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SVM-based identification and un-calibrated visual servoing for micro-manipulation

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Abstract

This paper presents an improved support vector machine (SVM) algorithm, which employs invariant moments-based edge extraction to obtain feature attribute. A heuristic attribute reduction algorithm based on rough set’s discernible matrix is proposed to identify and classify micro-targets. To avoid the complicated calibration for intrinsic parameters of camera, an improved Broyden’s method is proposed to estimate the image Jacobian matrix which employs Chebyshev polynomial to construct a cost function to approximate the optimization value. Finally, a visual controller is designed for a robotic micromanipulation system. The experiment results of micro-parts assembly show that the proposed methods and algorithms are effective and feasible.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, PRC

    Xin-Han Huang, Xiang-Jin Zeng & Min Wang

Authors
  1. Xin-Han Huang
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  2. Xiang-Jin Zeng
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  3. Min Wang
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Corresponding author

Correspondence to Xin-Han Huang.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60873032) and National High Technology Research and Development Program of China (863 Program) (No. 2008AA8041302)

Xin-Han Huang graduated from Huazhong University of Science and Technology (HUST), Wuhan, PRC in 1969. He worked at the Robotics Institute of Carnegie-Mellon University, Pittsburgh, USA, as a visiting scholar from 1985 to 1986 and the Systems Engineering Division of Wales University, Cardiff, UK, as a senior visiting scholar in 1996. He is currently a professor and head of the Intelligence and Control Engineering Division of HUST. He is a senior member of the Chinese Automation Society and chairman of the Intelligent Robot Specialty Committee of the Chinese Association for Artificial Intelligence (CAAI).

His research interests include robotics, sensing techniques, data fusion, and intelligent control.

Xiang-Jin Zeng received his B.Eng. degree in 2000 and M.Eng. degree in 2006, both from Huazhong University of Science and Technology (HUST), Wuhan, PRC. Since 2006, he has been a Ph.D. candidate of HUST, majoring in control science and engineering.

His research interests include microscope visual servoing, image procession, robotics, and embedded system.

Min Wang received B.Eng. and M.Eng. degrees from Huazhong University of Science and Technology (HUST), Wuhan, PRC in 1982 and 1989, respectively. She is currently a professor of the Department of Control Science and Engineering of HUST. She is a secretary-general of the Intelligent Robots Specialty Committee of the Chinese Association for Artificial Intelligence (CAAI).

Her research interests include robotics, sensing techniques, neural networks and their applications.

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Huang, XH., Zeng, XJ. & Wang, M. SVM-based identification and un-calibrated visual servoing for micro-manipulation. Int. J. Autom. Comput. 7, 47–54 (2010). https://doi.org/10.1007/s11633-010-0047-1

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  • DOI: https://doi.org/10.1007/s11633-010-0047-1

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