臺灣博碩士論文加值系統

現今自動化檢測流程大都透過機構或振動盤的方式讓料桶中的零件轉成特定方位，再利用工業相機進行定位或人工排列的方式將零件放置至特定位置，以利機械手臂將零件夾至檢測區域，但此方式需針對不同零件來進行調整且浪費人力，為克服此問題，本研究開發「自動化隨機拾取與瑕疵檢測系統」，以減少手臂夾取前的前置處理工作，並搭配表面影像檢測，讓自動化檢測流程更加彈性。
本研究主要分成三大部分：零件隨機拾取、檢測視角決定及零件瑕疵檢測，其中“零件隨機拾取”是利用3D點資料演算法分析料桶內所有零件的點雲資料，尋找適當的零件及適合機械手臂吸取該零件的位置，並使用EPSON機械手臂將零件以特定方位放置於檢測區域；“檢測視角決定”是以零件整體外觀來判定該零件適合以哪些基本視角（上、下、左、右、前、後）進行檢測；“零件瑕疵檢測”是使用零件外觀的2D影像來偵測刮痕及凹洞，以決定產品是否為良品。
本論文除了說明隨機拾取及瑕疵檢測所使用到的演算法外，亦針對多個不同幾何特性的產品來測試隨機拾取的實用性，而瑕疵檢測因須針對零件特性開發不同演算法，故僅針對華接頭進行測試。

The typical process of automatic inspection for defective parts usually utilizes some mechanism to obtain specific orientations of the parts in order for a robotic arm to grab the parts to an inspection area. Such a process is time consuming and is hard to fit the need of different parts. In order to reduce the pre-processing time of robot grasping, this thesis studies the issue of automatic bin picking for random placement of parts. Automatic defect inspection based on surface images of the parts is also undertaken to make the inspection work more flexible.
This study consists of the following three research issues: automatic random bin picking of parts, automatic determination of inspection views and automatic defect inspection. The first issue “automatic random bin picking of parts” discusses the development of several algorithms using the 3D data points obtained by scanning the whole parts placed in a tank. The algorithms are used to automatically find the orientation of each part in the tank. With the orientations of all parts, the system automatically decides the part to be picked and it’s appropriate grasping position for a robot arm. After that, an EPSON robot arm is used to move the part to an inspection zone. The second issue “automatic determination of inspection views” is the use of the part’s exterior appearance to determine which views (top view, bottom view, front view, back view, left view and right view) are proper for the part to be fully inspected. The last issue “automatic defect inspection” focuses on detecting the scratches and dents on a part’s exterior appearance based on the 2D image taken from the part.
In random bin picking, in addition to proposing solution algorithms, this study tests a number of parts with different geometric characteristics to prove the stability and applicability of the developed system. While for defect inspection, different algorithms should be developed for different parts, thus this study will only take thread pipe as an example for implementation.

摘要 II
目錄 III
圖目錄 VII
表目錄 XIII
第一章 緒論 2
1.1 研究動機與目的 2
1.2 研究方法 5
1.3 文獻探討 6
1.4 論文架構 12
第二章 實驗設備及系統架構介紹 13
2.1 實驗設備 17
2.1.1 3D結構光掃描 17
2.1.2 工業相機 18
2.1.3 EPSON機械手臂 19
2.1.4 機械手臂吸嘴 20
2.2 系統架構 21
2.2.1 系統運作流程 21
2.2.2 程式邏輯架構 24
第三章 點資料及影像處理之理論 27
3.1 掃描器與手臂座標轉換 27
3.2 3D點雲演算法 28
3.2.1 減採樣 28
3.2.2 叢聚法 30
3.2.3 快速特徵點直方圖 32
3.2.4 KD-Tree 35
3.3 2D影像演算法 41
3.3.1 ORB特徵點 42
3.3.2 Homography的原理 46
3.3.3 模糊化 48
3.3.4 銳利化 50
3.3.5 二值化 51
3.4 RANSAC的原理 52
3.4.1 RANSAC應用於Homography 53
3.4.2 RANSAC應用於3D點資料配對 54
第四章 隨機物件拾取 56
4.1 降低點雲密度 56
4.2 尋找較合適的群體 57
4.3 物件特徵估計與比對 61
4.4 吸取位置及方向 65
4.4.1 平面搜尋 65
4.4.2 在平面以亂數搜尋吸取位置 66
4.5 隨機拾取例外處理 68
4.5.1 點雲資料叢聚結果不佳 68
4.5.2 吸取與放置位置干涉 71
第五章 瑕疵檢測 78
5.1 檢測視角判定 78
5.2 物件二次定位 79
5.3 瑕疵檢測處理 81
5.3.1 尋找尺寸標記 81
5.3.2 切割不同光源方向區域 82
5.3.3 影像基本處理 83
5.3.4 瑕疵判斷 85
第六章 結論與未來研究方向 88
6.1 結論 88
6.2 未來研究方向 89
參考文獻 90
附錄 93
開發工具之簡介 93
Point Cloud Library（PCL） 93
Open CV Library 93
Epson Robot API 94
Basler Camera API 95
HP Pro S3 Scanner SDKs 95

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