCLAB MACHINE VISION SYSTEM SOFTWARE PROGRAM 64 5.1 BASIC CONCEPT OF OBJECT-ORIENTED DESIGN 64 5.1.1 Classification 64 5.1.2 Inheritance 65 5.1.3 Dynamic Polymorphism 66 5.2 VSCLAB MACHINE VISION SYSTEM 67 CHAPTER 6 EXPERIMENTAL SETUPS AND RESULTS 72 6.1 EXPERIMENTAL SETUPS 72 6.1.1 6-Axis Robotic Arms 72 6.1.2 Cameras and Lenses 73 6.1.3 Gripper and Nozzle 74 6.1.4 Target Objects 76 6.1.5 Environmental Setup for Eye-in-Hand Configuration 77 6.1.5.1 Setup of Single Eye-in-Hand Camera 78 6.1.5.2 Structure of Single Camera Holder 79 6.1.5.3 Experimental Platform 80 6.1.6 Environmental Setup for Eye-to-Hand System 80 6.1.6.1 Setup of Stereo Camera 81 6.1.6.2 Structure of Stereo Camera Holder 82 6.1.6.3 Experimental platform 84 6.2 EXPERIMENTAL RESULTS 85 6.2.1 Camera Calibration Results 86 6.2.2 Stereo Calibration Results 89 6.2.3 Hand-Eye Calibration Results 93 6.2.4 Object Recognition Results 96 6.2.4.1 2D Object Recognition Results 96 6.2.4.2 3D Object Recognition Results 99 6.2.5 Object Pick-and-Place Results 104 6.2.6 Comparison of the two machine vision systems 106 CHAPTER 7 CONCLUSION AND FUTURE WORKS 108 7.1 CONCLUSION 108 7.2 FUTURE WORKS 109 REFERENCES 110

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