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Experiments of Composite Learning Admittance Control on 7-DoF Collaborative Robots

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Intelligent Robotics and Applications (ICIRA 2021)

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Abstract

With the increasing demand for collaborative robots in various industries, the implementation of interaction control algorithms with superior performances for collaborative robots becomes significant. This paper develops a composite learning admittance control (CLAC) approach and implements it to an industrial collaborative robot with 7 degrees of freedom named Franka Emika Panda. The interaction performance of the CLAC in the task space is verified by both force tracking and compliance control experiments. Experimental results show that the CLAC enables the Panda robot with favorable force tracking capability under a specific admittance model, high tracking accuracy in the free state, and satisfied compliance under motion constraints.

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Acknowledgements

This work was supported in part by the Guangdong Pearl River Talent Program of China under Grant No. 2019QN01X154, and in part by the Fundamental Research Funds for the Central Universities of China under Grant No. 19lgzd40.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Xin Liu, Zhiwen Li & Yongping Pan

Authors
  1. Xin Liu
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  2. Zhiwen Li
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  3. Yongping Pan
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Corresponding author

Correspondence to Yongping Pan .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

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Liu, X., Li, Z., Pan, Y. (2021). Experiments of Composite Learning Admittance Control on 7-DoF Collaborative Robots. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_50

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  • DOI: https://doi.org/10.1007/978-3-030-89098-8_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89097-1

  • Online ISBN: 978-3-030-89098-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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