Skip to main content
Log in

Socially aware robot navigation system in human interactive environments

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

In social environments, humans mostly stay in social interactive groups with their daily activities. A mobile service robot must be aware of not only human individuals but also social interactive groups, and then behave safely and socially (politely and, respectively) in human interactive environments. In this paper, we propose a social reactive control (SRC) that enables a mobile service robot to navigate safely and socially in the human interactive environments. The SRC is derived by incorporating both states of individuals (position, orientation, motion, and human field of view) and social interactive groups (group’s types, group’s centre, group’s radius, and group’s velocity) into the conventional social force model . The SRC can be combined with a conventional path planning technique to generate a socially aware robot navigation system that is capable of controlling mobile service robots to traverse with socially acceptable behaviours. We validate the effectiveness of the proposed social reactive control through a series of real-world experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://pedsim.silmaril.org/.

  2. https://github.com/srl-freiburg.

  3. https://youtu.be/G5BdforJx60.

References

  1. Arras KO, Mozos OM, Burgard W (2007) Using boosted features for the detection of people in 2d range data. In: IEEE international conference on robotics and automation, pp 3402–3407

  2. Daniel C, Daniel H, Karl T (2015) Towards human-safe navigation with pro-active collision avoidance in a shared workspace. In: IROS 2015 workshop on on-line decision-making in multi-robot coordination

  3. Ferrer G, Garrell A, Sanfeliu A (2013) Robot companion: a social-force based approach with human awareness-navigation in crowded environments. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1688–1694

  4. Hall ET (1963) A system for the notation of proxemic behavior. Am Anthropol 65:1003–1026

    Article  Google Scholar 

  5. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    Article  Google Scholar 

  6. Helbing D, Molnr P (1995) Social force model for pedestrian dynamics. Phys Rev E 51:4282–4286

    Article  Google Scholar 

  7. Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580

    Article  Google Scholar 

  8. Kruse T, Pandey AK, Alami R, Kirsch A (2013) Human-aware robot navigation: a survey. Robot Auton Syst 61:1726–1743

    Article  Google Scholar 

  9. LaValle SM, Kuffner JJ Jr (2001) Randomized kinodynamic planning. Int J Robot Res 20(5):378–400

    Article  Google Scholar 

  10. Moussaid 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):e10,047

    Article  Google Scholar 

  11. Munaro M, Basso F, Menegatti E (2012) Tracking people within groups with rgb-d data. In: IEEE/RSJ international conference on intelligent robots and systems, pp 2101–2107

  12. Ng AY, Russell SJ (2000) Algorithms for inverse reinforcement learning. In: Proceedings of the seventeenth international conference on machine learning, pp 663–670

  13. Pantic M, Vinciarelli A (2014) Social signal processing. The Oxford Handbooks of Affective Computing, p 84

  14. PrimeSense NITE (2011) http://www.openni.org. Accessed 6 August 2012

  15. Quigley M, Gerkey B, Conley K, Faust J, Foote T, Leibs J, Berger E, Wheeler R, Ng A (2009) ROS: an open-source Robot Operating System. ICRA Workshop Open Source Softw 32:151–170

    Google Scholar 

  16. Ratsamee P, Mae Y, Ohara K, Kojima M, Arai T (2013) Social navigation model based on human intention analysis using face orientation. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1682–1687

  17. Rios-Martinez J, Spalanzani A, Laugier C (2011) Understanding human interaction for probabilistic autonomous navigation using risk-rrt approach. In: IEEE/RSJ international conference on intelligent robots and systems, pp 2014–2019

  18. Rios-Martinez J, Spalanzani A, Laugier C (2014) From proxemics theory to socially-aware navigation: a survey. Int J Soc Robot 7:137–153

    Article  Google Scholar 

  19. Setti F, Russell C, Bassetti C, Cristani M (2015) F-formation detection: individuating free-standing conversational groups in images. PLoS One 10(5):e0123,783

    Article  Google Scholar 

  20. Shiomi M, Zanlungo F, Hayashi K, Kanda T (2014) Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model. Int J Soc Robot 6(3):443–455

    Article  Google Scholar 

  21. Siegwart R, Nourbakhsh IR, Scaramuzza D (2011) Introduction to autonomous mobile robots. MIT Press, Cambridge

  22. Stentz A (1994) The d* algorithm for real-time planning of optimal traverses. Tech. rep., Tech. Rep. CMU-RI-TR-94-37, The Robotics Institute, Carnegie-Mellon University

  23. Truong XT, Ngo TD (2016) Dynamic social zone based mobile robot navigation for human comfortable safety in social environments. Int J Soc Robot 8(5):663–684

    Article  Google Scholar 

  24. Truong XT, Voo NY, Ngo TD (2015) RGB-D and laser data fusion-based human detection and tracking for socially aware robot navigation framework. In: Proceedings of the 2015 IEEE conference on robotics and biomimetics, pp 608–613

  25. Vinciarelli A, Pantic M, Bourlard H (2009) Social signal processing: survey of an emerging domain. Image Vis Comput 27(12):1743–1759

    Article  Google Scholar 

  26. Zanlungo F, Ikeda T, Kanda T (2011) Social force model with explicit collision prediction. EPL (Europhys Lett) 93(6):68,005

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuan-Tung Truong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Truong, XT., Yoong, V.N. & Ngo, TD. Socially aware robot navigation system in human interactive environments. Intel Serv Robotics 10, 287–295 (2017). https://doi.org/10.1007/s11370-017-0232-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-017-0232-y

Keywords

Navigation