International Journal of Social Robotics

, Volume 6, Issue 3, pp 443–455 | Cite as

Towards a Socially Acceptable Collision Avoidance for a Mobile Robot Navigating Among Pedestrians Using a Pedestrian Model

  • Masahiro Shiomi
  • Francesco Zanlungo
  • Kotaro Hayashi
  • Takayuki Kanda
Article

Abstract

Safe navigation is a fundamental capability for robots that move among pedestrians. The traditional approach in robotics to attain such a capability has treated pedestrians as moving obstacles and provides algorithms that assure collision-free motion in the presence of such moving obstacles. In contrast, recent studies have focused on providing the robot not only collision-free motion but also a socially acceptable behavior by planning the robot’s path to maintain a “social distance” from pedestrians and respect their personal space. Such a social behavior is perceived as natural by the pedestrians and thus provides them a comfortable feeling, even if it may be considered a decorative element from a strictly safety oriented perspective. In this work we develop a system that realizes human-like collision avoidance in a mobile robot. In order to achieve this goal, we use a pedestrian model from human science literature, a version of the popular Social Force Model that was specifically designed to reproduce conditions similar to those found in shopping malls and other pedestrians facilities. Our findings show that the proposed system, which we tested in 2-h field trials in a real world environment, not only is perceived as comfortable by pedestrians but also yields safer navigation than traditional collision-free methods, since it better fits the behavior of the other pedestrians in the crowd.

Keywords

Safe navigation Collision avoidance Pedestrian modeling Field experiments 

References

  1. 1.
    Burgard W, Cremers AB, Fox D, Hänel D, Lakemeyer G, Schulz D, Steiner W, Thrun S (1998) The interactive museum tour-guide robot. In: Proceedings of the National Conference on Artificial Intelligence, pp 11–18Google Scholar
  2. 2.
    Thrun S, Bennewitz M, Burgard W, Cremers AB, Dellaert F, Fox D, Hahnel D, Rosenberg C, Roy N, Schulte J, Schulz D (1999) Minerva: a second-generation museum tour-guide robot. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 1999–2005Google Scholar
  3. 3.
    Gross H-M, Boehme H, Schroeter C, Mueller S, Koenig A, Einhorn E, Martin C, Merten M, Bley A (2009) TOOMAS: interactive shopping guide robots in everyday use—final implementation and experiences from long-term field trials. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 2005–2012Google Scholar
  4. 4.
    Huttenrauch H, Eklundh KS (2002) Fetch-and-Carry with Cero: observations from a long-term user study with a service robot. In: Proceedings of IEEE International Workshop on Robot and Human Interactive Communication, pp 158–163Google Scholar
  5. 5.
    Fox D, Burgard W, Thruny S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33CrossRefGoogle Scholar
  6. 6.
    Stachniss C, Burgard W (2002) An integrated approach to goal-directed obstacle avoidance under dynamic constraints for dynamic environments. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 508–513Google Scholar
  7. 7.
    Seder M, Petrovic I (2007) Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 1986–1992Google Scholar
  8. 8.
    Belkhouche F (Aug. 2009) Reactive path planning in a dynamic environment. IEEE Trans Robot 25(4):902–911Google Scholar
  9. 9.
    Goller M, Steinhardt F, Kerscher T, Zollner JM, Dillmann R (2010) Proactive Avoidance of moving obstacles for a service robot utilizing a behavior-based control. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 5984–5989Google Scholar
  10. 10.
    Luber M, Stork JA, Tipaldi GD, Arras KO (2010) People tracking with human motion predictions from social forces. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 464–469Google Scholar
  11. 11.
    Ellis D, Sommerlade E, Reid I (2010) Modelling Pedestrian Trajectories with Gaussian Processes. In: Proceedings of International Workshop on Visual Surveillance, pp 1229–1234Google Scholar
  12. 12.
    Ziebart BD, Ratliff N, Gallagher G, Mertz C, Peterson K (2009) Planning-based prediction for pedestrians, In: Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3931–3936Google Scholar
  13. 13.
    Zanlungo F, Ikeda T, Kanda T (2011) Social force model with explicit collision prediction. Europhys Lett 93:68005CrossRefGoogle Scholar
  14. 14.
    Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):875–883CrossRefGoogle Scholar
  15. 15.
    Pacchierotti E, Christensen HI, Jensfelt P (2006) Evaluation of passing distance for social robots. In: Proceedings of IEEE International Work on Robot and Human Interactive Communication, pp 315–320Google Scholar
  16. 16.
    Albert Wu, Jonathan PJ (2012) Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles. Auton Robot 32(3):227–242CrossRefGoogle Scholar
  17. 17.
    Lu Yibiao, Huo Xiaoming, Arslan O, Tsiotras P (2011) Incremental Multi-Scale Search Algorithm for Dynamic Path Planning. IEEE Trans Syst Man Cybern Part B 41(6):1556–1570CrossRefGoogle Scholar
  18. 18.
    Henry P, Vollmer C, Ferris B, Fox D (2010) Learning to navigate through crowded environments. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 981–986Google Scholar
  19. 19.
    Tamura Y, Fukuzawa T, Asama H (2010) Smooth collision avoidance in human-robot coexisting environment. In: Proceedings of IEEE/RSJ International Conference on Intelligent robots and systems, pp 3887–3892Google Scholar
  20. 20.
    Ratsamee P, Mae Y, Ohara K, Takubo T, Arai T (2013) Human-robot collision avoidance using a modified social force model with body pose and face orientation. Int J Hum Robot 10(1):1350008CrossRefGoogle Scholar
  21. 21.
    Hall ET (1969) The hidden dimension. Anchor Books, New YorkGoogle Scholar
  22. 22.
    Walters ML, Dautenhahn K, Boekhorst R, Koay KL, 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 IEEE International Workshop on Robot and Human Interactive Communication, pp 347–352Google Scholar
  23. 23.
    Michalowski MP, Sabanovic S, Simmons R (2006) A spatial model of engagement for a social robot. In: Proceedings of IEEE International Workshop on advanced motion control, pp 762–767Google Scholar
  24. 24.
    Kirby R, Simmons R, Forlizzi J (2009) COMPANION: a constraint optimizing method for person-acceptable navigation. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)Google Scholar
  25. 25.
    Pandey AK, Alami R (2010) A framework towards a socially aware mobile robot motion in human-centered dynamic environment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Google Scholar
  26. 26.
    Qian K, Ma X, Dai X, Fang F (2010) Socially acceptable pre-collision safety strategies for human-compliant navigation of service robots. Adv Robot 24(13):1813–1840CrossRefGoogle Scholar
  27. 27.
    Lichtenthaeler C., Lorenz T., Kirsch A. (2012) Influence of Legibility on Perceived Safety in a Virtual Human-Robot Path Crossing Task. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)Google Scholar
  28. 28.
    Rios-Martinez J, Renzaglia A, Spalanzani A, Martinelli A, Laugier C (2012) Navigating between people: a stochastic optimization approach. In: IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  29. 29.
    Luber M, Spinello L, Silva J, Arras KO (2012) Socially-aware robot navigation: a learning approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Google Scholar
  30. 30.
    Fraichard T (2007) A Shorth Paper about Motion Safety. In: IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  31. 31.
    Glas DF, Miyashita T, Ishiguro H, Hagita N (2009) Laser-based tracking of human position and orientation using parametric shape modeling. Adv Robot 23(4):405–428CrossRefGoogle Scholar
  32. 32.
    Van den Berg J, Ling M, Manocha D (2008) Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  33. 33.
    Bennewitz M, Burgard W, Cielniak G, Thrun S (2005) Learning motion patterns of people for compliant robot motion. Int J Robot Res 24(1):31–48CrossRefGoogle Scholar
  34. 34.
    Ziebart BD, Ratliff N, Gallagher G, Mertz C Peterson K (2009) Planning-Based Prediction for Pedestrians. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3931–3936Google Scholar
  35. 35.
    Trautman P, Krause A (2010) Unfreezing the Robot: navigation in Dense, Interacting Crowds. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 797–803Google Scholar
  36. 36.
    Helbing D, Farkas IJ, Molnar P, Vicsek T (2002) Simulation of pedestrian crowds in normal and evacuation situations. Pedestr Evac Dyn 21:31–58Google Scholar
  37. 37.
    Moussaïd M, Helbing D, Garnier S, Johansson A, Combe M, Theraulaz G (2009) Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc R Soc B 276:2755–2762CrossRefGoogle Scholar
  38. 38.
    Curtis S, Zafar B, Gutub B, Manocha D (2012) Right of way. Vis Comput 25:1–16Google Scholar
  39. 39.
    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):e10047CrossRefGoogle Scholar
  40. 40.
    Zanlungo F, Ikeda T, Kanda T (2012) A microscopic social norm model to obtain realistic macroscopic velocity and density pedestrian distributions. PLoS One 7(12):e50720CrossRefGoogle Scholar
  41. 41.
    Zanlungo F, Ikeda T, Kanda T (2014) Potential for the dynamics of pedestrians in a socially interacting group. Phys Rev E 89(1):021811CrossRefGoogle Scholar
  42. 42.
    Kidokoro H, Kanda T, Brščic D, Shiomi M (2012) Will I bother here?: a robot anticipating its influence on pedestrian walking comfort. In: Proceedings of the 8th ACM/IEEE international conference on Human-robot, interaction, pp 259–266Google Scholar
  43. 43.
    Guy SJ, Curtis S, Lin MC, Manocha D (2012) Least-effort trajectories lead to emergent crowd behaviors. Phys Rev E 85(1):016110CrossRefGoogle Scholar
  44. 44.
    Kanda T, Miyashita T, Osada T, Haikawa Y, Ishiguro H (2008) Analysis of humanoid appearances in human-robot interaction. IEEE Trans Robot 24(3):725–735CrossRefGoogle Scholar
  45. 45.
    Helbing D, Molnar P (2009) Social force model for pedestrian dynamics. Phys Rev E 51:4282–4286 (Belkhouche F, 1995)CrossRefGoogle Scholar
  46. 46.
    Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:487–490CrossRefGoogle Scholar
  47. 47.
    Helbing D, Johansson A (2010) Pedestrian, Crowd and Evacuation Dynamics. Encycl Complex Syst Sci 16:6476–6495Google Scholar
  48. 48.
    Reynolds CW (1999) Steering behaviors for autonomous characters. Game Developers Conf 1061:763–782Google Scholar
  49. 49.
    Lämmel G, Plaue M (2012) Getting out of the way: collision avoiding pedestrian models compared to the real world. Pedestr Evac Dyn 2014:1275–1289Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Masahiro Shiomi
    • 1
    • 2
  • Francesco Zanlungo
    • 1
    • 2
  • Kotaro Hayashi
    • 1
    • 2
  • Takayuki Kanda
    • 1
    • 2
  1. 1.ATR-IRCKyotoJapan
  2. 2.JST, CRESTTokyoJapan

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