SVM-based Identification and Un-calibrated Visual Servoing for Micro-manipulation

作     者：Xin-Han Huang Xiang-Jin Zeng Min Wang Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PRC 

作者机构：Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan PRC 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2010年第7卷第1期

页      面：47-54页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (No.60873032) National High Technology Research and Development Program of China (863 Program) (No.2008AA8041302) 

主　　题：Micro-assembly support vector machine part identification Broyden method visual servoing 

摘      要：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.