Skip to main content
SpringerLink
Log in
Book cover

Workshop of Physical Agents

WAF 2020: Advances in Physical Agents II pp 3–17Cite as

Defining Adaptive Proxemic Zones for Activity-Aware Navigation

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1285))

Abstract

Many of the tasks that a service robot can perform at home involve navigation skills. In a real world scenario, the navigation system should consider individuals beyond just objects, theses days it is necessary to offer particular and dynamic representation in the scenario in order to enhance the HRI experience. In this paper, we use the proxemic theory to do this representation. The proxemic zones are not static. The culture or the context influences them and, if we have this influence into account, we can increase humans’ comfort. Moreover, there are collaborative tasks in which these zones take different shapes to allow the task’s best performance. This research develops a layer, the social layer, to represent and distribute the proxemics zones’ information in a standard way, through a cost map and using it to perform a social navigate task. We have evaluated these components in a simulated scenario, performing different collaborative and human-robot interaction tasks and reducing the personal area invasion in a 32%. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF), as well as instructions to facilitate the reproducibility of the results.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bera, A., Randhavane, T., Prinja, R., Manocha, D.: Sociosense: robot navigation amongst pedestrians with social and psychological constraints. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7018–7025 (2017). https://doi.org/10.1109/IROS.2017.8206628

  2. Billy Okal, T.L.: pedsim\_ros (2016). https://github.com/jginesclavero/pedsim_ros/tree/eloquent-dev

  3. Breazeal, C.L.: Designing Sociable Robots. MIT Press, Cambridge (2004)

    Book  Google Scholar 

  4. Butler, J.T., Agah, A.: Psychological effects of behavior patterns of a mobile personal robot. Auton. Robots 10(2), 185–202 (2001). https://doi.org/10.1023/A:1008986004181

  5. Coles, A.J., Coles, A.I., Fox, M., Long, D.: Forward-chaining partial-order planning. In: Twentieth International Conference on Automated Planning and Scheduling (2010)

    Google Scholar 

  6. Foote, T.: TF: the transform library. In: 2013 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Open-Source Software workshop, pp. 1–6 (2013). https://doi.org/10.1109/TePRA.2013.6556373

  7. Fox, M., Long, D.: PDDL2. 1: An extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61–124 (2003)

    Google Scholar 

  8. Galvan, M., Repiso, E., Sanfeliu, A.: Robot navigation to approach people using G2-spline path planning and extended social force model. In: Advances in Intelligent Systems and Computing. AISC, vol. 1093, pp. 15–27. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36150-1_2

  9. Ginés, J., Martín, F., Vargas, D., Rodríguez, F.J., Matellán, V.: Social navigation in a cognitive architecture using dynamic proxemic zones. Sensors 19(23) (2019). https://doi.org/10.3390/s19235189

  10. Gloor, C.: Pedsim: pedestrian crowd simulation, vol. 5, no. 1 (2016). http://pedsim.silmaril.org

  11. Hall, E.T.: The Hidden Dimension, vol. 609. Doubleday, Garden City (1910)

    Google Scholar 

  12. Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995). https://doi.org/10.1103/physreve.51.4282

  13. Kirby, R.: Social robot navigation. ProQuest Dissertations and Theses, no. 3470165, p. 232 (2010)

    Google Scholar 

  14. Kirby, R., Simmons, R., Forlizzi, J.: Companion: a constraint-optimizing method for person-acceptable navigation. In: RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 607–612 (2009). https://doi.org/10.1109/ROMAN.2009.5326271

  15. Koay, K.L., Syrdal, D., Bormann, R., Saunders, J., Walters, M.L., Dautenhahn, K.: Initial design, implementation and technical evaluation of a context-aware proxemics planner for a social robot. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 10652, pp. 12–22. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-70022-9_2

  16. Kostavelis, I., Kargakos, A., Giakoumis, D., Tzovaras, D.: Robot’s workspace enhancement with dynamic human presence for socially-aware navigation. In: Liu, M., Chen, H., Vincze, M. (eds.) Computer Vision Systems, pp. 279–288. Springer, Cham (2017)

    Google Scholar 

  17. Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Elsevier. https://www.sciencedirect.com/science/article/pii/S0921889013001048

  18. Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Robot. Auton. Syst. 61(12), 1726 – 1743 (2013). https://doi.org/10.1016/j.robot.2013.05.007. http://www.sciencedirect.com/science/article/pii/S0921889013001048

  19. Lu, D.V., Hershberger, D., Smart, W.D.: Layered cost maps for context-sensitive navigation. In: IEEE International Conference on Intelligent Robots and Systems, pp. 709–715. Institute of Electrical and Electronics Engineers Inc. (2014). https://doi.org/10.1109/IROS.2014.6942636

  20. Lu, D.V., Smart, W.D.: Towards more efficient navigation for robots and humans. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1707–1713 (2013). https://doi.org/10.1109/IROS.2013.6696579

  21. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K.: The office marathon: robust navigation in an indoor office environment. In: 2010 IEEE International Conference on Robotics and Automation, pp. 300–307 (2010). https://doi.org/10.1109/ROBOT.2010.5509725

  22. Mead, R., Mataric, M.J.: Robots have needs too: people adapt their proxemic preferences to improve autonomous robot recognition of human social signals. In: AISB Convention 2015 (2015)

    Google Scholar 

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

    Google Scholar 

  24. Pacchierotti, E., Christensen, H.I., Jensfelt, P.: Evaluation of passing distance for social robots. In: ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 315–320 (2006). https://doi.org/10.1109/ROMAN.2006.314436

  25. Repiso, E., Garrell, A., Sanfeliu, A.: Adaptive side-by-side social robot navigation to approach and interact with people. Int. J. Soc. Robot. 1–22 (2019). https://doi.org/10.1007/s12369-019-00559-2

  26. Rico, F.M.: ros2\(\_\)planning\(\_\)system (2019). https://github.com/IntelligentRoboticsLabs/ros2_planning_system

  27. Vega, A., Manso, L.J., Macharet, D.G., Bustos, P., Núñez, P.: Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances. Pattern Recogn. Lett. 118, 72–84 (2019). https://doi.org/10.1016/j.patrec.2018.07.015

    Article  Google Scholar 

  28. Vega-Magro, A., Manso, L.J., Bustos, P., Núñez, P.: A flexible and adaptive spatial density model for context-aware social mapping: towards a more realistic social navigation. In: Proceedings of 15th International Conference on Control, Automation, Robotics and Vision, pp. 1727–1732 (2018)

    Google Scholar 

  29. Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Syrdal, D.S., Nehaniv, C.L.: An empirical framework for Human-Robot proxemics. In: Adaptive and Emergent Behaviour and Complex Systems - Proceedings of the 23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009, pp. 144–149 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escuela Internacional de Doctorado, Rey Juan Carlos University, Móstoles, Spain

    Jonatan Ginés Clavero

  2. Intelligent Robotics Lab, Rey Juan Carlos University, Fuenlabrada, Spain

    Francisco Martín Rico & José Miguel Guerrero Hernández

  3. Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain

    Francisco J. Rodríguez-Lera

  4. Supercomputación Castilla y León, SCAYLE, León, Spain

    Vicente Matellán Olivera

Authors
  1. Jonatan Ginés Clavero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Martín Rico
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco J. Rodríguez-Lera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Miguel Guerrero Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vicente Matellán Olivera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonatan Ginés Clavero .

Editor information

Editors and Affiliations

  1. Electronics Department, University of Alcalá, Madrid, Spain

    Luis M. Bergasa

  2. Electronics Department, University of Alcalá, Madrid, Spain

    Manuel Ocaña

  3. Electronics Department, University of Alcalá, Madrid, Spain

    Rafael Barea

  4. Electronics Department, University of Alcalá, Madrid, Spain

    Elena López-Guillén

  5. Electronics Department, University of Alcalá, Madrid, Spain

    Pedro Revenga

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ginés Clavero, J., Martín Rico, F., Rodríguez-Lera, F.J., Guerrero Hernández, J.M., Matellán Olivera, V. (2021). Defining Adaptive Proxemic Zones for Activity-Aware Navigation. In: Bergasa, L.M., Ocaña, M., Barea, R., López-Guillén, E., Revenga, P. (eds) Advances in Physical Agents II. WAF 2020. Advances in Intelligent Systems and Computing, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-62579-5_1

Download citation

Publish with us

Policies and ethics

Search

Navigation