Autonomous Robots

, Volume 12, Issue 3, pp 313–324 | Cite as

A Social Robot that Stands in Line

  • Yasushi Nakauchi
  • Reid Simmons


Recent research in mobile robot navigation make it feasible to utilize autonomous robots in service fields. But, such applications require more than just navigation. To operate in a peopled environment, robots should recognize and act according to human social behavior. In this paper, we present the design and implementation of one such social behavior: a robot that stands in line much as people do. The system employs stereo vision to recognize lines of people, and uses the concept of personal space for modeling the social behavior. Personal space is used both to detect the end of a line and to determine how much space to leave between the robot and the person in front of it. Our model of personal space is based on measurements from people forming lines. We demonstrate our ideas with a mobile robot navigation system that can purchase a cup of coffee, even if people are waiting in line for service.

service robot social interaction stand in line personal space people detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Brunelli, R. and Poggio, T. 1995. Template matching: Matched spatial filters and beyond. M.I.T., Cambridge, MA, A.I. Memo, 1536.Google Scholar
  2. Fitzgibbon, A.W., Pilu, M., and Fisher, R.B. 1996. Direct least square fitting of ellipses. Department of Artif. Intell., The University of Edinburgh, Scotland, DAI Research Paper 794.Google Scholar
  3. HelpMate Robotics Inc., Scholar
  4. Illingworth, J. and Kittler, J. 1988. A survey of the Hough transform. Computer Vision, Graphics, and Image Processing, 44:87–116.Google Scholar
  5. Koenig, S., Goodwin, R., and Simmons, R. 1996. Robot navigation with Markov models: A framework for path planning and learning with limited computational resources. In Reasoning with Uncertainty in Robotics, Lecture Notes in Artificial Intelligence, vol. 1093, Dorst, van Lambalgen and Voorbraak (Eds.), Springer: Berlin, pp. 322–337.Google Scholar
  6. Konolige, K. 1997. Small vision systems: Hardware and implementation. In Proc. Eighth International Symposium on Robotics Research.Google Scholar
  7. Malmberg, M. 1980. Human Territoriality: Survey of Behavioural Territories in Man with Preliminary Analysis and Discussion of Meaning, Mouton Publishers.Google Scholar
  8. Matsui, T. et al. 1997. An office conversation mobile robot that learns by navigation and conversation. In Proc. of RealWorld Computing Symposium, pp. 59–62.Google Scholar
  9. Nakauchi, Y. and Simmons, R. 1999. Social behavioral robot that stands in line. In Proc. of IEEE SMC, pp. II-993–998.Google Scholar
  10. Oren, M., Papageorgiou, C., Shinha, P., Osuna, E., and Poggio, T. 1997. A trainable system for people detection. In Proc. of Image Understanding Workshop, pp. 207–214.Google Scholar
  11. Rosin, P.L. 1993. Anote on the least square fitting of ellipses. Pattern Recognition Letters, 14:799–808.Google Scholar
  12. Rowley, H., Baluja, S., and Kanade, T. 1998. Neural network-based face detection. In Proc. of IEEE PAMI.Google Scholar
  13. Sack, R. 1986. Human Territoriality. Cambridge University Press: Cambridge, UK.Google Scholar
  14. Simmons, R., Goodwin, R., Zita Haigh, K., Koenig, S., and O'Sullivan, J. 1997. A layered architecture for office delivery robots. In Proc. of Autonomous Agents, pp. 245–252.Google Scholar
  15. Thrun, S. et al., 1999. MINERVA: A second generation mobile tour-guide robot. In Proc. of IEEE ICRA'99.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Yasushi Nakauchi
    • 1
  • Reid Simmons
    • 2
  1. 1.Department of Computer ScienceNational Defense AcademyYokosukaJapan
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations