Eye-on-Hand Calibration Method for Cable-Driven Parallel Robots

  • Nicolas TremblayEmail author
  • Kaveh Kamali
  • Philippe Cardou
  • Christian Desrosiers
  • Marc Gouttefarde
  • Martin J.-D. Otis
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 74)


Estimating the geometric parameters of a cable-driven parallel robot (CDPR) can be a labour intensive process or one that requires expensive sensors. This paper presents a low-cost method for estimating initial cable lengths and fixed cable attachment points of CDPRs. The proposed approach relies on the detection, mapping and localisation of fiduciary markers in the robot environment using a camera attached to the end effector. This paper additionally tackles the generation of a list of reachable calibration poses and presents a control scheme allowing the CDPR to reach those. Experiments are also carried out to assess the performance of the proposed calibration method. It appears that the proposed eye-on-hand method is more accurate than a previously reported method relying solely on cable-length measurements.


Cable-driven parallel robot parameter identification experimental testing vision-based calibration 


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This work is financially supported by the Fonds de recherche du Québec – Nature et technologies (FRQNT), under the grant number 2016-PR-188869.


  1. 1.
    J. A. Dit Sandretto, D. Daney, and M. Gouttefarde. Calibration of a fully-constrained parallel cable-driven robot. In 19th CISM-IFToMM Symposium on Robot Design, Dynamics, and Control (ROMANSY 2012), Paris, France, 2012Google Scholar
  2. 2.
    A. Fortin-Côté, P. Cardou, and C. Gosselin. An admittance control scheme for haptic interfaces based on cable-driven parallel mechanisms. In Proceedings – IEEE International Conference on Robotics and Automation, pages 819–825, 2014Google Scholar
  3. 3.
    S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and R. Medina-Carnicer. Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition, 51:481–491, 2016CrossRefGoogle Scholar
  4. 4.
    J.-B. Izard, M. Gouttefarde, M. Michelin, O. Tempier, and C. Baradat. A Reconfigurable Robot for Cable-Driven Parallel Robotic Research and Industrial Scenario Proofing. In T. Bruckmann and A. Pott, editors, Cable-Driven Parallel Robots, pages 135–148. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013Google Scholar
  5. 5.
    W. Khalil and S. Besnard. Self calibration of Stewart-Gough parallel robots without extra sensors. IEEE Transactions on Robotics and Automation, 15(6):1116–1121, 1999CrossRefGoogle Scholar
  6. 6.
    D. Lau. Initial Length and Pose Calibration for Cable-Driven Parallel Robots with Relative Length Feedback. In C. Gosselin, P. Cardou, T. Bruckmann, and A. Pott, editors, Cable-Driven Parallel Robots, pages 140–151, Cham, 2018. Springer International PublishingGoogle Scholar
  7. 7.
    R. K. Lenz and R. Y. Tsai. Calibrating a Cartesian robot with eye-on-hand configuration independent of eye-to-hand relationship. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(9):916–928, sep 1989CrossRefGoogle Scholar
  8. 8.
    J. Mattingley and S. Boyd. CVXGEN: A code generator for embedded convex optimization. Optimization and Engineering, 13(1):1–27, 2012MathSciNetCrossRefGoogle Scholar
  9. 9.
    R. Meziane, P. Cardou, and M. J. Otis. Cable interference control in physical interaction for cable-driven parallel mechanisms. Mechanism and Machine Theory, 132:30–47, 2019CrossRefGoogle Scholar
  10. 10.
    P. Miermeister and A. Pott. Auto Calibration Method for Cable-Driven Parallel Robots Using Force Sensors. In J. Lenarcic and M. Husty, editors, Latest Advances in Robot Kinematics, pages 269–276. Springer Netherlands, Dordrecht, 2012CrossRefGoogle Scholar
  11. 11.
    R. Muñoz-Salinas, M. J. Marín-Jimenez, E. Yeguas-Bolivar, and R. Medina-Carnicer. Mapping and localization from planar markers. Pattern Recognition, 73:158–171, 2018CrossRefGoogle Scholar
  12. 12.
    A. J. Patel and K. F. Ehmann. Calibration of a hexapod machine tool using a redundant leg. International Journal of Machine Tools and Manufacture, 40(4):489–512, mar 2000CrossRefGoogle Scholar
  13. 13.
    P. Renaud, N. Andreff, F. Marquet, and P. Martinet. Vision-based kinematic calibration of a H4 parallel mechanism. 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 1:1191–1196, 2003Google Scholar
  14. 14.
    F. J. Romero-Ramirez, R. Muñoz-Salinas, and R. Medina-Carnicer. Speeded up detection of squared fiducial markers. Image and Vision Computing, 76:38–47, 2018CrossRefGoogle Scholar
  15. 15.
    H. Zhuang, O. Masory, and J. Yan. Kinematic calibration of a Stewart platform using pose measurements obtained by a single theodolite. Intelligent Robots and Systems 95. ‘Human Robot Interaction and Cooperative Robots’, Proceedings. 1995 IEEE/RSJ International Conference on, 2:329–334 vol. 2, 1995Google Scholar
  16. 16.
    H. Zhuang, J. Yan, and O. Masory. Calibration of Stewart platforms and other parallel manipulators by minimizing inverse kinematic residuals. Journal of Robotic Systems, 15:395–405, 1998CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicolas Tremblay
    • 1
    Email author
  • Kaveh Kamali
    • 2
  • Philippe Cardou
    • 1
  • Christian Desrosiers
    • 2
  • Marc Gouttefarde
    • 3
  • Martin J.-D. Otis
    • 4
  1. 1.Laboratoire de Robotique, Département de génie mécaniqueUniversité LavalQuébec CityCanada
  2. 2.École de technologie supérieureMontréalCanada
  3. 3.LIRMM, University of Montpellier, CNRSMontpellierFrance
  4. 4.Laboratoire d’automatique et d’interaction 3D multimodale intelligente (LAIMI), Département des Sciences AppliquéesUniversité du Québec à Chicoutimi (UQAC)ChicoutimiCanada

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