Robot Navigation Based on an Artificial Somatosensorial System

  • Ignazio InfantinoEmail author
  • Adriano Manfré
  • Umberto Maniscalco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)


An artificial somatosensory system processes robot’s perceptions by mean of suitable soft sensors. The robot moves in a real and complex environment, and the physical sensing of it causes a positive or negative reaction. A global wellness function drives the robot’s movements and constitutes a basis to compute the motivation of a cognitive architecture. The paper presents preliminary experimentations and explains the influence of the parameters on the robot behavior and personality. Pepper freely moves in an office environment searching for people to engage. The robot searches for a safe path, avoiding obstacles and aiming to explore a significant part of a known space by an approximative map stored in its long term memory (LTM). The short-term memory (STM) stores somatosensory values related to perceptions considered relevant for the navigation task. The collection of previous navigation experiences allows the robot to memorize on the map places that have positive (or negative) effects on robot’s wellness state. The robot could reach the places labeled as negative, but it needs some positive counter effects to contrast its reluctance.


  1. 1.
    Broadbent, E.: Interactions with robots: The truths we reveal about ourselves. Ann. Rev. Psychol. 68, 627652 (2017)CrossRefGoogle Scholar
  2. 2.
    Cuperlier, N., Quoy, M., Gaussier, P.: Neurobiologically inspired mobile robot navigation and planning. Front. Neurorobotics 1, 3 (2007)CrossRefGoogle Scholar
  3. 3.
    Arkin, R.C.: Dynamic replanning for a mobile robot based on internal sensing. In: 1989 Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1416–1421. IEEE (1989)Google Scholar
  4. 4.
    Hasson, C., Boucenna, S., Gaussier, P., Hafemeister, L.: Using emotional interactions for visual navigation task learning. In: International Conference on Kansei Engineering and Emotion Research KEER2010, pp. 1578–1587 (2010)Google Scholar
  5. 5.
    Mead, R., Mataric, M.J.: Autonomous humanrobot proxemics: socially aware navigation based on interaction potential. Auton. Robots 113 (2016)Google Scholar
  6. 6.
    Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: A survey. Robot. Auton. Syst. 61(12), 1726–1743 (2013)CrossRefGoogle Scholar
  7. 7.
    Augello, A., Infantino, I., Maniscalco, U., Pilato, G., Vella, F.: The effects of soft somatosensory system on the execution of robotic tasks. In: IEEE International Conference on Robotic Computing 2017, Taiwan, p. 17. IEEE (2017)Google Scholar
  8. 8.
    Maniscalco, U., Pilato, G., Vella, F.: Soft sensor network for environmental monitoring. In: Intelligent Interactive Multimedia Systems and Services 2016, pp. 705–714 (2016)Google Scholar
  9. 9.
    Augello, A., Maniscalco, U., Pilato, G., Vella, F.: Disaster prevention virtual advisors through soft sensor paradigm. In: Intelligent Interactive Multimedia Systems and Services 2016, pp. 619–627 (2016)Google Scholar
  10. 10.
    Koditschek, D.: Exact robot navigation by means of potential functions: Some topological considerations. In: 1987 Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, p. 16 (1987)Google Scholar
  11. 11.
    Masutani, Y., Miyazaki, F., Arimoto, S.: Sensory feedback control for space manipulators. In: 1989 Proceedings of the International Conference on Robotics and Automation, vol. 3, pp. 1346–1351 (1989)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ignazio Infantino
    • 1
    Email author
  • Adriano Manfré
    • 1
  • Umberto Maniscalco
    • 1
  1. 1.Institute of High Performance and Networking (ICAR), National Research Council (CNR)PalermoItaly

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