Volume 9 Number 1
February 2012
Article Contents
Hong-Bin Wang and Mian Liu. Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm. International Journal of Automation and Computing, vol. 9, no. 1, pp. 24-29, 2012. doi: 10.1007/s11633-012-0612-x
Cite as: Hong-Bin Wang and Mian Liu. Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm. International Journal of Automation and Computing, vol. 9, no. 1, pp. 24-29, 2012. doi: 10.1007/s11633-012-0612-x

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

Author Biography:
  • Corresponding author: Hong-Bin Wang
  • Received: 2010-05-04
  • A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
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  • 加载中
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    [2] Qiang Fu, Xiang-Yang Chen, Wei He. A Survey on 3D Visual Tracking of Multicopters . International Journal of Automation and Computing, 2019, 16(6): 707-719.  doi: 10.1007/s11633-019-1199-2
    [3] Ying Xie, Xiang-Dong Yang, Zhi Liu, Shu-Nan Ren, Ken Chen. Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features . International Journal of Automation and Computing, 2017, 14(2): 147-158.  doi: 10.1007/s11633-017-1063-1
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    [5] Sheng-Ye Yan, Xin-Xing Xu, Qing-Shan Liu. Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance . International Journal of Automation and Computing, 2014, 11(5): 480-488.  doi: 10.1007/s11633-014-0833-2
    [6] R. I. Minu,  K. K. Thyagharajan. Semantic Rule Based Image Visual Feature Ontology Creation . International Journal of Automation and Computing, 2014, 11(5): 489-499.  doi: 10.1007/s11633-014-0832-3
    [7] Rong-Min Cao,  Zhong-Sheng Hou,  Hui-Xing Zhou. Data-driven Nonparametric Model Adaptive Precision Control for Linear Servo Systems . International Journal of Automation and Computing, 2014, 11(5): 517-526.  doi: 10.1007/s11633-014-0834-1
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    [9] Chao-Lei Wang,  Tian-Miao Wang,  Jian-Hong Liang,  Yi-Cheng Zhang,  Yi Zhou. Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments . International Journal of Automation and Computing, 2013, 10(5): 387-396.  doi: 10.1007/s11633-013-0735-8
    [10] Han Wang, Wei Mou, Gerald Seet, Mao-Hai Li, M. W. S. Lau, Dan-Wei Wang. Real-time Visual Odometry Estimation Based on Principal Direction Detection on Ceiling Vision . International Journal of Automation and Computing, 2013, 10(5): 397-404.  doi: 10.1007/s11633-013-0736-7
    [11] Xin-Han Huang,  Xiang-Jin Zeng,  Min Wang. SVM-based Identification and Un-calibrated Visual Servoing for Micro-manipulation . International Journal of Automation and Computing, 2010, 7(1): 47-54.  doi: 10.1007/s11633-010-0047-1
    [12] Fei Li, Hua-Long Xie. Sliding Mode Variable Structure Control for Visual Servoing System . International Journal of Automation and Computing, 2010, 7(3): 317-323.  doi: 10.1007/s11633-010-0509-5
    [13] Jin-Kui Chu,  Rong-Hua Li,  Qing-Ying Li,  Hong-Qing Wang. A Visual Attention Model for Robot Object Tracking . International Journal of Automation and Computing, 2010, 7(1): 39-46.  doi: 10.1007/s11633-010-0039-1
    [14] De Xu,  Min Tan,  Xiaoguang Zhao,  Zhiguo Tu. Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision . International Journal of Automation and Computing, 2004, 1(1): 63-75.  doi: 10.1007/s11633-004-0063-0
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Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

    Corresponding author: Hong-Bin Wang

Abstract: A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Hong-Bin Wang and Mian Liu. Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm. International Journal of Automation and Computing, vol. 9, no. 1, pp. 24-29, 2012. doi: 10.1007/s11633-012-0612-x
Citation: Hong-Bin Wang and Mian Liu. Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm. International Journal of Automation and Computing, vol. 9, no. 1, pp. 24-29, 2012. doi: 10.1007/s11633-012-0612-x
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