整合光源控制與深度學習辨識之平放膠體散料夾取系統開發

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姓名

陳尚宏(Shang-Hong Chen) 查詢紙本館藏

畢業系所

光機電工程研究所

論文名稱

整合光源控制與深度學習辨識之平放膠體散料夾取系統開發
(Development of a flat lying plastic micro connector component picking system integrating light control and deep learning recognition)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

少量多樣的電子連接器產品製造和裝配需要大量的人力資源，但廠商面臨嚴重的缺工問題。因此，本研究開發一套取放料系統，使用六軸機械手臂進行膠體零件散料夾取，此系統結合機械手臂、光源控制、機器視覺以及深度學習等技術，分別使用兩種不同作業系統來實現系統的開發，使用 Windows 作業系統來完成環狀光源與控制器的整合，且基於 Linux Ubuntu18.04 版本的作業系統環境，透過機器人作業系統(Robot Operating System,ROS)開發軟體系統，經由 ROS 分散式的架構將實驗用電腦、六軸機械手臂、工業相機及自適應夾爪與深度學習、機器視覺進行軟硬體的整合，以達到散料辨識夾取系統的開發。
本論文首先透過基因演算法於調整光源參數，進而增強 YOLOv4 模型辨識膠體正反面的效果，並有效地解決了由於環境因素導致辨識率降低的問題，接著使用六軸機械手臂，將散料膠體依照其正反面準確的夾取至指定的治具上，目前此系統的成功率為94%，成果顯示本論文能夠成功克服原先合作廠商須採用在少量多樣或試產時尚未投入成本建置振動盤進行料件取料問題。

摘要(英)

Manufacturing and assembling a variety of electronic connector products in small quantities require a significant amount of human resources, yet manufacturers are grappling
with severe labor shortages. Hence, this research presents the development of an electronic connector materials handling system that employs a six-axis robotic arm for the precise retrieval of adhesive components. This study utilized two different operating systems for system
development. Windows operating system was employed for integrating the light source and controller, while the Linux Ubuntu 18.04 version operating system environment was used to develop the software system through the Robot Operating System (ROS). The computer, sixaxis robot arm, industrial camera adaptive gripper, and deep learning and machine vision technologies were integrated by ROS distributed architecture, ultimately achieving the development of a system for picking micro connector component.
This research employs genetic algorithm to optimize the parameters of the light source, followed by utilizing the YOLOv4 deep learning model to identify the front sides or back sides of the plastic component. Then use six-axis robotic arm to handle these materials with precision,
placing them onto designated fixtures. Currently, this system boasts a success rate of 94%. The results highlight the successful resolution of the challenge faced by collaborating manufacturers, eliminating the need to invest in vibration feeders for scenarios involving smallscale, diverse, or trial production.

關鍵字(中)

★ 微小電子零件辨識
★ 光源參數調整
★ YOLOv4
★ 基因演算法
★ 機器視覺
★ 六軸機械手臂
★ ROS

關鍵字(英)

★ Recognition of Tiny Electronic Components
★ Light source parameter tuning
★ YOLOv4
★ Genetic algorithm
★ Machine vision
★ Six-axis robot arm
★ Robot Operating System

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
符號對照表 XII
第1章緒論 1
1.1 研究背景 1
1.2 文獻回顧 1
1.2.1 機器視覺 1
1.2.2 物件夾取系統 5
1.3 研究動機與目的 6
1.4 論文架構 7
第2章系統架構 8
2.1硬體規格介紹 8
2.1.1 六軸協作型機械手臂 8
2.1.2 自適應夾爪 9
2.1.3 電腦規格 10
2.1.4 相機鏡頭設備 11
2.1.5 環狀光源與控制器 12
2.2 軟體介紹 14
2.2.1 機器人作業系統(Robot Operating System, ROS) 14
2.2.2 基因演算法 16
2.2.3 YOLO演算法 18
第3章研究方法 25
3.1 影像處理系統架構 25
3.1.1 影像灰階化與二值化 25
3.1.2 雜訊濾波 26
3.1.3 邊緣檢測 27
3.1.4 最小外接矩形 28
3.1.5 霍夫圓檢測 29
3.2光源參數調整系統架構 29
3.2.1 影像對比度檢測 30
3.2.2 光源控制器整合 31
3.2.3 基因演算法調整光源參數 32
3.3 膠體散料偵測與夾取點辨識 35
3.3.1 資料集與標註 36
3.3.2 模型訓練參數設定 38
3.3.3 YOLOv4模型訓練 40
3.3.4 夾取點辨識 42
3.4機械手臂運動系統 43
3.4.1 D-H參數 44
3.4.2 手臂運動學 45
3.4.3 手眼校正 48
3.4.4 ROS節點功能介紹 56
第4章實驗結果與討論 59
4.1光源參數調整實驗 59
4.1.1 對比度與深度學習模型關係 60
4.1.2 基因演算法參數調整實驗 61
4.2物件辨識夾取實驗 65
4.2.1 YOLOv4模型參數訓練 66
4.2.2 機械手臂放置膠體姿態實驗 68
4.2.3 膠體辨識取放成功率評估 71
第5章結論與未來工作 74
5.1 論文結論 74
5.2 未來展望 74
參考文獻 76

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指導教授

陳怡呈(Yi-Cheng Chen)

審核日期

2023-10-24

推文