Teleoperation of Domestic Service Robots: Effects of Global 3D Environment Maps in the User Interface on Operators’ Cognitive and Performance Metrics

  • Marcus Mast
  • Michal Španěl
  • Georg Arbeiter
  • Vít Štancl
  • Zdeněk Materna
  • Florian Weisshardt
  • Michael Burmester
  • Pavel Smrž
  • Birgit Graf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8239)

Abstract

This paper investigates the suitability of visualizing global 3D environment maps generated from RGB-D sensor data in teleoperation user interfaces for service robots. We carried out a controlled experiment involving 27 participants, four teleoperation tasks, and two types of novel global 3D mapping techniques. Results show substantial advantages of global 3D mapping over a control condition for three of the four tasks. Global 3D mapping in the user interface lead to reduced search times for objects and to fewer collisions. In most situations it also resulted in less operator workload, higher situation awareness, and higher accuracy of operators’ mental models of the remote environment.

Keywords

global environment map service robot teleoperation user interface 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mast, M., Burmester, M., Krüger, K., Fatikow, S., Arbeiter, G., Graf, B., Kronreif, G., Pigini, L., Facal, D., Qiu, R.: User-Centered Design of a Dynamic-Autonomy Remote Interaction Concept for Manipulation-Capable Robots to Assist Elderly people in the Home. Journal of Human-Robot Interaction 1, 96–118 (2012)CrossRefGoogle Scholar
  2. 2.
    Nielsen, C.W., Goodrich, M.A., Ricks, R.W.: Ecological Interfaces for Improving Mobile Robot Teleoperation. IEEE Transactions on Robotics 23, 927–941 (2007)CrossRefGoogle Scholar
  3. 3.
    Labonté, D., Boissy, P., Michaud, F.: Comparative Analysis of 3-D Robot Teleoperation Interfaces With Novice Users. IEEE T. Syst. Man. Cyb. B 40, 1331–1342 (2010)CrossRefGoogle Scholar
  4. 4.
    Bruemmer, D.J., Few, D.A., Boring, R.L., Marble, J.L., et al.: Shared Understanding for Collaborative Control. IEEE T. Syst. Man. Cyb. A 35, 494–504 (2005)CrossRefGoogle Scholar
  5. 5.
    Drury, J.L., Scholtz, J., Yanco, H.A.: Awareness in Human-Robot Interactions. In: Proc. IEEE Int. Conf. Syst. Man. Cyb., pp. 912–918 (2003)Google Scholar
  6. 6.
    Kitchin, R.M.: Cognitive Maps: What Are They and Why Study Them? Journal of Environmental Psychology 14, 1–19 (1994)CrossRefGoogle Scholar
  7. 7.
    Fong, T., Thorpe, C., Baur, C.: Advanced Interfaces for Vehicle Teleoperation: Collaborative Control, Sensor Fusion Displays, and Remote Driving Tools. Autonomous Robots 11, 77–85 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Fiala, M.: Pano-Presence for Teleoperation. In: Proc. IROS, pp. 3798–3802 (2005)Google Scholar
  9. 9.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots 34, 189–206 (2013)CrossRefGoogle Scholar
  10. 10.
    Yguel, M., Aycard, O.: 3D mapping of outdoor environment using clustering techniques. In: Proc. IEEE International Conference on Tools with Artificial Intelligence, pp. 403–408 (2011)Google Scholar
  11. 11.
    Arbeiter, G., Bormann, R., Fischer, J., Hägele, M., Verl, A.: Towards Geometric Mapping for Semi-Autonomous Mobile Robots. In: Stachniss, C., Schill, K., Uttal, D. (eds.) Spatial Cognition 2012. LNCS (LNAI), vol. 7463, pp. 114–127. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Kakiuchi, Y., Ueda, R., Okada, K., Inaba, M.: Creating Household Environment Map for Environment Manipulation Using Color Range Sensors on Environment and Robot. In: Proc. ICRA, pp. 305–310 (2011)Google Scholar
  13. 13.
    Reiser, U., Connette, C., Fischer, J., Kubacki, J., Bubeck, A., Weisshardt, F., Jacobs, T., Parlitz, C., Hägele, M., Verl, A.: Care-O-bot 3 - Creating a product vision for service robot applications by integrating design and technology. In: Proc. IROS, pp. 1992–1998 (2009)Google Scholar
  14. 14.
  15. 15.
    ROS documentation, http://www.ros.org/wiki/
  16. 16.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload, pp. 139–183. North Holland, Amsterdam (1988)CrossRefGoogle Scholar
  17. 17.
    Taylor, R.M.: Situational awareness rating Technique (SART): The Development of a Tool for Aircraft Systems Design. Proc. AGARD No. 478, pp. 3/1–3/17 (1989)Google Scholar
  18. 18.
    Wirth, W., Hartmann, T., Böcking, S., Vorderer, P., Klimmt, C., et al.: A Process Model of the Formation of Spatial Presence Experiences. Media Psychology 9, 493–525 (2007)CrossRefGoogle Scholar
  19. 19.
    Jones, C.M., Healy, S.D.: Differences in cue use and spatial memory in men and women. Proceedings of the Royal Society, B 273, 2241–2247 (2006)CrossRefGoogle Scholar
  20. 20.
    Peters, M., Laeng, B., et al.: A Redrawn Vandenberg & Kuse Mental Rotations Test: Different Versions and Factors That Affect Performance. Brain and Cogn. 28, 39–58 (1995)CrossRefGoogle Scholar
  21. 21.
    Bender, R., Lange, S.: Adjusting for multiple testing – when and how? Journal of Clinical Epidemiology 54, 343–349 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcus Mast
    • 1
    • 2
  • Michal Španěl
    • 3
  • Georg Arbeiter
    • 4
  • Vít Štancl
    • 3
  • Zdeněk Materna
    • 3
  • Florian Weisshardt
    • 4
  • Michael Burmester
    • 1
  • Pavel Smrž
    • 3
  • Birgit Graf
    • 4
  1. 1.Stuttgart Media UniversityGermany
  2. 2.Linköping UniversitySweden
  3. 3.Brno University of TechnologyCzech Republic
  4. 4.Fraunhofer Institute for Manufacturing Engineering and AutomationGermany

Personalised recommendations