Social Group Motion in Robots

Francesco Zanlungo, Zeynep Yucel, Florent Ferreri, Jani Even, Luis Yoichi Morales Saiki, Takayuki Kanda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Mobile social robots and (semi-)autonomous small size vehicles such as robotic wheelchairs need to understand and replicate pedestrian behaviour, in order to move safely in the crowd and to interact with, move along with and transport humans. A large amount of research about pedestrian behaviour has been undertaken by the crowd simulation community, but such results cannot be trivially adapted to robot applications. We discuss a simple but general recipe to apply an acceleration based pedestrian model (“Social Force Model”) to mobile robots, and, as a specific example, we show how to replicate in a group of robots the behaviour of social pedestrian groups.

Original languageEnglish
Title of host publicationSocial Robotics - 9th International Conference, ICSR 2017, Proceedings
PublisherSpringer Verlag
Pages474-484
Number of pages11
Volume10652 LNAI
ISBN (Print)9783319700212
DOIs
Publication statusPublished - Jan 1 2017
Event9th International Conference on Social Robotics, ICSR 2017 - Tsukuba, Japan
Duration: Nov 22 2017Nov 24 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10652 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Social Robotics, ICSR 2017
CountryJapan
CityTsukuba
Period11/22/1711/24/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zanlungo, F., Yucel, Z., Ferreri, F., Even, J., Morales Saiki, L. Y., & Kanda, T. (2017). Social Group Motion in Robots. In Social Robotics - 9th International Conference, ICSR 2017, Proceedings (Vol. 10652 LNAI, pp. 474-484). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10652 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-70022-9_47