Pebbles: User-Configurable Device Network for Robot Navigation

  • Kentaro Ishii
  • Haipeng Mi
  • Lei Ma
  • Natsuda Laokulrat
  • Masahiko Inami
  • Takeo Igarashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8118)


This study proposes devices suitable for use by non-experts to design robot navigation routes. The user places landmarks, called pebbles, on the floor to tell navigation routes to a robot. Using infrared communication, the pebbles automatically generate navigation routes. The system is designed such that non-expert users can understand the system status to configure the user’s target environment without expert assistance. During deployment, the system provides LED and voice feedback. The user can confirm that the devices are appropriately placed for the construction of a desired navigation network. In addition, because there is a device at each destination, our method can name locations by associating a device ID with a particular name. A user study showed that non-expert users were able to understand device usage and construct robot navigation routes.


Robot Navigation Tangible User Interface Navigation Landmark Non-Expert User 


  1. 1.
    Leonard, J.J., Durrant-Whyte, H.F.: Simultaneous Map Building and Localization for an Autonomous Mobile Robot. In: IEEE/RSJ International Workshop on Intelligent Robots and Systems, vol. 3, pp. 1442–1447 (1991)Google Scholar
  2. 2.
    Shiomi, M., Sakamoto, D., Kanda, T., Ishi, C.T., Ishiguro, H., Hagita, N.: A Semi-autonomous Communication Robot - A Field Trial at a Train Station -. In: ACM/IEEE Annual Conference on Human-Robot Interaction, pp. 303–310 (2008)Google Scholar
  3. 3.
    Ishii, K., Takeoka, Y., Inami, M., Igarashi, T.: Drag-and-Drop Interface for Registration-Free Object Delivery. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 228–233 (2010)Google Scholar
  4. 4.
    Park, S., Hashimoto, S.: Indoor localization for autonomous mobile robot based on passive RFID. In: IEEE International Conference on Robotics and Biomimetics, pp. 1856–1861 (2009)Google Scholar
  5. 5.
    Park, S., Hashimoto, S.: Autonomous Mobile Robot Navigation Using Passive RFID in Indoor Environment. IEEE Transactions on Industrial Electronics 56(7), 2366–2373 (2009)CrossRefGoogle Scholar
  6. 6.
    Mi, H., Ishii, K., Ma, L., Laokulrat, N., Inami, M., Igarashi, T.: Pebbles: An Interactive Configuration Tool for Indoor Robot Navigation. In: Annual ACM Symposium on User Interface Software and Technology, Demonstrations, pp. 11–12 (2012)Google Scholar
  7. 7.
    Bahl, P., Padmanabhan, V.N.: RADAR: An In-Building RF-based User Location and Tracking System. In: The Conference on Computer Communications, Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 (2000)Google Scholar
  8. 8.
    Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical Robust Localization over Large-Scale 802.11 Wireless Networks. In: Annual International Conference on Mobile Computing and Networking, pp. 70–84 (2004)Google Scholar
  9. 9.
    Nishida, Y., Aizawa, H., Hori, T., Hoffman, N.H., Kanade, T., Kakikura, M.: 3D Ultrasonic Tagging System for Observing Human Activity. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 785–791 (2003)Google Scholar
  10. 10.
    Chang, H., Choi, J., Kim, M.: Experimental research of probabilistic localization of service robots using range image data and indoor GPS system. In: IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1021–1027 (2006)Google Scholar
  11. 11.
    Niwa, H., Kodaka, K., Sakamoto, Y., Otake, M., Kawaguchi, S., Fujii, K., Kanemori, Y., Sugano, S.: GPS-based Indoor Positioning system with Multi-Channel Pseudolite. In: IEEE International Conference on Robotics and Automation, pp. 905–910 (2008)Google Scholar
  12. 12.
    Gonzalez, J., Blanco, J.L., Galindo, C., Ortiz-de-Galisteo, A., Fernaindez-Madrigal, J.A., Moreno, F.A., Martinez, J.L.: Combination of UWB and GPS for indoor-outdoor vehicle localization. In: IEEE International Symposium on Intelligent Signal Processing, pp. 1–6 (2007)Google Scholar
  13. 13.
  14. 14.
    Ishiguro, H.: Distributed Vision System: A Perceptual Information Infrastructure for Robot Navigation. In: International Joint Conference on Artificial Intelligence, pp. 36–41 (1997)Google Scholar
  15. 15.
    Saito, S., Hiyama, A., Tanikawa, T., Hirose, M.: Indoor Marker-based Localization Using Coded Seamless Pattern for Interior Decoration. In: IEEE Virtual Reality Conference, pp. 67–74 (2007)Google Scholar
  16. 16.
    Nakazato, Y., Kanbara, M., Yokoya, N.: Localization System for Large Indoor Environments Using Invisible Markers. In: ACM Symposium on Virtual Reality Software and Technology, pp. 295–296 (2008)Google Scholar
  17. 17.
    Miyama, S., Imai, M., Anzai, Y.: Rescue Robot under Disaster Situation: Position Acquisition with Omni-directional Sensor. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 4, pp. 3132–3137 (2003)Google Scholar
  18. 18.
    Pugh, J., Martinoli, A.: Relative Localization and Communication Module for Small-Scale Multi-Robot Systems. In: IEEE International Conference on Robotics and Automation, pp. 188–193 (2006)Google Scholar
  19. 19.
    Pugh, J., Raemy, X., Favre, C., Falconi, R., Martinoli, A.: A Fast Onboard Relative Positioning Module for Multirobot Systems. IEEE Transactions on Mechatronics 14(2), 151–162 (2009)CrossRefGoogle Scholar
  20. 20.
    Roberts, J.F., Stirling, T.S., Zufferey, J.C., Floreano, D.: 2.5D Infrared Range and Bearing System for Collective Robotics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3659–3664 (2009)Google Scholar
  21. 21.
    Yap, T.N., Shelton, C.R.: SLAM in Large Indoor Environments with Low-Cost, Noisy, and Sparse Sonars. In: IEEE International Conference on Robotics and Automation, pp. 1395–1401 (2009)Google Scholar
  22. 22.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. In: National Conference on Artificial Intelligence, pp. 593–598 (2002)Google Scholar
  23. 23.
    Davison, A.J., Murray, D.W.: Mobile robot localisation using active vision. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 809–825. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  24. 24.
    Hwang, S.Y., Song, J.B.: Monocular Vision-Based SLAM in Indoor Environment Using Corner, Lamp, and Door Features From Upward-Looking Camera. IEEE Transactions on Industrial Electronics 58(10), 4804–4812 (2011)CrossRefGoogle Scholar
  25. 25.
    Pirker, K., Ruther, M., Bischof, H., Schweighofer, G., Mayer, H.: An Omnidirectional Time-of-Flight Camera and its Application to Indoor SLAM. In: International Conference on Control, Automation, Robotics and Vision, pp. 988–993 (2010)Google Scholar
  26. 26.
    Ishii, H., Ullmer, B.: Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms. In: ACM SIGCHI Conference on Human Factors in Computer Systems, pp. 234–241 (1997)Google Scholar
  27. 27.
    Dijkstra, E.W.: A Note on Two Problems in Connexion with Graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Kentaro Ishii
    • 1
    • 2
  • Haipeng Mi
    • 1
    • 2
  • Lei Ma
    • 1
    • 2
  • Natsuda Laokulrat
    • 1
    • 2
  • Masahiko Inami
    • 3
    • 2
  • Takeo Igarashi
    • 1
    • 2
  1. 1.The University of TokyoTokyoJapan
  2. 2.IGARASHI Design Interface ProjectJST, ERATOTokyoJapan
  3. 3.Keio UniversityYokohamaJapan

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