博碩士論文 109327003 詳細資訊




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姓名 陳顥壬(Hao-Ren Chen)  查詢紙本館藏   畢業系所 光機電工程研究所
論文名稱 整合深度學習與立體視覺之六軸機械手臂夾取系統開發
(Development of a six-axis robotic arm gripping system integrating deep learning and stereo vision)
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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]   至系統瀏覽論文 ( 永不開放)
摘要(中) 本研究使用Linux中的Ubuntu18.04版本作業系統的環境下透過機器人作業系統(Robot Operating System, ROS)開發軟體系統,經由ROS點對點網路與其分散式架構將所有資訊進行資料傳遞並整合工業用IPC、六軸機械手臂、Ensenso深度相機以及自適應夾爪,實現軟、硬體協同的設計。

本論文的任務目標在於藉由導入影像視覺系統,開發一種不需要使用CAD模型匹配的辨識夾取策略,其中整合了二維與三維點雲資料,透過深度學習網路檢測目標物以及對目標物點雲進行最佳六自由度(6 Degrees of Freedom, 6DoF)夾取姿態估計,並在機構限制下利用逆向運動學控制六軸機械手臂對四種不同的目標物在工作區域內隨意平放的情況下執行夾取與分類任務,夾取與分類任務的成功率為86%,成果顯示本論文確實能成功建立一套物件辨識與夾取分類系統。
摘要(英) Robot operating system (ROS) is used to develop a software system under the Ubuntu 18.04 version of Linux environment in this study. The industrial IPC, the robot arm, the binocular structured light camera and the grippers are integrated by ROS distributed architecture and peer-to-peer network, and all information and data collected can be transferred to them as well. Therefore, the collaborative design is used to realize the integrated software and hardware.

The main purpose of this paper is to develop a recognition and gripping strategy that does not require CAD model matching through the introduction of the depth image vision system, to identify the target through the neural network and automatically generate a six-degree-offreedom (6DoF) gripping pose for the object, and the success rate of gripping and sorting is 86% under the condition that the six-axis robot arm is controlled to place four different objects in the working area at will under the limitation of the mechanism. The results show that this paper can indeed successfully establish a set of object recognition and gripping system.
關鍵字(中) ★ 夾取姿態估計
★ 目標檢測
★ 深度學習
★ 六軸機械手臂
★ ROS
★ 運動學
★ 座標轉換
關鍵字(英) ★ Grasp pose estimation
★ Object detection
★ Deep learning
★ 6 DoF robotic arm
★ ROS
★ Kinematics
★ Coordinate transformation
論文目次 摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 X
符號對照表 XII
第1章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.2.1 抓取檢測系統 2
1.2.2 抓取規劃和控制系統 5
1.3 論文目標 6
1.4 論文架構 7
第2章 軟體介紹與理論說明 8
2.1 軟體介紹 8
2.1.1 ROS簡介 8
2.1.2 Moveit套件介紹 11
2.1.3 點雲庫(Point Cloud Library,PCL) 12
2.2 理論說明 13
2.2.1 YOLOv4物件偵測網路 13
2.2.2 點雲夾取姿態估計 15
第3章 系統架構與研究方法 20
3.1 系統架構 20
3.2 硬體規格介紹 21
3.3 二維影像的物件偵測與辨識 25
3.3.1 目標物資料集與標註 26
3.3.2 模型訓練參數設定以及模型訓練 28
3.3.3 YOLOv4目標物偵測 34
3.4 點雲處理 35
3.4.1 紋理映射與紋理點雲切割 35
3.4.2 點雲前處理 38
3.5 目標物夾取姿態估計 42
3.5.1 定義與採樣夾取候選對象 43
3.5.2 夾取候選對象評估 44
3.5.3 夾取姿態修正 46
第4章 機械手臂運動學與應用 49
4.1 正向運動學 50
4.2 逆向運動學 52
4.3 手眼標定 54
4.4 機器人作業系統(ROS)整合應用 58
4.4.1 ROS節點功能介紹 59
4.4.2 實驗節點流程概述 63
第5章 實驗結果 65
5.1 機械手臂抓取系統的工作環境 65
5.2 夾取姿態修正驗證實驗 66
5.2.1 夾取姿態修正驗證實驗成功率評估 68
5.2.2 夾取姿態修正分析 69
5.3 物件辨識夾取實驗 71
5.3.1 物件辨識分類抓取成功率評估 74
第6章 結論及未來展望 77
6.1 論文結論 77
6.2 未來展望 77
參考文獻 79
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指導教授 陳怡呈(Yi-Cheng Chen) 審核日期 2023-2-1
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