Social Navigation Restrictions for Interactive Robots Using Augmented Reality

  • Francisco J. Rodríguez LeraEmail author
  • Fernando Casado
  • Camino Fernández
  • Vicente Matellán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9422)


This paper describes the navigation mechanisms proposed for a mobile robot that uses augmented reality as interaction mechanism and laser scanners as main sensors. The peculiarities imposed by this interaction mechanism require continuous tracking of the person being escorted. The mechanism proposed for detecting and tracking people is based on a population of Kalman Filters and a basic association algorithm that matches past and new observations. The navigation system has been designed taking into account the special needs that an augmented reality interaction system imposes to a social navigation algorithm. This navigation system is integrated into a motivational control architecture that is also briefly described, as well as some preliminary experiments.


Social navigation Path planning Augmented reality Human-robot interaction 



This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under grant DPI2013-40534-R.


  1. 1.
    Aguirre, E., Garcia-Silvente, M., Plata, J.: Leg detection and tracking for a mobile robot and based on a laser device, supervised learning and particle filtering. In: Armada, M.A., Sanfeliu, A., Ferre, M. (eds.) ROBOT 2013: First Iberian Robotics Conference. AISC, vol. 252, pp. 433–440. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  2. 2.
    Arras, K.O., Lau, B., Grzonka, S., Luber, M., Mozos, O.M., Meyer-Delius, D., Burgard, W.: Range-based people detection and tracking for socially enabled service robots. In: Prassler, E., et al. (eds.) Towards Service Robots for Everyday Environ. STAR, vol. 76, pp. 235–280. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  3. 3.
    Bar-Shalom, Y., Li, X.-R.: Multitarget-Multisensor Tracking: Principles and Techniques, vol. 19. YBS Publishing, Storrs (1995)Google Scholar
  4. 4.
    Cosgun, A., Florencio, D., Christensen, H.I., et al.: Autonomous person following for telepresence robots. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 4335–4342. IEEE (2013)Google Scholar
  5. 5.
    Garrell, A., Sanfeliu, A.: Cooperative social robots to accompany groups of people. Int. J. Robot. Res. 31(13), 1675–1701 (2012)CrossRefGoogle Scholar
  6. 6.
    Gockley, R., Forlizzi, J., Simmons, R.: Natural person-following behavior for social robots. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 17–24. ACM (2007)Google Scholar
  7. 7.
    Kobayashi, Y., Suzuki, R., Sato, Y., Arai, Kuno, M.Y., Yamazaki, A., Yamazaki, K.: Robotic wheelchair easy to move and communicate with companions. In: 2013 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2013, Paris, France, 27 April–2 May, 2013, Extended Abstracts, pp. 3079–3082 (2013)Google Scholar
  8. 8.
    Rodríguez Lera, F.J., Matellán, V.: Hybrid architecture for human-robot interaction: Updating the classical three-layer solution. In: Actas del XV Workshop en Agentes Físicos (2013)Google Scholar
  9. 9.
    Rodríguez Lera, F.J., Rodríguez, V., Rodríguez, C., Matellán, V.: Augmented reality in robotic assistance for the elderly. In: Alonso, I.G. (ed.) International Technology Robotics Applications. Intelligent Systems, Control and Automation: Science and Engineering, vol. 70. Springer International Publishing, Switzerland (2014) Google Scholar
  10. 10.
    Matellán, V., Simmons, R.: Implementing human-acceptable navigational behavior and a fuzzy controller for an autonomous robot. In: Actas del Workshop en Agentes Físicos (2002)Google Scholar
  11. 11.
    Montemerlo, D., Thrun, S., Whittaker, W.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: International Conference on Robotics and Automation ICRA 2002 (2002)Google Scholar
  12. 12.
    Morales, Y., Kanda, T., Hagita, N.: Walking together: Side-by-side walking model for an interacting robot. J. Human-Robot Interact. 3, 50–73 (2014)Google Scholar
  13. 13.
    Prassler, E., Bank, D., Klunge, B.: Key technologies in robot assistants: Motion coordination between a human and a mobile robot. In: ICASE: Institute of Control, Automation adn Systems Engineering, KOREA (2002)Google Scholar
  14. 14.
    Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware navigation: A survey. Int. J. Soc. Robot. 7, 137–153 (2015)CrossRefGoogle Scholar
  15. 15.
    Simmons, R.: The curvature-velocity method for local obstacle avoidance. In: Proceedings of the 1996 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3375–3382. IEEE (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francisco J. Rodríguez Lera
    • 1
    Email author
  • Fernando Casado
    • 1
  • Camino Fernández
    • 1
  • Vicente Matellán
    • 1
  1. 1.School of Industrial Engineering and Information TechnologyUniversity of LeónLeónSpain

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