Skip to main content

Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1093)

Abstract

Path planning is one of the most widely studied problems in robot navigation. It deals with estimating an optimal set of waypoints from an initial to a target coordinate. New generations of assistive robots should be able to compute these paths considering not only obstacles but also social conventions. This ability is commonly referred to as social navigation. This paper describes a new socially-acceptable path-planning framework where robots avoid entering areas corresponding to the personal spaces of people, but most importantly, areas related to human-human and human-object interaction. To estimate the social cost of invading personal spaces we use the concept of proxemics. To model the social cost of invading areas where interaction is happening we include the concept of object interaction space. The framework uses Dijkstra’s algorithm on a uniform graph of free space where edges are weighed according to the social traversal cost of their outbound node. Experimental results demonstrate the validity of the proposal to plan socially-accepted paths.

Keywords

  • Social navigation
  • Path-planning
  • Dijkstra

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-36150-1_53
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-36150-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Notes

  1. 1.

    The actual detection of humans is out of the scope of the paper. In the experiments carried out it was performed by the Human agent of the CORTEX architecture.

References

  1. Gomez, J., Mavridis, N., Garrido, S.: Social path planning: generic human-robot interaction framework for robotic navigation tasks. In: Workshop on Cognitive Robotics Systems: Replicating Human Actions and Activities at IEEE/RSJ International Conference on Robots and Systems (2013)

    Google Scholar 

  2. Lichtenthaler, C., Peters, A., Griffiths, S., Kirsch, A.: Social navigation - identifying robot navigation patterns in a path crossing scenario. In: Lecture Notes in Computer Science, vol. 8239, pp. 84–93 (2013)

    Google Scholar 

  3. Charalampous, K., Kostavelis, I., Gasteratos, A.: Robot navigation in large-scale social maps: an action recognition approach. Expert Syst. Appl. 66, 261–273 (2016)

    CrossRef  Google Scholar 

  4. Vega, A., Manso, L., Bustos, P., Núñez, P., Macharet, D.: Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances. Pattern Recogn. Lett. 1, 72–84 (2019)

    CrossRef  Google Scholar 

  5. Foux, G., Heymann, M., Bruckstein, A.: Two-dimensional robot navigation among unknown stationary polygonal obstacles. IEEE Trans. Robot. Autom. 9, 96–102 (1993)

    CrossRef  Google Scholar 

  6. Weihua, C., Tie, Z., Yanbiao, Z.: Mobile robot path planning based on social interaction space in social environment. Int. J. Adv. Rob. Syst. 1, 1–10 (2018)

    Google Scholar 

  7. Kruse, T., Pandey, A., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Robot. Auton. Syst. 61(12), 1726–1743 (2013)

    CrossRef  Google Scholar 

  8. Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware navigation: a survey. Int. J. Soc. Robot. 7(2), 137–153 (2015)

    CrossRef  Google Scholar 

  9. Rios-Martinez, J.: Socially-aware robot navigation: combining risk assessment and social conventions. Ph.d. Inria, France (2013)

    Google Scholar 

  10. Althaus, P., Ishiguro, H., Kanda, T., Miyashita, T., Christensen, H.I.: Navigation for human robot interaction tasks. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 1894–1989 (2004)

    Google Scholar 

  11. Kirby, R., Simmons, R., Forlizzi, J.: COMPANION: a constraint-optimizing method for person-acceptable navigation. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 607–612 (2009)

    Google Scholar 

  12. Tranberg Hansen, S., Svenstrup, M., Andersen, H.J., Bak, T.: Adaptive human aware navigation based on motion pattern analysis. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 927–932 (2009)

    Google Scholar 

  13. Photchara, R., Mae, Y., Ohara, K., Kojima, M., Arai, T.: Social navigation model based on human intention analysis using face orientation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 1, pp. 1682–1688 (2010)

    Google Scholar 

  14. LaValle, S.: Planning Algorithms. Cambridge University Press, Cambridge (2006)

    CrossRef  Google Scholar 

  15. Lam, C., Chou, C., Chiang, K., Fu, C.: Human-centered robot navigation - towards a harmoniously human-robot coexisting environment. IEEE Trans. Rob. 27(1), 99–112 (2011)

    CrossRef  Google Scholar 

  16. Bustos, P., Manso, L., Bandera, A., Bandera, J.P., García-Varea, I., Martínez-Gómez, J.: The CORTEX cognitive robotics architecture: use cases. Cogn. Syst. Res. 55, 107–123 (2019)

    CrossRef  Google Scholar 

  17. Okal, B., Arras, K.: Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning. In: IEEE International Conference on Robotics and Automation, pp. 2889 – 2895 (2016)

    Google Scholar 

  18. Kostavelis, I.: Robot behavioral mapping: a representation that consolidates the human-robot coexistence. Robot. Autom. Eng. 1, 1–3 (2017)

    Google Scholar 

Download references

Acknowledgment

This work has been partially supported by the National project RTI2018-099522-B-C42. by the Extremaduran Government projects GR15120, IB18056 and by the FEDER project 0043-EUROAGE-4-E (Interreg V-A Portugal-Spain - POCTEP).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pedro Núñez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Vega, A., Cintas, R., Manso, L.J., Bustos, P., Núñez, P. (2020). Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_53

Download citation