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研究生:郭佳文
研究生(外文):Chia-Wen Kuo
論文名稱:機器人整合3D物體辨識與夾取系統應用於工廠自動化
論文名稱(外文):Robot Integrated 3D Object Recognition and Fetching System for Factory Automation
指導教授:羅仁權羅仁權引用關係
口試委員:張帆人陳金聖
口試日期:2015-07-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:91
中文關鍵詞:工廠自動化生產自動化機器手臂三維物體辨識物件抓取
外文關鍵詞:factory automationmanufacturing automation3D object recognitionobject fetching
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  • 被引用被引用:3
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目前工廠自動化發展一個重大的瓶頸,在於機器視覺,機器人沒辦法像產線上的工人一樣,能夠快速且精確的辨識出從輸送帶運輸過來,任意擺放的複雜物件,既然無法辨認出物件,那就更遑論如何將物體抓取起來,進行後續的操作像是組裝、焊接、塗膠等等操作。
為了解決這個問題,目前大多數的廠商是想辦法將物件的種類和位置固定,捨去辨識場景中物體的步驟,轉而依賴機器手臂高精準度的特性,在已知位置之間來回運動和操作。這樣一來一但物件位置有偏差,就會導致災難性的後果,而且也白白花費了高額成本在精準地擺放物體。
因此本研究的主題,在於如何將三維物體辨識整合到這個系統中,讓機器手臂可以自動辨識場景中要操作的物體,並且結合直覺性教導的功能,來事先教導手臂如何正確且穩定的抓取辨識出來的物件,當中最重要的兩個模組,分別是物體辨識,以及機器手臂本身的控制。
在本研究中,我們成功的實作了一個整合式的系統,來達成物體自動辨識與抓取,我們也對物體辨識和抓取進行大量的測試,並識別出整個系統的瓶頸,以利後續研究者能夠基於此系統繼續發展。


One of the bottlenecks for manufacturing automation is machine vision. Robots are not able to recognize randomly oriented components coming from the assembly line quickly and accurately just like human operators do. Once this very first step fails, any other subsequent operations such as picking up the component, assembling, welding, painting, etc, are impossible.
Currently, manufacturers solve this problem by fixing the component. The robot arm then performs the task and manipulates the component based on this precondition. This approach totally omits the fragile object recognition step and relies solely on the precision and repeatability of the robot arm. Once there are pose error setting up the component, a disastrous consequence may occur and the whole manufacturing process might shutdown simply because of this minor fault.
The research objective is to integrate 3D model-based object recognition into the system for the capability of the robot arm to recognize the component in the scene. Furthermore, teaching by touching is integrated to let human operators teach the robot how to pick up the components stably. Two of the most important modules for the success of this integrated system are 3D object recognition system and the manipulator itself.
In this research, we successfully implement an integrated system for recognizing and fetching the randomly oriented objects. We also evaluate the system extensively and identify the bottleneck of this system, hoping that this could open up a road for robot-integrated manufacturing automation and become the basis for future research.


口試委員會審定書 #
誌謝 i
中文摘要 i
ABSTRACT i
TABLE OF CONTENTS ii
LIST OF FIGURES i
LIST OF TABLES i
Chapter 1 Introduction 1
1.1 History 1
1.1.1 Traditional industrial robot arms 1
1.1.2 Lightweight collaborative robot 2
1.1.3 Dual arm robot 3
1.2 Industrial Applications 4
1.2.1 Material handling 5
1.2.2 Welding 6
1.2.3 Assembly 6
1.3 Challenges 7
1.3.1 Machine vision 7
1.3.2 Object fetching 8
1.4 Research Objectives 8
1.5 Thesis Structure 9
Chapter 2 Scenario 10
2.1 Experimental Setup 10
2.1.1 Scene 10
2.1.2 Tested parts 10
2.2 Procedures 11
2.3 Preconditions 11
2.3.1 Structured environment 12
2.3.2 Finite set of known objects 12
2.3.3 Reliable object segmentation 13
2.3.4 No mutual occlusion 13
2.3.5 Rigid bodies 14
Chapter 3 System Architecture 15
3.1 Generalized Robot Fetching Architecture 15
3.2 Proposed Robot Fetching Architecture 16
3.3 Key Modules 17
3.3.1 2.5D sensors 17
3.3.2 Object recognition 21
3.3.3 Operation database 22
3.3.4 Trajectory interpolator 22
Chapter 4 Manipulator 23
4.1 Mechanism 23
4.1.1 D-H parameters 24
4.1.2 Transmission and actuator 26
4.1.3 Gripper 28
4.2 Control Architecture 30
4.3 Software Architecture 32
4.3.1 Motivation 33
4.3.2 Hardware layer 33
4.3.3 Basic utility 34
4.3.4 Application layer 34
4.3.5 Timer 35
4.4 Realtime Linux 36
4.4.1 Introduction 36
4.4.2 Dual Kernel 37
4.4.3 Kernel Patch 38
4.4.4 Kernel Configuration 39
4.4.5 Test Results 40
4.5 Manipulator Functionalities 42
4.5.1 Teaching by Touching 42
4.5.2 Online Trajectory Generation 46
Chapter 5 Object Recognition 50
5.1 Point Cloud Library (PCL) 50
5.2 Global Recognition Pipeline 51
5.3 Database Generation 52
5.4 Pre-Processing 54
5.4.1 ROI segmentation 55
5.4.2 Down-sampling 55
5.4.3 Denoising 56
5.4.4 Plane segmentation 57
5.4.5 Clustering 57
5.5 Object type recognition 58
5.5.1 Global descriptors 58
5.5.2 Descriptor estimation 59
5.5.3 Matching 64
5.6 Object pose estimation 65
5.7 Post-Processing 66
5.7.1 Iterative closest points (ICP) 66
5.7.2 Hypothesis verification 67
Chapter 6 Operation Database 68
6.1 Database Generation 68
6.1.1 Grasps 68
6.1.2 Trajectory via-points 71
6.2 Database Structure 72
6.3 Operation Selection 73
Chapter 7 Experiments 74
7.1 Object Recognition 74
7.1.1 Precision 74
7.1.2 Success rate 75
7.1.3 Time consumption 75
7.2 Object Fetching 75
7.2.1 Grasp selection 76
7.2.2 Grasp precision 76
Chapter 8 Results and Discussions 77
8.1 Object Recognition 77
8.1.1 Results 77
8.1.2 Precision 77
8.1.3 Success rate 78
8.1.4 Time consumption 79
8.2 Object Fetching 80
8.2.1 Results 80
8.2.2 Grasp selection 81
8.2.3 Grasp precision 81
Chapter 9 Conclusion and Future Works 84
REFERENCE 85
VITA 91

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