Effects of Haptic Feedback in Dual-Task Teleoperation of a Mobile Robot

  • José CorujeiraEmail author
  • José Luís Silva
  • Rodrigo Ventura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10515)


Teleoperation system usage is challenging to human operators, as this system has a predominantly visual interface that limits the ability to acquire situation awareness, (e.g. maintain a safe teleoperation). This limitation coupled with the dual-task problem of teleoperating a mobile robot, negatively affects the operators cognitive load and motor skills. Our motivation is to offload some of the visual information to a secondary perceptual channel (haptic), by proposing an assisted teleoperation system. This system uses haptic feedback to alert the operator of obstacle proximity, without directly influencing the operator’s command inputs. The objective of this paper, is to evaluate and validate the efficacy of our system’s haptic feedback, by providing the obstacle proximity information to the operator. The user experiment was conducted to emulate the dual-task problem, by having a concurrent task for cognitive distraction. Our results showed significant differences in time to complete the navigation task and the duration of collisions, between the haptic feedback condition and the control condition.


Teleoperation Human-robot interaction Haptic feedback Mobile robots 



The work described in this paper was carried out with the support of ARDITI – Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação under Project M1420 - 09-5369-FSE-000001- PhD Scholarship, whose support we gratefully acknowledge. This work was also supported from Fundação para a Ciência e a Tecnologia (FCT, Portugal), through project UID/EEA/50009/2013. This research has been funded through ERAChair Grant Agreement 621413.


  1. 1.
    Bejczy, A.K.: Teleoperation, telerobotics. In: The Mechanical Systems Design Handbook, Pasadena, California, vol. 3, no. 2, pp. 205–214 (2001)Google Scholar
  2. 2.
    Chen, J.Y.C., Haas, E.C., Barnes, M.J.: Human performance issues and user interface design for teleoperated robots. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1231–1245 (2007). doi: 10.1109/TSMCC.2007.905819 CrossRefGoogle Scholar
  3. 3.
    Trouvain, B.: Teleoperation of unmanned vehicles: the human factor (2006)Google Scholar
  4. 4.
    Adamides, G., Christou, G., Katsanos, C., Xenos, M., Hadzilacos, T.: Usability guidelines for the design of robot teleoperation: a taxonomy. IEEE Trans. Hum.-Mach. Syst. 45(2), 256–262 (2015)CrossRefGoogle Scholar
  5. 5.
    Sanguino, T.J.M., Márquez, J.M.A., Carlson, T., Millán, J.D.R.: Improving skills and perception in robot navigation by an augmented virtuality assistance system. J. Intell. Robot. Syst. Theory Appl. 1–12 (2014)Google Scholar
  6. 6.
    Chua, W.L.K., Johnson, M., Eskridge, T., Keller, B.: AOA: ambient obstacle avoidance interface. In: Proceedings of IEEE International Workshop on Robot and Human Interactive Communication, vol. 2014, no. October, pp. 18–23 (2014)Google Scholar
  7. 7.
    Reveleau, A., Ferland, F., Labbé, M., Létourneau, D., Michaud, F.: Visual representation of sound sources and interaction forces in a teleoperation interface for a mobile robot. J. Hum.-Robot Interact. 4(2), 1 (2015)CrossRefGoogle Scholar
  8. 8.
    Yanco, H.A., Drury, J.: ‘Where am i?’ acquiring situation awareness using a remote robot platform. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), vol. 3, pp. 2835–2840 (2004)Google Scholar
  9. 9.
    Nielsen, C.W., Goodrich, M.A., Ricks, R.W.: Ecological interfaces for improving mobile robot teleoperation. IEEE Trans. Robot. 23(5), 927–941 (2007)CrossRefGoogle Scholar
  10. 10.
    Hacinecipoglu, A., Konukseven, E.I., Koku, A.B.: Evaluation of haptic feedback cues on vehicle teleoperation performance in an obstacle avoidance scenario. In: 2013 World Haptics Conference, WHC 2013, pp. 689–694 (2013)Google Scholar
  11. 11.
    Dragan, A.D., Srinivasa, S.S.: Online customization of teleoperation interfaces. In: Proceedings of IEEE International Workshop on Robot Human Interactive Communication, pp. 919–924 (2012)Google Scholar
  12. 12.
    Quintas, J., Almeida, L., Sousa, E., Menezes, P.: A context-aware immersive interface for teleoperation of mobile robots (2015)Google Scholar
  13. 13.
    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
  14. 14.
    Lee, S., Sukhatme, G., Kim, G.J., Park, C.-M.: Haptic teleoperation of a mobile robot: a user study. Presence Teleoperators Virtual Environ. 14(3), 345–365 (2005)CrossRefGoogle Scholar
  15. 15.
    de Barros, P.G., Lindeman, R.W.: Multi-sensory urban search-and-rescue robotics: improving the operatoror’s omni-directional perception. Front. Robot. AI 1, 1–15 (2014)CrossRefGoogle Scholar
  16. 16.
    Brandt, A.M., Colton, M.B.: Haptic collision avoidance for a remotely operated quadrotor UAV in indoor environments. In: 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 2724–2731 (2010)Google Scholar
  17. 17.
    Casper, J., Murphy, R.R.: Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Trans. Syst. Man Cybern. Part B Cybern. 33(3), 367–385 (2003)CrossRefGoogle Scholar
  18. 18.
    Ryu, D., Hwang, C.S., Kang, S., Kim, M., Song, J.B.: Wearable haptic-based multi-modal teleloperation of field mobile manipulator for explosive ordnance disposal. In: IEEE International Workshop on Safety, Security and Rescue Rototics, pp. 98–103 (2005)Google Scholar
  19. 19.
    Valero-Gomez, A., Gomez, J.V., Garrido, S., Moreno, L.: The path to efficiency: fast marching method for safer, more efficient mobile robot trajectories. IEEE Robot. Autom. Mag. 20(4), 111–120 (2013)CrossRefGoogle Scholar
  20. 20.
    De Barros, P.G., Lindeman, R.W., Ward, M.O.: Enhancing robot teleoperator situation awareness and performance using vibro-tactile and graphical feedback. In: Proceedings of 2011 IEEE Symposium on 3D User Interfaces, 3DUI, pp. 47–54 (2011)Google Scholar
  21. 21.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Mg, A.: ROS: an open-source robot operating system. ICRA 3(3), 5 (2009)Google Scholar
  22. 22.
    Hedges, R.: Stage (2008). Accessed 24 Sept 2016
  23. 23.
    Open source robotics foundation: TurtleBot (2016). Accessed 24 Sept 2016
  24. 24.
    Royston, J.P.: Algorithm AS 181: the W test for normality. Appl. Stat. 31(2), 176 (1982)CrossRefGoogle Scholar
  25. 25.
    Conover, W.J.: Practical nonparametric statistics. Statistician 22, 309–314 (1971)Google Scholar
  26. 26.
    Tukey, J.W.: Exploratory data analysis. Analysis 2(1999), 688 (1977)zbMATHGoogle Scholar
  27. 27.
    Hoaglin, D.C., Iglewicz, B.: Fine-tuning some resistant rules for outlier labeling. J. Am. Stat. Assoc. 82(400), 1147–1149 (1987)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • José Corujeira
    • 1
    • 2
    Email author
  • José Luís Silva
    • 2
    • 3
  • Rodrigo Ventura
    • 4
  1. 1.Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.Madeira-ITIFunchalPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IULLisbonPortugal
  4. 4.Institute for Systems and Robotics, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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