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International Conference on Social Robotics

ICSR 2017: Social Robotics pp 474–484Cite as

Social Group Motion in Robots

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10652))

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.

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Notes

  1. 1.

    By distance between pedestrians, or more in general between moving agents, we usually mean the distance between their body centres.

  2. 2.

    Namely, we change the current distance with the future one “at the time of collision”, computed using relative velocity information.

  3. 3.

    Such social norms were not implemented in the simulator developed for this work.

  4. 4.

    In our implementation, \(R_{max}\) was fixed by an optimisation algorithm as \({\approx } 0.4\) m. Since the term \(\mathbf {f}_{obs}\) is not intended to provide the robot navigation around obstacles, which should be inferred by a proper navigation algorithm, but only “emergency” collision avoidance, a relatively small value of \(R_{max}\) is completely acceptable. Furthermore, since in the example we are using Eq. 6, such \(R_{max}\) corresponds to a threshold for the distance at the time of maximum approach, and not for the current distance, which may be considerably larger.

  5. 5.

    When dealing with the collision avoidance force between two pedestrians, robots or other moving agents \(\mathbf {f}_{ped}\), \(\overline{r}\) should be replaced with the body radius, i.e. assuming the size of the robot is similar to the human one, \({\approx } 0.5\) m.

  6. 6.

    The difference between the pedestrians and the robots is clear when they change direction, since the former do it instantaneously, while the latter need to rotate.

  7. 7.

    The used model introduces some minor changes in the equations of [23, 24], which were introduced to maximise stability, in particular with respect to actual robot implementation. Furthermore, some parameters have been changed with respect to the original work by using a GA algorithm with the intent, again, of minimising robot collisions. Since the purpose of this work is not to present a maximally efficient robot system, but to provide a general recipe to convert a pedestrian model for robot use, we leave these details for an incoming extended technical paper.

  8. 8.

    In this preliminary implementation, robots just navigated to the end of the corridor, and the goal was decided in advance.

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Acknowledgements

This research is partially based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

Author information

Authors and Affiliations

  1. ATR Labs, Kyoto, Japan

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

  2. Okayama University, Okayama, Japan

    Zeynep Yücel

  3. Nagoya University, Nagoya, Japan

    Luis Yoichi Morales Saiki

Authors
  1. Francesco Zanlungo
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  2. Zeynep Yücel
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  3. Florent Ferreri
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  4. Jani Even
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  5. Luis Yoichi Morales Saiki
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  6. Takayuki Kanda
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Corresponding author

Correspondence to Francesco Zanlungo .

Editor information

Editors and Affiliations

  1. CNRS-AIST JRL, Tsukuba, Japan

    Abderrahmane Kheddar

  2. CNRS-AIST JRL, Tsukuba, Japan

    Eiichi Yoshida

  3. The National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  4. University of Tsukuba, Tsukuba, Japan

    Kenji Suzuki

  5. Qatar University, Doha, Qatar

    John-John Cabibihan

  6. CITEC, Bielefeld, Germany

    Friederike Eyssel

  7. Wichita State University, Wichita, Kansas, USA

    Hongsheng He

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Zanlungo, F., Yücel, Z., Ferreri, F., Even, J., Morales Saiki, L.Y., Kanda, T. (2017). Social Group Motion in Robots. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_47

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  • DOI: https://doi.org/10.1007/978-3-319-70022-9_47

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70022-9

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