Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466 (2015)
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). https://doi.org/10.1007/978-3-540-30301-5_51
Sanna, A., Lamberti, F., Paravati, G., Manuri, F.: A Kinect-based natural interface for quadrotor control. Entertain. Comput. 4(3), 179–186 (2013)
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)
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 publication
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)
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)
Nef, T., Mihelj, M., Riener, R.: ARMin: a robot for patient-cooperative arm therapy. Med. Biol. Eng. Comput. 45(9), 887–900 (2007)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Schmidt, R.A., Wrisberg, C.A.: Motor Learning and Performance (2004)
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)
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)
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)
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)
Cherpillod, A., Mintchev, S., Floreano, D.: Embodied flight with a drone. arXiv preprint arXiv:1707.01788 (2017)
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)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Rognon, C., Wu, A.R., Mintchev, S., Ijspeert, A., Floreano, D. (2018). Haptic Guidance with a Soft Exoskeleton Reduces Error in Drone Teleoperation. In: Prattichizzo, D., Shinoda, H., Tan, H., Ruffaldi, E., Frisoli, A. (eds) Haptics: Science, Technology, and Applications. EuroHaptics 2018. Lecture Notes in Computer Science(), vol 10894. Springer, Cham. https://doi.org/10.1007/978-3-319-93399-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-93399-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93398-6
Online ISBN: 978-3-319-93399-3
eBook Packages: Computer ScienceComputer Science (R0)