A New Technique to Improve Estimation of Position for Serial Robots

Ya Li Wang; Zhen Zhong Wei; Ming Wei Shao; Guang Jun Zhang

doi:10.4028/www.scientific.net/AMM.511-512.817

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512A New Technique to Improve Estimation of Position...

A New Technique to Improve Estimation of Position for Serial Robots

Abstract:

This paper presents a new method to improve the estimation of the positions for serial robots using power-activated feed-forward neural network. In the paper, a six-input three-output neural network is created with robot joint angle sine values as inputs and positions in the world frame as outputs. The neuron is activated with an orthogonal polynomial sequence,and the neural weights can be calculated directly without involving iterative and convergent problem. It is found that, the RMS error is less than 0.25 mm for the whole work space. And the absolute and relative errors of this method are smaller than those of built in kinematics model and the traditional back propagation (BP) neural network method. Experimental results show that the proposed method can effectively predict the positioning of the given joint angles.

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Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

817-821

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.817

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Cite this paper

Online since:

February 2014

Authors:

Ya Li Wang, Zhen Zhong Wei, Ming Wei Shao, Guang Jun Zhang

Keywords:

Neural Network (NN), Positioning Accuracy, Power-Activated, Serial Robot

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References

[1] Roth Z, Mooring B and Ravani B. An overview of robot calibration. IEEE Journal of Robotics and Automation. 3(1987) 377–385.

DOI: 10.1109/jra.1987.1087124

Google Scholar

[2] Elatta AY, Li PG, Fan LZ, Yu D and Luo F. An Overview of Robot Calibration. Information Technology Journal. 3(2004) 74–78.

Google Scholar

[3] Denavit J and Hartenberg RS. A kinematic notation for lower-pair mechanisms based on matrices. Trans ASME J. Appl. 23 (1955) 215–221.

DOI: 10.1115/1.4011045

Google Scholar

[4] Samad A Hayati. Robot arm geometric link parameter estimation. The 22nd IEEE Conference on Decision and Control . 22(1983) 1477–83.

DOI: 10.1109/cdc.1983.269783

Google Scholar

[5] Stone HW. Kinematic modeling, identification and control of robotic manipulator . Dissertation, Caregie Mellon University, (1986).

Google Scholar

[6] Wu Chi-haur. The kinematic error model for the design of robot manipulators. American Control Conference. (1983) 497– 502.

Google Scholar

[7] Veitschegger W and Chi-huar Wu. Robot accuracy analysis based on kinematics. IEEE Journal of Robotics and Automation. 2 (1986) 171–179.

DOI: 10.1109/jra.1986.1087054

Google Scholar

[8] Gatti G and Danieli G. A practical approach to compensate for geometric errors in measuring arms: application to a six-degree-of-freedom kinematic structure. Meas. Sci. Technol. 19 (2008) 015107.

DOI: 10.1088/0957-0233/19/1/015107

Google Scholar

[9] Lewis JM, Zhong XL and Rea H. A neural network approach to the robot inverse calibration problem. Second International Conference on Intelligent Systems Engineering. (1994)342–347.

DOI: 10.1049/cp:19940648

Google Scholar

[10] Zhong X, Lewis J and N-Nagy FL. Inverse robot calibration using artificial neural networks. Engineering Applications of Artificial Intelligence. 9(1996) 83–93.

DOI: 10.1016/0952-1976(95)00069-0

Google Scholar

[11] Xiao X, Jiang X and Zhang Y. An algorithm for designing chebyshev neural network. 2009 ISECS International Colloquium on Computing, Communication, Control, and Management. 2 (2009) 206–209.

DOI: 10.1109/cccm.2009.5267944

Google Scholar

[12] Zhang Y, Li W, Yi C, and Chen K . A weights-directly-determined simple neural network for nonlinear system identification. 2008 IEEE International Conference on Fuzzy Systems (FUZZ 2008). p.455–460.

DOI: 10.1109/fuzzy.2008.4630408

Google Scholar

[13] Lin C. Numerical analysis. Beijing, China: Science Press, (2007).

Google Scholar