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German Conference on Pattern Recognition

GCPR 2018: Pattern Recognition pp 33–45Cite as

A Table Tennis Robot System Using an Industrial KUKA Robot Arm

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

Abstract

In recent years robotic table tennis has become a popular research challenge for image processing and robot control. Here we present a novel table tennis robot system with high accuracy vision detection and fast robot reaction. Our system is based on an industrial KUKA Agilus R900 sixx robot with 6 DOF. Four cameras are used for ball position detection at 150 fps. We employ a multiple-camera calibration method, and use iterative triangulation to reconstruct the 3D ball position with an accuracy of 2.0 mm. In order to detect the flying ball with higher velocities in real-time, we combine color and background thresholding. For predicting the ball’s trajectory we test both a curve fitting approach and an extended Kalman filter. Our robot is able to play rallies with a human counting up to 50 consequential strokes and has a general hitting rate of 87%.

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Acknowledgement

This work was supported in part by the Vector Stiftung and KUKA.

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Authors and Affiliations

  1. Cognitive Systems, Eberhard Karls University, Tübingen, Germany

    Jonas Tebbe, Yapeng Gao, Marc Sastre-Rienietz & Andreas Zell

Authors
  1. Jonas Tebbe
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  2. Yapeng Gao
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  3. Marc Sastre-Rienietz
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  4. Andreas Zell
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Corresponding authors

Correspondence to Jonas Tebbe , Yapeng Gao , Marc Sastre-Rienietz or Andreas Zell .

Editor information

Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Tebbe, J., Gao, Y., Sastre-Rienietz, M., Zell, A. (2019). A Table Tennis Robot System Using an Industrial KUKA Robot Arm. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

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