Visual Servo Tracking Control for Slider Robot Using CMAC Neural Network Recognition Approach

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This paper describes an image-based visual servo tracking control scheme using CMAC neural network as object recognition feedback methodology. A web camera based image capture system is mounted on the slider robot to capture the desired object and a CMAC (Cerebellar Model Articulation Controller) based object recognition scheme is developed to recognize the captured object image. Comparing the relative location of the recognized object and the web camera, then the position feedback signal can be obtained. Using the feedback signal, a PID (Proportional-Integral-Derivative) controller is designed to track the desired object by moving a single-axis slider robot, such that the captured object image located on the center of the captured frame always.

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Periodical:

Edited by:

Wen-Hsiang Hsieh

Pages:

2092-2095

DOI:

10.4028/www.scientific.net/AMM.284-287.2092

Citation:

C. P. Hung and F. T. Hsieh, "Visual Servo Tracking Control for Slider Robot Using CMAC Neural Network Recognition Approach", Applied Mechanics and Materials, Vols. 284-287, pp. 2092-2095, 2013

Online since:

January 2013

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$38.00

[1] F. Chaumette and S. Hutchinson, Visual Servo Control Part I: Basic Approaches, IEEE Robotics & Automation Magazine, pp.83-90, (2006).

DOI: 10.1109/mra.2006.250573

[2] Z. -X. Chen, C. -Y. Liu, F. -L. Chang and G. -Y. Wang, Automatic License-Plate Location and Recognition Based on Feature Salience, IEEE Trans. Veh. Technol., Vol. 58, No. 7, September, (2009).

DOI: 10.1109/tvt.2009.2013139

[3] P. Kulkarni, A. Khatri, P. Banga and K. Shah, A Feature Based Approach for Localization of Indian Number Plates, IEEE Intel. Conf. on Electro/Information Technol., pp.157-162, (2009).

DOI: 10.1109/eit.2009.5189601

[4] N. -S. Pai, S. -F. Huang, Y. -P Kuo and C. -L. Kuo, License Plate Recognition Based on Extension Theory, 2010 Inter. Symposium on Computer, Communication, Control and Automation, pp.164-167, Oct. (2010).

DOI: 10.1109/3ca.2010.5533616

[5] J. Wang, J. -F. Yang, S. -F Li, Q. -F Dai and J. -X. Xie, Number Image Recognition Based on Neural Network Ensemble, Third International Conference on Natural Computation, pp.237-240, July. (2007).

DOI: 10.1109/icnc.2007.506

[6] H. -H. Wang, Y. -H. Liu, W. -D. Chen and Z. -L. Wang, A New Approach to Dynamic Eye-in-Hand Visual Tracking Using Nonlinear Observers, IEEE/ASME Trans. on Methtronics, Vol. 16, No. 2, pp.387-394, (2011).

DOI: 10.1109/tmech.2009.2039941

[7] Y. -H. Liu and H. -H. Wang, An Adaptive Controller for Image-based Visual Servoing of Robot Manipulators, 2010 8th Congress on Intelligent Control and Automation, pp.988-993, (2010).

DOI: 10.1109/wcica.2010.5554505

[8] C. -P. Hung and B. -C. Tsai, Character Recognition Using CMAC Neural Network Approach, Proceeding of 6th Intelligent Living Technology Conf., pp.419-425, (2011).

[9] J. S. Albus, A New Approach to Manipulator Control: the Cerebeller Model Articulation Controller (CMAC)1 , Trans. ASME J. Dynam., Syst., Meas., and Contr., Vol. 97, pp.220-227, (1975).

DOI: 10.1115/1.3426922

[10] C. -P. Hung and M. -H. Wang, Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach, 7th Online World Conference on Soft Computing in Industrial Applications, Session 1, #1, (2002).

DOI: 10.1007/978-1-4471-3744-3_1

[11] D. A. Handeiman, S. H. Lane and J. J. Gelfand, Integrating Neural Networks and Knowledge-Based Systems for Intelligent Robotic Control, IEEE Control System Magazine, pp.77-86, (1990).

DOI: 10.1109/37.55128

[12] C. -P. Hung, W. -G. Liu and H. -J. Su, Fault Diagnosis of Steam Turbine-Generator Sets Using CMAC Neural Network Approach and Portable Diagnosis Apparatus Implementation, Lecture Notes in Computer Science, (2009).

DOI: 10.1007/978-3-642-04070-2_78

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