Towards Coexistence of Human and Robot: How Ubiquitous Computing Can Contribute?

  • Jingyuan Cheng
  • Xiaoping Chen
  • Paul Lukowicz
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 345)

Abstract

After the ISO 10218-1/2 in 2011, safety factors for industry robot are standardized. As robotics expands its area from industry further into service, educational, healthcare and etc., both human and robot are exposed to a space with more openness and less certainty. Because there is no common safety specification, we raise in this paper our own hypotheses on the safety requirements in dense human-robot co-existing scenarios and focus more on demonstrating the possibilities provided by the research field named Ubiquitous Computing.

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References

  1. 1.
    World robotics industrial robots 2013 - summary; service robots 2013 - summary. Technical report, IFR Statistical Department, VDMA Robotics and Automation association (2013), http://www.worldrobotics.org/uploads/media/Executive_Summary_WR_2013.pdf
  2. 2.
    ISO10218-1:2011: Robots and robotic devices – safety requirements for industrial robots, part 1: Robots (2011)Google Scholar
  3. 3.
    ISO10218-2:2011: Robots and robotic devices – safety requirements for industrial robots, part 2: Robot systems and integration (2011)Google Scholar
  4. 4.
    Vasic, M., Billard, A.: Safety issues in human-robot interactions. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 197–204. IEEE (2013)Google Scholar
  5. 5.
    Jiang, B.C., Gainer Jr., C.A.: A cause-and-effect analysis of robot accidents. Journal of Occupational Accidents 9(1), 27–45 (1987)CrossRefGoogle Scholar
  6. 6.
    Weiser, M.: The computer for the 21st century. Scientific American 265(3), 94–104 (1991)CrossRefGoogle Scholar
  7. 7.
    Sanpechuda, T., Kovavisaruch, L.: A review of rfid localization: Applications and techniques. In: 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2008, vol. 2, pp. 769–772. IEEE (2008)Google Scholar
  8. 8.
    Mantyjarvi, J., Paternò, F., Salvador, Z., Santoro, C.: Scan and tilt: towards natural interaction for mobile museum guides. In: Proceedings of the 8th Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 191–194. ACM (2006)Google Scholar
  9. 9.
    Mamei, M., Zambonelli, F.: Pervasive pheromone-based interaction with rfid tags. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 2(2), 4 (2007)CrossRefGoogle Scholar
  10. 10.
    Utsumi, Y., Kato, Y., Kunze, K., Iwamura, M., Kise, K.: Who are you?: A wearable face recognition system to support human memory. In: Proceedings of the 4th Augmented Human International Conference, pp. 150–153. ACM (2013)Google Scholar
  11. 11.
    Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)CrossRefGoogle Scholar
  12. 12.
    Arkin, R.C.: Homeostatic control for a mobile robot: Dynamic replanning in hazardous environments. Journal of Robotic Systems 9(2), 197–214 (1992)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Haddadin, S., Albu-Schäffer, A., Hirzinger, G.: Safety evaluation of physical human-robot interaction via crash-testing. In: Robotics: Science and Systems, vol. 3, pp. 217–224 (2007)Google Scholar
  14. 14.
    Cannata, G., Maggiali, M., Metta, G., Sandini, G.: An embedded artificial skin for humanoid robots. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2008, pp. 434–438. IEEE (2008)Google Scholar
  15. 15.
    Graf, B., Hägele, M.: Dependable interaction with an intelligent home care robot. In: Proceedings of ICRA-Workshop on Technical Challenge for Dependable Robots in Human Environments, pp. 21–26 (2001)Google Scholar
  16. 16.
    Schmitz, N., Spranger, C., Berns, K.: 3d audio perception system for humanoid robots. In: Second International Conferences on Advances in Computer-Human Interactions, ACHI 2009, pp. 181–186. IEEE (2009)Google Scholar
  17. 17.
    DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)CrossRefGoogle Scholar
  18. 18.
    Alavi, B., Pahlavan, K.: Modeling of the toa-based distance measurement error using uwb indoor radio measurements. IEEE Communications Letters 10(4), 275–277 (2006)CrossRefGoogle Scholar
  19. 19.
    Chintalapudi, K., Padmanabha Iyer, A., Padmanabhan, V.N.: Indoor localization without the pain. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, pp. 173–184. ACM (2010)Google Scholar
  20. 20.
    Evennou, F., Marx, F.: Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. Eurasip Journal on Applied Signal Processing 2006, 164–164 (2006)Google Scholar
  21. 21.
    Leppäkoski, H., Collin, J., Takala, J.: Pedestrian navigation based on inertial sensors, indoor map, and wlan signals. Journal of Signal Processing Systems 71(3), 287–296 (2013)CrossRefGoogle Scholar
  22. 22.
    Pirkl, G., Lukowicz, P.: Robust, low cost indoor positioning using magnetic resonant coupling. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 431–440. ACM (2012)Google Scholar
  23. 23.
    Zhou, B., Cheng, J., Sundholm, M., Lukowicz, P.: From smart clothing to smart table cloth: Design and implementation of a large scale, textile pressure matrix sensor. In: Maehle, E., Römer, K., Karl, W., Tovar, E. (eds.) ARCS 2014. LNCS, vol. 8350, pp. 159–170. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  24. 24.
    Cheng, J., Bannach, D., Adamer, K., Bernreiter, T., Lukowicz, P.: A wearable, conductive textile based user interface for hospital ward rounds document access. In: Roggen, D., Lombriser, C., Tröster, G., Kortuem, G., Havinga, P. (eds.) EuroSSC 2008. LNCS, vol. 5279, pp. 182–191. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2000, vol. 1, pp. 321–328. IEEE (2000)Google Scholar
  26. 26.
    Brooks, R.R., Iyengar, S.S.: Multi-sensor fusion: fundamentals and applications with software. Prentice-Hall, Inc. (1998)Google Scholar
  27. 27.
    Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Computer Networks 52(12), 2292–2330 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jingyuan Cheng
    • 1
  • Xiaoping Chen
    • 2
  • Paul Lukowicz
    • 1
  1. 1.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany
  2. 2.Multi-Agent Systems LabUniversity of Science and Technology of ChinaHefeiChina

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