Haptic Guidance with a Soft Exoskeleton Reduces Error in Drone Teleoperation

  • Carine RognonEmail author
  • Amy R. Wu
  • Stefano Mintchev
  • Auke Ijspeert
  • Dario Floreano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10894)


Haptic guidance has been shown to improve performance in many fields as it can give additional information without overloading other sensory channels such as vision or audition. Our group is investigating new intuitive ways to interact with robots, and we developed a suit to control drones with upper body movement, called the FlyJacket. In this paper, we present the integration of a cable-driven haptic guidance in the FlyJacket. The aim of the device is to apply a force relative to the distance between the drone and a predetermined trajectory to correct user torso orientation and improve the flight precision. Participants (n = 10) flying a simulated fixed-wing drone controlled with torso movements tested four different guidance profiles (three linear profiles with different stiffness and one quadratic). Our results show that a quadratically shaped guidance, which gives a weak force when the error is small and a strong force when the error becomes significant, was the most effective guidance to improve the performance. All participants also reported through questionnaires that the haptic guidance was useful for flight control.


Wearable haptics and exoskeletons Teleoperation and telepresence Robotics 



The authors would like to acknowledge Alexandre Cherpillod for the implementation of the error calculation in the drone simulator and thanks Claire Donnat for her help with the statistical analysis. This work has been supported by the Swiss National Center of Competence in Research in Robotics (NCCR Robotics).


  1. 1.
    Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466 (2015)CrossRefGoogle Scholar
  2. 2.
    Murphy, R.R., Tadokoro, S., Nardi, D., Jacoff, A., Fiorini, P., Choset, H., Erkmen, A.M.: Search and rescue robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1151–1173. Springer, Heidelberg (2008). Scholar
  3. 3.
    Sanna, A., Lamberti, F., Paravati, G., Manuri, F.: A Kinect-based natural interface for quadrotor control. Entertain. Comput. 4(3), 179–186 (2013)CrossRefGoogle Scholar
  4. 4.
    Pfeil, K., Koh, S.L., LaViola, J.: Exploring 3D gesture metaphors for interaction with unmanned aerial vehicles. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 257–266. ACM, Santa Monica (2013)Google Scholar
  5. 5.
    Miehlbradt, J., Cherpillod, A., Mintchev, S., Coscia, M., Artoni, F., Floreano, D., Micera, S.: A data-driven body-to-machine interface for the effortless control of drones, submitted for publicationGoogle Scholar
  6. 6.
    Rognon, C., Mintchev, S., Dell’Agnola, F., Cherpillod, A., Atienza, D., Floreano, D.: FlyJacket: an upper-body soft exoskeleton for immersive drone control. IEEE Robot. Autom. Lett. 3(3), 2362–2369 (2018)CrossRefGoogle Scholar
  7. 7.
    Coad, M.M., Okamura, A.M., Wren, S., Mintz, Y., Lendvay, T.S., Jarc, A.M., Nisky, I.: Training in divergent and convergent force fields during 6-DOF teleoperation with a robot-assisted surgical system. In: IEEE World Haptics Conference, pp. 195–200. IEEE, Munich (2017)Google Scholar
  8. 8.
    Nef, T., Mihelj, M., Riener, R.: ARMin: a robot for patient-cooperative arm therapy. Med. Biol. Eng. Comput. 45(9), 887–900 (2007)CrossRefGoogle Scholar
  9. 9.
    Rauter, G., von Zitzewitz, J., Duschau-Wicke, A., Vallery, H., Riener, R.: A tendon-based parallel robot applied to motor learning in sports. In: Proceedings of 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 82–87. IEEE, Tokyo (2010)Google Scholar
  10. 10.
    Sigrist, R., Rauter, G., Riener, R., Wolf, P.: Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon. Bull. Rev. 20(1), 21–53 (2013)CrossRefGoogle Scholar
  11. 11.
    Lam, T.M., Mulder, M., van Paassen, M.R.: Haptic interface in UAV tele-operation using force-stiffness feedback. In: International Conference on Systems, Man and Cybernetics, pp. 835–840. IEEE, San Antonio (2009)Google Scholar
  12. 12.
    Son, H.I., Kim, J., Chuang, L., Franchi, A., Giordano, P.R., Lee, D., Bülthoff, H.H.: An evaluation of haptic cues on the tele-operator’s perceptual awareness of multiple UAVs’ environments. In: World Haptics Conference, pp. 149–154. IEEE, Istanbul (2011)Google Scholar
  13. 13.
    Omari, S., Hua, M.D., Ducard, G., Hamel, T.: Bilateral haptic teleoperation of VTOL UAVs. In: IEEE International Conference on Robotics and Automation, pp. 2393–2399. IEEE, Karlsruhe (2013)Google Scholar
  14. 14.
    Hou, X., Mahony, R., Schill, F.: Comparative study of haptic interfaces for bilateral teleoperation of VTOL aerial robots. IEEE Trans. Syst. Man Cybern. Syst. 46(10), 1352–1363 (2016)CrossRefGoogle Scholar
  15. 15.
    Kanso, A., Elhajj, I.H., Shammas, E., Asmar, D.: Enhanced teleoperation of UAVs with haptic feedback. In: IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 305–310. IEEE, Busan (2015)Google Scholar
  16. 16.
    van Asseldonk, E.H., Wessels, M., Stienen, A.H., van der Helm, F.C., van der Kooij, H.: Influence of haptic guidance in learning a novel visuomotor task. J. Physiol. Paris 103(3), 276–285 (2009)CrossRefGoogle Scholar
  17. 17.
    Mulder, M., Abbink, D.A., Boer, E.R.: The effect of haptic guidance on curve negotiation behavior of young, experienced drivers. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 804–809. IEEE, Singapore (2008)Google Scholar
  18. 18.
    Forsyth, B.A., MacLean, K.E.: Predictive haptic guidance: intelligent user assistance for the control of dynamic tasks. IEEE Trans. Vis. Comput. Graph. 12(1), 103–113 (2006)CrossRefGoogle Scholar
  19. 19.
    Schmidt, R.A., Wrisberg, C.A.: Motor Learning and Performance (2004)Google Scholar
  20. 20.
    McGill, S., Seguin, J., Bennett, G.: Passive stiffness of the lumber torso in flexion, extension, lateral bending, and axial rotation: effect of belt wearing and breath holding. Spine 19(6), 696–704 (1994)CrossRefGoogle Scholar
  21. 21.
    Mcneill, T., Warwick, D., Andersson, G., Schultz, A.: Trunk strengths in attempted flexion, extension, and lateral bending in healthy subjects and patients with low-back disorders. Spine 5(6), 529–538 (1980)CrossRefGoogle Scholar
  22. 22.
    Graves, J.E., Pollock, M.L., Carpenter, D.M., Leggett, S.H., Jones, A., MacMillan, M., Fulton, M.: Quantitative assessment of full range-of-motion isometric lumbar extension strength. Spine 15(4), 289–294 (1990)CrossRefGoogle Scholar
  23. 23.
    Rebelo, J., Sednaoui, T., den Exter, E.B., Krueger, T., Schiele, A.: Bilateral robot teleoperation: a wearable arm exoskeleton featuring an intuitive user interface. IEEE Robot. Autom. Magaz. 21(4), 62–69 (2014)CrossRefGoogle Scholar
  24. 24.
    Cherpillod, A., Mintchev, S., Floreano, D.: Embodied flight with a drone. arXiv preprint arXiv:1707.01788 (2017)
  25. 25.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carine Rognon
    • 1
    Email author
  • Amy R. Wu
    • 2
  • Stefano Mintchev
    • 1
  • Auke Ijspeert
    • 2
  • Dario Floreano
    • 1
  1. 1.Laboratory of Intelligent SystemsEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  2. 2.Biorobotics LaboratoryEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations