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姓名	陳振榮(Zhen-Rong Chen)  查詢紙本館藏	畢業系所	光機電工程研究所
論文名稱	整合深度學習與機器視覺之微小電子連結器智慧整料系統開發 (Development of An Intelligent Material Handling System Integrating Deep Learning and Machine Vision for Micro Connector)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	隨著AI技術的進步，智慧化機器人技術已然成為趨勢，它能夠擁有如同人類般的感知能力，以便執行更加複雜的任務與目標，電子廠內，對於電子零件所導入的自動化產線已相當成熟，但對於少量多樣的電子連結器裝配任務，建置其自動化產線與振動盤相對耗費成本，故本研究旨於打造一套使用機械手臂、機器視覺及深度學習的散料夾取系統，以適應微小電子連結器的複雜特徵與環境，將其嵌入本系開發之類產線架構中，成功實現了彈性產線，解決自動化產線與振動盤的高昂成本及缺工問題。 本論文使用YOLOv8物件偵測模型，並以資料擴增(Data augmentation)的方法，以當前環境亮度±25%之光源進行訓練，進而提升穩健性，物件偵測模型會自動產生許多的候選框(Boundingbox)，透過Non Maximum Suppression(NMS)方法，濾除多餘的候選框，為了提升組裝成功率，本研究設計了用於整理微小電子連結器姿態的轉接治具，根據物件框選中心與影像質心位置計算其夾取之開口方向，由於組裝插件有方向性需求，故需以開口向上、下以及正反面，四種特徵設定其放料姿態，置放於轉接治具中，接著會等待轉盤的訊號收發，將轉接治具上的電子連結器夾持至插端子治具上，為了提升組裝準確率，亦開發了一套標定演算法與標定治具，嵌入於插入微小電子連結器的治具上，以判定組裝的插槽實際位置，完成後，透過IO訊號發送任務結束訊號給轉盤，以切換下一組治具，達成自動化彈性產線。
摘要(英)	With the advancement of AI technology, intelligent robotic system have become a fashion trend. These systems are capable of processing human-like perception, allowing them to perform more complex tasks and objectives. In electronic factories, the automation of production lines for electronic components has become quite mature. However, for the assembly tasks of a variety of electronic connectors in small quantities, the cost of building automated production lines and vibration feeders is quite high. Therefore, this study aims to develop a grasping system using robotic arms, machine vision, and deep learning to adapt to the complex features and environment of micro electronic connectors. This system is integrated into the production line framework developed in this study, successfully achieving a flexible production line and address the high costs of traditional automated lines and vibration feeders, as well as labor shortages. This paper uses the YOLOv8 object detection model and applies data argumentation techniques to train the model under varying lighting conditions with brightness adjustments, thereby enhancing robustness. The object detection model automatically generates multiple candidate bounding boxes. These are filtered by the Non-Maximum Suppression(NMS) method to remove redundant boxes. To improve the assembly success rate, this study designs an adapter fixture to arrange the pose of micro connectors. The gripper orientation is determined by calculating the direction of the opening based on the center of the bounding box and the image centroid. Since the assembly of the connectors requires a specific orientation, the object is placed with the opening facing up, down, or either the front or back, setting four distinct features for its pose within the adapter fixture. Then, the system waits for the signal from the rotary table to transfer the electronic connector from the adapter fixture to the insertion fixture. To enhance assembly accuracy, a calibration algorithm and calibration fixture are developed and integrated into the insertion fixture to determine the actual position of the assembly groove. Once completed, an IO signal is sent to the rotary table to confirm end the task, prompting the switch to the next fixture, thus achieving an automated, flexible production line.
關鍵字(中)	★ 微小電子零件辨識 ★ YOLOv8 ★ 機器視覺 ★ 六軸機械手臂 ★ ROS(Robotic Operation System) ★ 物件偵測夾取系統	關鍵字(英)	★ Recognition of micro connectors ★ YOLOv8 ★ Machine vision ★ Six-axis robot arm ★ ROS(Robotic Operation System) ★ Object detection grasping system
論文目次	目錄 摘要 I Abstract II 致謝 IV 目錄 V 圖目錄 VII 表目錄 X 第1章 緒論 1 1.1 研究背景 1 1.2 文獻回顧 1 1.2.1 物件偵測 1 1.2.2 物件夾取 3 1.3 研究動機與目的 6 第2章 系統架構 8 2.1 硬體規格 9 2.2 軟體介紹 11 2.2.1 機器人作業系統(Robot Operation System, ROS) 11 2.2.2 YOLOv8 13 第3章 研究方法 20 3.1 膠體散料偵測與姿態放置 20 3.1.1 資料集建立與標註 21 3.1.2 模型訓練參數設定 23 3.1.3 模型訓練 24 3.1.4 辨識結果處理 26 3.2 影像處理方法 28 3.2.1 灰階化 28 3.2.2 中值濾波 28 3.2.3 二值化 29 3.2.4 侵蝕 30 3.2.5 膨脹 30 3.2.6 邊緣偵測 30 3.2.7 最小外接矩形 32 3.3 手眼校正(Hand-Eye Calibration) 32 3.4 系統優化與整合 39 3.4.1 資料集前處理 41 3.4.2 流程設計與治具優化 41 3.4.3 組裝標定演算法 46 3.4.4 機器人作業系統(ROS)整合應用 48 第4章 實驗結果 51 4.1 YOLOv8模型辨識實驗 51 4.1.1 模型訓練指標比較 51 4.1.2 模型穩健性評估 55 4.2 散料組裝系統實驗 60 4.2.1 膠體整料速度與成功率評估 60 4.2.2 膠體組裝速度與成功率評估 63 4.2.3 整體組裝速度與成功率評估 66 第5章 結論與未來工作 69 5.1 結論 69 5.2 未來展望 69 參考文獻 71
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指導教授	陳怡呈(Yi-Cheng Chen)	審核日期	2025-2-25
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