跳到主要內容

臺灣博碩士論文加值系統

(18.204.48.64) 您好!臺灣時間:2021/08/01 10:03
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:施智文
研究生(外文):zhi-wen shi
論文名稱:雙機械臂於視覺導引物件搬運之協調控制
論文名稱(外文):Coordination Control of Two Robot Arms for Vision-guided Material Handling
指導教授:蔡清元蔡清元引用關係
指導教授(外文):Tsing-Iuan Tsay
學位類別:碩士
校院名稱:國立成功大學
系所名稱:機械工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:92
中文關鍵詞:倒傳遞類神經網路支援向量機視覺導引控制
外文關鍵詞:back-propagation neural networkvision-guided controlSupport Vector Machine
相關次數:
  • 被引用被引用:0
  • 點閱點閱:183
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
除了傳統工業應用之外,機器人已經應用在我們生活之中如醫療保健、保全和家庭生活,因而,透過人形機器人之雙手臂進行協調工作,其控制策略之開發逐漸引起大家的興趣。本論文之目標為提出視覺導引控制策略,用以使人形機器人驅動雙手臂進行物件搬運之應用,而所提出之控制策略是基於倒傳遞神經網路,此神經網路是用來達成目標物之影像特徵及其姿態之映射,並建構一人形機器人,來驗證我們提出方法之理論結果。所建構的半身人形機器人為一固定位置之人形身驅,並擁有一個二自由度的單眼機械頭部,及兩隻各六個自由度的機械手臂。在訓練完成後,機器人即可精確地定位出目標所在。實驗結果顯示所提出之方法,可在不需要事先得知兩方塊之位置資訊下,能使人形機器人協調雙手臂來抱住一個方塊,並將其堆疊置另一方塊上。
In addition to traditional applications in industry, robots now support our lives in such areas as medical care, security and home life. There is growing interest in the development of control strategies for cooperating tasks being done by two robot arms of a humanoid robot. The objective of this thesis is to propose a vision-guided control strategy for a humanoid robot to drive both robot arms in the application of material handling. The proposed control strategy is based on the back-propagation neural network, which is applied to achieve the mapping between image features of a target and the pose of the target. To verify the theoretical results of the proposed method, a humanoid robot is constructed. The constructed partial-body humanoid robot is a humanoid torso in a fixed location, with one 2 degrees of freedom (DOF) robotic monocular head and two 6-DOF robot arms. After the training stage, the robot is capable of locating the target accurately. The experimental results reveal that the proposed approach ensures that two cooperative arms of a humanoid robot can hold and transport a cube from one place to the top of the other cube without beforehand information about the location of the both cubes.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
符號說明 x

第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 文獻回顧 1
1.4 本文內容與架構 4

第二章 機器人系統架構及硬體規格 5
2.1 伺服機馬達 5
2.2 網路攝影機 7
2.3 機器人系統的硬體及控制架構 8

第三章 機械人的運動學分析 12
3.1 座標系統之定義 12
3.2 機械人之座標系統 14
3.3 機械手臂之順向運動學 21
3.4 機械手臂之逆向運動學 25
3.5 機械手臂之速度運動學 28
3.6 機械手臂工作空間之軌跡 36
3.6.1 空間的直線路徑 36
3.6.2 空間的方位 37
3.6.3 軌跡的規劃 39
3.7 雙手臂抓取與搬運及方塊之軌跡規劃 41
3.8 OpenGL視覺化模擬環境 44

第四章 影像處理 47
4.1 支援向量機 47
4.2 影像區塊分類處理 55
4.3 邊緣偵測 57
4.4 擷取角落特徵值 58
4.5 總結 61

第五章 機械人之頭手操作協調學習 65
5.1 類神經網路的選取 65
5.2 倒傳遞網路模型架構 66
5.3 倒傳遞網路的訓練步驟 69
5.4 機械人之頭手操作協調學習步驟 70

第六章 實驗 71
6.1 實驗設置 71
6.2 方塊影像之辨識 74
6.3 學習參數設定 74
6.3.1參數設定 75
6.3.2訓練步驟 76
6.4 倒傳遞網路訓練結果 77
6.5 機器人定位性能之評估 79
6.6 機器人之方塊堆疊測試 81

第七章 結論 86
7.1 結果與討論 86
7.2 未來發展 87
參考文獻 88
[1]M. A. Aizerman, E. M. Braverman and L. I. Rozonoer, “Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning,” Autom. Remote Control, Vol. 25, 1964.
[2]G. Asuni, G. Teti, C. Laschi, E. Guglielmelli and P. Dario, “Extension to End-effector Position and Orientation Control of a Learning-based Neurocontroller for a Humanoid Arm,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4151-4156, Oct. 2006.
[3]L. Bottou, C. Cortes, J. Denker, H.Drucker, I. Guyon, L.Jackel, Y. LeCun, U.Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparision of Classifier Methods: A Case Study in Handwriting Digit Recognition,” IEEE Computer Society Press In International Conference on Pattern Recognition, pp. 77-87, 1994.
[4]B. E. Boser, I. Guyon, and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” Proceedings of the Fifth Annual Workshop on Computational Learning Theory 5, pp. 144-152, 1992.
[5]C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[6]J. Smola and B. Schölkopf, “A Tutorial on Support Vector Regression,” Tech. Rep.NC2-TR-1998-030, Neural and Computational Learning II, 1998
[7]J. C. Burges and B. Schölkopf, “Improving the Accuracy and Speed of Support Vector Learning Machines,” in Advances in Neural Information Processing Systems 9 (M. Mozer, M. Jordan, and T. Petsche, eds.), pp. 375-381, Cambridge, MA: MIT Press, 1997.
[8]L. J. Cao, K. S. Chua, and L. K. Guan, “C-Ascending Support Vector Machines for Financial Time Series Forecasting,” in 2003 International Conference on Computational Intelligence for Financial Engineering (CIFEr2003), (Hong Kong), pp. 317-323, 2003.
[9]C. Charalambous, “Conjugate Gradient Algorithm for Efficient Training of Artificial Neural Network,” Proceedings of the IEE International Conference on Circuits, Devices and Systems, Vol. 139, No. 3, pp. 301-310, June 1992.
[10]C. Chevallerean and W. Khalil, “Efficient Method for The Calculation of The Pseudo Inverse Kinematic Problem,” Proceedings of the IEEE Conference on Robotics and Automation, pp. 1842-1848, 1984. [4]
[11]C. Cortes and V. Vapnik, “Support vector networks,”Machine Learning, vol. 20, pp. 273-297,1995.
[12]J. J. Craig, Introduction of Robotics Mechanics & Control, Addision-Wesley, 1986.
[13]K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” in Computational Learning Theory, pp. 35-46, 2000
[14]H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola and V. Vapnik, “Support Vector Regression Machines,” in Advances in Neural Information Processing Systems, vol. 9, p. 155, The MIT Press, 1997.
[15]G. Flandin, F. Chaumette and E. Marchand, “Eye-in-hand/Eye-to-hand Cooperation for Visual Servoing,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2741-2746, Apr. 2000.
[16]G. Fung, O. L. Mangasarian, and J. Shavlik, “Knowledge-based support vector machine classifiers,” in Advances in Neural Information Processing, 2002.
[17]M. T. Hagan and M. B. Menhaj, “Training Feedforward Networks with the Marquardt Algorithm,” IEEE Transactions on Neural Networks, Vol. 5, Issue 6, pp. 989-993, Nov. 1994.
[18]C. W. Hsu and C. J. Lin, “A Comparison of Methods for Multi-class Support Vector Machines,” IEEE Transactions on Neural Networks, pp.415-425, 2002. KreBel U., Pairwise Classification and Support Vector Machines. Advances in Kernel Methods-Support Vector Learning, MIT Press , Cambridge, pp. 254-268, 1999.
[19]S. Hutchinson, G. D. Hager and P. I. Corke, “A Tutorial on Visual Servo Control,” IEEE Transactions on Robotics and Automation, Vol. 12, Issue 5, pp. 651-670, Oct. 1996.
[20]T. Joachims, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” in Proceedings of ECML-98, 10th European Conference on Machine Learning (C.Nédellec and C. Rouveirol, eds.), (Chemnitz, DE), pp. 137-142, Springer Verlag, Heidelberg, DE,1998.
[21]W. Khalil and J. F. Kleinfinger, “A New Geometric Notation for Open and Closed Loop Robots,” Proceedings of IEEE International Conference on Robotics and Automation, pp.1174-1180, 1986.
[22]C. H. Lai, Design and Control of an Anthropomorphic Robot, Master Thesis, Dept. of Mechanical Eng., Nation Cheng Kung University, 2003.
[23]V. Lippiello, B. Siciliano and L. Villani, “Position-Based Visual Servoing in Industrial Multirobot Cells Using Hybrid Camera Configuration,” IEEE Transactions on Robotics, Vol. 23, Issue 1, pp. 73-86, Feb. 2007.
[24]Q. Meng and M. H. Lee, “Biologically Inspired Automatic Construction of Cross-Modal Mapping in Robotic Eye/Hand systems,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4742-4749, Oct. 2006.
[25]A. Muis and K. Ohnishi, “Eye-to-Hand Approach on Eye-in-Hand Configuration Within Real-Time Visual Servoing,” IEEE/ASME Transactions on Mechatronics, Vol. 10, Issue 4, pp. 404-410, Aug. 2005.
[26]S. Mukherjee, E. Osuna and F. Girosi, “Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines,” in 1997 IEEE Workshop on Neural Networks for Signal Processing, pp.511-519, 1997.
[27]K.-R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen and V. Vapnik, “Predicting time series with support vector machines,” in Articial Neural Networks - ICANN'97 (W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, eds.), pp. 999-1004, 1997.
[28]E. D. Orin and W. W. Schrader, “Efficient Computation of the Jacobian for Robot Manipulator,” The International Journal of Robotics Research, Vol.3, No.4, pp.66-75, 1984.
[29]M. Pontil and A. Verri, Support Vector Machines for 3D Object Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 637–646, 1998.
[30]Platt J. C., N. Cristianini and J. Shawe-Taylor. Large Margin DAGs for Multiclass Classification. In Advances in Neural Information Processing Systems, MIT Press, Vol. 12, pp. 547-553, 2000.
[31]H. Ritter, T. Martinetz and K. Schulten, Neural Computation and Self-Organizing Maps: an Introduction, Addison-Wesley, pp. 64-72, 127-130, 1992.
[32]A. C. Sanderson and L. E. Weiss, “Image-Based Visual Servo Control using Relational Graph Error Signals,” Proceedings of the IEEE International Conference on Cybernetics and Society, pp. 1074-1077, 1980.
[33]L. Sciavicco and B. Siciliano, ”Modeling and Control of Robot Manipulators,” New York: McGraw-Hill Company, Inc. 1996.
[34]W. Sepp, S. Fuchs and G. Hirzinger, “Hierarchical Featureless Tracking for Position-Based 6-DoF Visual Servoing,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4310-4315, Oct. 2006.
[35]I. H. Suh and T. W. Kim, “Fuzzy Membership Function Based Neural Networks with Applications to the Visual Servoing of robot Manipulators,” IEEE Transactions on Fuzzy Systems, Vol. 2, Issue 3, pp. 203-220, Aug. 1994.
[36]J. Su, Y. Xi, U. D. Hanebeck and G. Schmidt, “Nonlinear Visual Mapping Model for 3-D Visual Tracking With Uncalibrated Eye-in-Hand Robotic System,” IEEE Transactions on Part B of System, Man, and Cybernetics, Vol. 34, Issue 1, pp. 652-659, Feb. 2004.
[37]F. E. H. Tay and L. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting,” Omega, vol. 29, pp. 309-317, 2001.
[38]V. Vapnik, Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982
[39]T. P. Vogl, J. K. Mangis, A. K. Zigler, W. T. Zink and D. L.Alkon, “Accelerating the Convergence of the Backpropagation Method,” Biological Cybernetics, Vol. 59, No. 4-5, pp. 256-264, Sep. 1998.
[40]J. K. Waldron, W. S. Liang and S. J. Bolin, “A Study of The Jacobian Matrix of Serial manipulator,” Tran. Of ASME Journal Of Mechanisms, Transmissions and Automation in Design, Vol. 107, pp.230-238, June 1985.
[41]J. A. Walter and K. J. Schulten, “Implementation of Self-Organizing Neural Networks for Visuo-Motor Control of an Industrial Robot,” IEEE Transactions on Neural Networks, Vol. 4, Issue 1, pp. 86-96, Jan. 1993.
[42]Robotis 網站http://www.robotis.com/html/main.php, AX-12 Manual (English) 說明書
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊