skip to main content
research-article
Open Access

Walking together: side-by-side walking model for an interacting robot

Authors Info & Claims
Published:10 July 2014Publication History
Skip Abstract Section

Abstract

This paper presents a computational model for side-by-side walking within human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of two walking partners. Previous studies only considered a robot moving alongside a person without collisions and with simple velocity-based predictions. In contrast, our proposed model includes two major considerations. First, it considers the current goal, modeling side-by-side walking as a process of moving toward a goal while maintaining a relative position with the partner. Second, it takes the partner's utility into consideration; it models side-by-side walking as a phenomenon where two agents maximize mutual utilities rather than only considering a single agent utility. The model is constructed based in a set of trajectories from pairs of people recorded in side-by-side walking; then, parameters of the model were calibrated for a mobile robot and tested in an autonomous robot walking side-by-side with participants. Finally, two evaluations were performed. The first evaluation shows that the proposed model considering mutual utilities performs better than a single utility method and a method that keeps distance from the walking partner. In the second evaluation the proposed method was used for a robot deployed in a shopping mall environment where it demonstrated to be effective.

References

  1. Berlin, M., Gray, J., Thomaz, A. L., & Breazeal, C. (2006). Perspective taking: An organizing principle for learning in human-robot interaction. In Proceedings of the National Conference on Artificial Intelligence. Boston, MA: AAAI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Besl, P., & McKay, N. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Costa, M. (2010). Interpersonal distances in group walking. Journal of Nonverbal Behavior, 34(1), 15--26.Google ScholarGoogle ScholarCross RefCross Ref
  4. Garrell, A., & Sanfeliu, A. (2010). Model validation: Robot behavior in people guidance mission using DTM model and estimation of human motion behavior. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google ScholarGoogle ScholarCross RefCross Ref
  5. Glas, D. F., Miyashita, T., Ishiguro, H., & Hagita, N. (2009). Laser-based tracking of human position and orientation using parametric shape modeling. Advanced Robotics, 23(4), 405--428.Google ScholarGoogle ScholarCross RefCross Ref
  6. Gockley, R., Forlizzi, J., & Simmons, R. (2007). Natural person-following behavior for social robots. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Gross, H.-M., Boehme, H.-J., Schroeter, C., Mueller, S., Koenig, A., Martin, Ch., Merten, M., & Bley, A. (2008). ShopBot: Progress in developing an interactive mobile shopping assistant for everyday use. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC).Google ScholarGoogle ScholarCross RefCross Ref
  8. Hüttenrauch, H., Severinson Eklundh, K., Green, A., & Topp, E. A. (2006). Investigating spatial relationships in human-robot interactions. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google ScholarGoogle ScholarCross RefCross Ref
  9. Hall, E. T. (1966). The hidden dimension: Man's use of space in public and private. London, UK: The Bodley Head Ltd.Google ScholarGoogle Scholar
  10. Helbing, D., Farkas, I. J., Molnár, P., & Vicsek, T. (2002). Simulation of pedestrian crowds in normal and evacuation situations. In M. Schreckenberg, M.S. S. D. Sharma, & S. D. Sharma (Eds.), Pedestrian and Evacuation Dynamics (pp. 21--58). Berlin: Springer.Google ScholarGoogle Scholar
  11. Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282--4286.Google ScholarGoogle Scholar
  12. Hoffman, G., & Breazeal, C. (2007). Effects of anticipatory action on human-robot teamwork. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hoogendoorn, S., & Bovy, P. H. L. (2003). Simulation of pedestrian flows by optimal control and differential games. Optimal Control Applications and Methods, 24(3), 153--172.Google ScholarGoogle ScholarCross RefCross Ref
  14. Iwamura, Y., Shiomi, M., Kanda, T., Ishiguro, H., & Hagita, N. (2011). Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets? In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kahn, P., Freier, N., Kanda, T., Ishiguro, H., Ruckert, J., Severson, R., & Kane, S. (2008). Design patterns for sociality in human-robot interaction. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kendon, A. (1990). Spatial organization in social encounters: The F-formation system. In A. Kendon (Ed.), Conducting interaction: Patterns of behavior in focused encounters (pp. 209--238): Cambridge, UK: Cambridge University Press.Google ScholarGoogle Scholar
  17. Kobayashi, Y., Kinpara, Y., Takano, E., Kuno, Y., Yamazaki, K., & Yamazaki, A. (2011). Robotic wheelchair moving with caregiver collaboratively depending on circumstances. In Proceedings of the extended abstracts on the ACM Conference on Human Factors in Computing Systems (CHI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kuzuoka, H., Suzuki, Y., Yamashita, J., & Yamazaki, K. (2010). Reconfiguring spatial formation arrangement by robot body orientation. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Montello, D. R. (2005). Navigation. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking (pp. 257--294). Cambridge, UK: Cambridge University Press.Google ScholarGoogle Scholar
  20. Montemerlo, M., Thrun, S., & Whittaker, W. (2002). Conditional particle filters for simultaneous mobile robot localization and people-tracking. In Proceedings of the International Conference on Robotics and Automation.Google ScholarGoogle ScholarCross RefCross Ref
  21. Morales, Y., Satake, S., Huq, R., Glas, D., Kanda, T., & Hagita, N. (2012). How do people walk side-by-side? Using a computational model of human behavior for a social robot. In Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Morales, Y., Satake, S., Kanda, T., & Hagita, N. (2011). Modeling environments from a route perspective. In Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI). Lausanne, Switzerland. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010). The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS ONE, 5(4).Google ScholarGoogle Scholar
  24. Nüchter, A., Lingemann, K., Hertzberg, J., & Surmann, H. (2007). 6d slam-3d mapping outdoor environments. Journal of Field Robotics, 24(8--9), 699--722. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Nüchter, A., Lingemann, K., Sprickerhof, J., Borrmann, D., Elseberg, J., Schneider, P., & Qui, D. (2011). Slam6d - Simultaneous localization and mapping with 6 DOF. Retrieved May, 20, 2011, from http://www.openslam.org/slam6d.htmlGoogle ScholarGoogle Scholar
  26. Pineau, J., Montemerlo, M., Pollack, M., Roy, N., & Thrun, S. (2003). Towards robotic assistants in nursing homes: Challenges and results. Robotics and Autonomous Systems, 42(3--4), 271--281.Google ScholarGoogle Scholar
  27. Prassler, E., Bank, D., & Kluge, B. (2002). Key technologies in robot assistants: Motion coordination between a human and a mobile robot. Transactions on Control, Automation and Systems Engineering, 4(1), 56--61.Google ScholarGoogle Scholar
  28. Ralston, Henry J. (1958). Energy-speed relation and optimal speed during level walking. Internationale Zeitschrift für Angewandte Physiologie Einschließlich Arbeitsphysiologie, 17(4), 277--283.Google ScholarGoogle Scholar
  29. Sisbot, E. A., Marin-Urias, L. F., Alami, R., & Simeon, T. (2007). A human aware mobile robot motion planner. IEEE Transactions on Robotics, 23(5), 874--883. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Sviestins, E., Mitsunaga, N., & Kanda, T. (2007). Speed adaptation for a robot walking with a human. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). doi> Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics (Intelligent robotics and autonomous agents). Cambridge, MA: The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Trafton, J. G., Cassimatis, N. L., Bugajska, M. D., Brock, D. P., Mintz, F. E., & Schultz, A. C. (2005). Enabling effective human-robot interaction using perspective- taking in robots. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans, 35(4), 460--470. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Walters, M. L., Dautenhahn, K., Boekhorst, R. te, Koay, K. L., Kaouri, C., Woods, S., Nehaniv, C., Lee, D., & Werry, I. (2005). The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment. In Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN).Google ScholarGoogle ScholarCross RefCross Ref
  34. Wang, C.-C., & Thorpe, C. (2002). Simultaneous localization and mapping with detection and tracking of moving objects. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Washington, D.C.Google ScholarGoogle Scholar
  35. Xu, S., & Duh, H. B.-L. (2010). A simulation of bonding effects and their impacts on pedestrian dynamics. IEEE Transactions on Intelligent Transportation Systems, 11(1), 153--161. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Zanlungo, F., Ikeda, T., & Kanda, T.. (2011). Social force model with explicit collision prediction. Europhysics Letters, 93(6), 68005.Google ScholarGoogle ScholarCross RefCross Ref
  37. Zanlungo, F., & Kanda, T. (2013). Do walking pedestrians stabily interact inside a large group? Analysis of group and sub-group spatial structure. In Proceedings of the 35th Annual Meeting of the Cognitive Science Society (COGSCI). Berlin, Germany.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader