Enhancement of Multi-user Teleoperation Systems by Prediction of Dyadic Haptic Interaction

  • Daniela Feth
  • Angelika Peer
  • Martin Buss
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


By integrating a model of the remote environment or the human operator into a haptic bilateral teleoperation control architecture, their behavior can be predicted to compensate time delay introduced by a non-ideal communication channel. This results in increased robustness and fidelity of the closed-loop system. In literature, models of the remote environment, the teleoperator dynamics or task-specific operator models are integrated into single-user teleoperation systems. The present paper is the first that explicitly considers dyadic haptic interaction between two operators in the prediction algorithms applied to a multi-user teleoperation system. Our comparative experimental results obtained in a 3 degree-of-freedom teleoperation system show an increased robustness and fidelity of this approach compared to a classic bilateral force-force architecture.


Control Architecture Virtual Linkage Constant Time Delay Haptic Interaction Remote Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lawrence, D.: Stability and transparency in bilateral teleoperation. In: Proceedings of the 31st IEEE Conference on Decision and Control 1992, vol. 3, pp. 2649–2655 (1993)Google Scholar
  2. 2.
    Passenberg, C., Peer, A., Buss, M.: A survey of environment-, operator-, and task-adapted controllers for teleoperation systems. Journal of Mechatronics (2010), doi: 10.1016/j.mechatronics.2010.04.005Google Scholar
  3. 3.
    Clarke, S., Schillhuber, G., Zaeh, M.F., Ulbrich, H.: Prediction-based methods for teleoperation across delayed networks. Multimedia Systems 13, 253–261 (2008)CrossRefGoogle Scholar
  4. 4.
    Kheddar, A.: Teleoperation based on the hidden robot concept. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 31, 1–13 (2001)CrossRefGoogle Scholar
  5. 5.
    Mitra, P., Niemeyer, G.: Model-mediated Telemanipulation. The International Journal of Robotics Research 27(2), 253–262 (2008)CrossRefGoogle Scholar
  6. 6.
    Weber, C., Nitsch, V., Unterhinninghofen, U., Färber, B., Buss, M.: Position and force augmentation in a telepresence system and their effects on perceived realism. In: Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)Google Scholar
  7. 7.
    Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M., Sastry, S.: Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control 49, 1453–1464 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Tang, P., de Silva, C.: Ethernet-based predictive control of an industrial hydraulic machine. In: Proceedings, 42nd IEEE Conference on Decision and Control 2003, vol. 1, pp. 695–700 (December 2003)Google Scholar
  9. 9.
    Smith, C., Christensen, H.I.: A minimum jerk predictor for teleoperation with variable time delay. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Saint Louis, USA, pp. 5621–5627. IEEE/RSJ (October 2009)Google Scholar
  10. 10.
    Smith, C., Jensfelt, P.: A predictor for operator input for time-delayed teleoperation. Mechatronics (2010), doi: 10.1016/j.mechatronics.2010.03.002Google Scholar
  11. 11.
    Buss, M., Lee, K.-K., Nitzsche, N., Peer, A., Stanczyk, B., Unterhinninghofen, U.: Advanced Telerobotics: Dual-Handed and Mobile Remote Manipulaton. In: Advances in telerobotics, pp. 471–497. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Sallnäs, E.-L.: Improved precision in mediated collaborative manipulation of objects by haptic force feedback. In: Proceedings of the First International Workshop on Haptic Human-Computer Interaction, London, UK, pp. 69–75. Springer (2001)Google Scholar
  13. 13.
    Reed, K.B., Peshkin, M.A.: Physical collaboration of human-human and human-robot teams. IEEE Trans. Haptics 1(2), 108–120 (2008)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Peer, A., Buss, M.: An HMM approach to realistic haptic human-robot interaction. In: Proceedings of The Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, WorldHaptics (2009)Google Scholar
  15. 15.
    Hölldampf, J., Peer, A., Buss, M.: Virtual Partner for a Haptic Interaction Task. In: Human-Centered Robot Systems, pp. 183–191. Springer (2009)Google Scholar
  16. 16.
    Feth, D., Groten, R., Peer, A., Buss, M.: Control-theoretic model of haptic human-human interaction in a pursuit tracking task. In: Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication, Ro-Man (2009)Google Scholar
  17. 17.
    Groten, R., Feth, D., Goshy, H., Peer, A., Kenny, D., Buss, M.: Experimental analysis of dominance in haptic collaboration. In: Proceedings of the 18th International Symposium on Robot and Human Interactive Communication, Ro-Man (2009)Google Scholar
  18. 18.
    Hashtrudi-Zaad, K., Salcudean, S.E.: Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators. The International Journal of Robotics Research 20, 419–445 (2001)CrossRefGoogle Scholar
  19. 19.
    Sakr, N., Georganas, N., Zhao, J., Shen, X.: Motion and force prediction in haptic media. In: IEEE International Conference on Multimedia and Expo, pp. 2242–2245 (July 2007)Google Scholar
  20. 20.
    Uchiyama, M., Iwasawa, N., Hakomori, K.: Hybrid position/force control for coordination of a two-arm robot. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 1242–1247 (1987)Google Scholar
  21. 21.
    Williams, D., Khatib, O.: The virtual linkage: a model for internal forces in multi-grasp manipulation, vol. 1, pp. 1025–1030 (May 1993)Google Scholar
  22. 22.
    Buss, M., Peer, A., Schauß, T., Stefanov, N., Unterhinninghofen, U., Behrendt, S., Leupold, J., Durkovic, M., Sarkis, M.: Development of a multi-modal multi-user telepresence and teleaction system. The International Journal of Robotics Research (2009)Google Scholar
  23. 23.
    Ganesh, G., Albu-Schäffer, A., Haruno, M., Kawato, M., Burdet, E.: Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2705–2711 (2010)Google Scholar

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© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Institute of Automatic Control EngineeringTechnische Universität MünchenMunichGermany

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