簡易檢索 / 詳目顯示

回結果列表
研究生： 賴中山
Le - Trung Son
論文名稱： Visual Servoing for Object Manipulation
Visual Servoing for Object Manipulation
指導教授： 林其禹
Chyi-Yeu Lin
口試委員: 郭重顯
Chung-Hsien Kuo
邱士軒
Shih-Hsuan Chiu
林遠球
Lin Yuan Chiu
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 49
中文關鍵詞： 視覺伺服 、圖像對應 、位置預測 、抓取 、物件偵測 、機器手臂控制
外文關鍵詞： image correspondence, robot manipulation
相關次數： 點閱：186  下載：4 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本論文主要探討以視覺伺服來控制機器手臂實施抓取任務之技術。在本論文中，將會討論視覺伺服應用於各種不同領域之研究主題，其中包含了相機模型、校正、圖像對應、位置預測和廣為人知的視覺伺服控制方案「基於影像伺覺伺服」(IBVS)。操控機器手臂至目標的方法主要分為兩個階段：低精確度的粗略控制階段與高精確度的精準控制階段。在粗略控制階段中，藉由機器人操作系統(ROS)之工作平台分離法所激發出一個簡單的適應化概念，這概念乃是採用背景相減法，供二維處理之用。之後，在IBVS控制階段中採用較精準的控制方法，在偵測到一簡單的物體之後，依據物體的特徵所需來識別其適合的抓取姿勢，最後再由IBVS所控制。

    This dissertation studies and implements a control scheme of robot manipulator to perform a grasping task using visual servoing techniques. Visual servoing is a multidiscipline research topic with several subjects being discussed in this thesis including camera model and calibration, image correspondence, pose estimation and the well-known visual servoing control scheme image-based visual servoing (IBVS). At initial control phase and first coarse approach, tabletop segmentation from Robot Operating System (ROS) inspires the idea of a simpler adaptation, using background segmentation, applied for 2D processing. Later, at IBVS control phase with fine approach, a simple object detection capability is applied to recognize suitable grasping poses as desired image features for IBVS control.

    Abstract 2 Acknowledgement 4 Content 5 List of Figures 6 List of Tables 8 Chapter 1: INTRODUCTION 1 1.1. Background 1 1.2. Motivation 1 1.3. Related Research 1 Chapter 2: COMPUTER VISION 3 2.1. Camera parameters and calibration 3 2.2. Segmentation 4 2.3. Feature tracking 6 2.3.1 Keypoint detection 6 2.3.2 Enhance methods for feature matching 10 2.4. Object detection 15 2.5. Pose estimation 16 2.5.1 Pose Estimation from Homography 16 2.5.2 Experiments with homography pose estimation method 18 Chapter 3: DENSO ROBOT CONTROL 21 3.1 DH parameter convention 21 3.2 Inverse Kinematic 22 3.3 End-effector kinematics - screw transformation 25 3.4 Velocity Jacobian 26 3.5 Resolved-Rate Methods 27 Chapter 4: VISUAL SERVOING 29 4.1 Image-based visual servo control 29 4.1.1 The Image Jacobian 29 4.1.2 Defining control rule 30 4.1.3 Assume a constant value for unknown depth 30 4.1.4 Online estimation of depth – depth from motion 31 Chapter 5: EXPERIMENTS & RESULTS 33 Object grasping 33 Chapter 6: CONCLUSION 39 REFERENCE 40

    1. Corke, P.I., A Tutorial on Visual Servo Control. IEEE, 1996.
    2. Hutchinson, F.C.a.S., Visual Servo Control Part I: Basic Approaches. IEEE, 2006.
    3. Hutchinson, F.C.a.S., Visual Servo Control Part II: Advanced Approaches. IEEE, 2007.
    4. Pedram Azad, D.M., Tamim Asfour, Rudiger Dillmann, 6-DOF Model-based Tracking of Arbitrarily Shaped 3D Objects. IEEE, 2011.
    5. Lowe, D.G., Fitting Parameterized Three-Dimensional Models to Images. IEEE Transactions, 1991.
    6. Zisserman, M.A.a.A., Robust Object Tracking.
    7. Eric Marchand, P.B., Francois Chaumette, Valerie Moreau, Robust real-time visual tracking using a 2D-3D model-based approach. IEEE, 1999.
    8. Matei Ciocarlie, K.H., E. Gil Jones, Sachin Chitta, Radu Bogdan Rusu and Ioan A. Sucan, Towards Reliable Grasping and Manipulation in Household Environments. Intl. Symposium on Experimental Robotics (ISER), 2010.
    9. Cousins, R.B.R.a.S., 3D is here: Point Cloud Library (PCL). IEEE International Conference on Robotics and Automation (ICRA), 2011.
    10. Rusu, R.B.B., G.; Thibaux, R.; Hsu, J., Fast 3D recognition and pose using the Viewpoint Feature Histogram. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010: p. 2155,2162.
    11. Eric Marchand, F.C., Feature Tracking for Visual Servoing Purposes. Robotics and Autonomous Systems, 2005.
    12. Tomasi, J.S.a.C., Good Features to Track. Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, 1994: p. 593 - 600.
    13. Lowe, D.G., Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004(November 2004): p. 91-110.
    14. Herbert Bay, A.E., Tinne TuyteLaars, and Luc Van Gool, Speed-Up Robust Features (SURF). Computer Vision Image Understanding, 2008. 110: p. 346 - 359.
    15. Corke, P.I., Robotics, Vision and Control - Fundamental Algorithms in MATLAB, 2011, springer.
    16. Bolles, M.A.F.a.R.C., Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Graphics and Image Processing. 24.
    17. Bradski, A.K.G., Learning OpenCV: Computer Vision with the OpenCV Library. 1 edition ed2008: O'Reilly Media.
    18. Zisserman, R.H.a.A., Multiple View Geometry in computer vision2003: Cambridge University Press.
    19. Zhang, Z., Flexible Camera Calibration By Viewing a Plane From Unknown Orientations. The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999. 1: p. 666-673.
    20. Sagues, E.M.a.C., Fast Pose Estimation For Visual Navigation Using Homographies. The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009.
    21. Liu Xi, F.Y., Zhang Xuebo, Homography-Based Robust Pose Estimation Method. Proceedings of the 27th Chinese Control Conference, 2008.
    22. Davis, D.F.D.a.L.S., Model-Based Object Pose in 25 Lines of Code. 1995.
    23. Adrian Kaehler, G.B., Learning OpenCV: Computer Vision with the OpenCV Library. 1 edition ed2008: O'Reilly Media.
    24. Woods, R.C.G.a.R.E., Digital Image Processing2002: Prentice Hall.

    QR CODE