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External Force Detection for Physical Human-Robot Interaction Using Dynamic Model Identification

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10462))

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Abstract

Nowadays as more and more tasks require humans to collaborate with robots in modern industry, and the focus of many robotic researchers worldwide has turned towards human-robot collaboration. In human-robot interaction, ensuring the safety issues has the absolute priority for all other research work. In this context, sensorless collision detection and fast response researches in robotics contribute significantly to solve the safety issues. However, existing approaches for collision detection involve in the usage of external sensors, not fit for closed industrial robots or the offline observer based on robot’s the generalized momentum, poor in the real time response. In this study, a different method of external forces detection for sensor-less industrial robots using dynamics model identification is proposed. The main idea of our method is to identify the external torques by the comparison of the actual motor torques with the predicted joint torques based on dynamics model. Without using any extra sensors, a strict dynamics model including the parameterized friction torques has been formulated only by utilizing the measurements of the joint angles and joint torques. In addition, the essential response strategies in the post-contact stage are the main directions for our following research. Finally, the model accuracy and performance of the proposed method were evaluated in a 6-DOF manipulator. The experimental results demonstrated the reliability of our detection method basically.

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Acknowledgments

This research is supported by National Natural Science Foundation of China (Grant No. 51675389), the International Science & Technology Cooperation Program, Hubei Technological Innovation Special Fund (Grant No. 2016AHB005), the Fundamental Research Funds for the Central Universities (Grant No. 2017III5XZ), and Engineering and Physical Sciences Research Council (EPSRC), UK (Grant No. EP/N018524/1).

Author information

Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Dewen Wu, Quan Liu, Wenjun Xu, Aiming Liu & Zude Zhou

  2. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China

    Dewen Wu & Wenjun Xu

  3. Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Wuhan University of Technology, Ministry of Education, Wuhan, 430070, China

    Quan Liu, Aiming Liu & Zude Zhou

  4. Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, B15 2TT, UK

    Duc Truong Pham

Authors
  1. Dewen Wu
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  2. Quan Liu
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  3. Wenjun Xu
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  4. Aiming Liu
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  5. Zude Zhou
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  6. Duc Truong Pham
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Corresponding author

Correspondence to Dewen Wu .

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Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Wu, D., Liu, Q., Xu, W., Liu, A., Zhou, Z., Pham, D.T. (2017). External Force Detection for Physical Human-Robot Interaction Using Dynamic Model Identification. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_55

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  • DOI: https://doi.org/10.1007/978-3-319-65289-4_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65288-7

  • Online ISBN: 978-3-319-65289-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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