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A New Hybrid Calibration Method for Robot Manipulators by Combining Model–Based Identification Technique and a Radial Basis Function–Based Error Compensation

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Though the kinematic parameters had been well identified, there are still existing some non-negligible non-geometric error sources such as friction, gear backlash, gear transmission, temperature variation etc. They need to be eliminated to further improve the accuracy of the robotic system. In this paper, a new hybrid calibration method for improving the absolute positioning accuracy of robot manipulators is proposed. The geometric errors and joint deflection errors are simultaneously calibrated by robot model identification technique and a radial basis function neural network is applied for compensating the robot positions errors, which are caused by the non-geometric error sources. A real implementation was performed with Hyundai HH800 robot and a laser tracker to demonstrate the effectiveness of the proposed method.

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Acknowledgment

This research was supported by the 2019 Research fund of University of Ulsan, Ulsan, Korea.

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Authors and Affiliations

  1. Graduate School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, South Korea

    Phu-Nguyen Le

  2. School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, South Korea

    Hee-Jun Kang

Authors
  1. Phu-Nguyen Le
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  2. Hee-Jun Kang
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Corresponding author

Correspondence to Hee-Jun Kang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Le, PN., Kang, HJ. (2019). A New Hybrid Calibration Method for Robot Manipulators by Combining Model–Based Identification Technique and a Radial Basis Function–Based Error Compensation. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

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