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CBF-Based STL Motion Planning for Social Navigation in Crowded Environment

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European Robotics Forum 2024 (ERF 2024)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 33))

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Abstract

A motion planning methodology based on the combination of Control Barrier Functions (CBF) and Signal Temporal Logic (STL) is employed in this paper. This methodology allows task completion at any point within a specified time interval, considering a dynamic system subject to velocity constraints. In this work, we apply this approach into the context of Socially Responsible Navigation (SRN), introducing a rotation constraint. This constraint is designed to maintain the user within the robot’s field of view (FOV), enhancing human-robot interaction with the concept of side-by-side human-robot companion. This angular constraint offers the possibility to customize social navigation to specific needs, thereby enabling safe SRN. Its validation is carried out through simulations demonstrating the system’s effectiveness in adhering to spatio-temporal constraints, including those related to robot velocity, rotation, and the presence of static and dynamic obstacles.

This work was supported by Horizon Europe program under the Grant Agreement 101070351 (SERMAS).

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Notes

  1. 1.

    DOI: https://doi.org/10.5281/zenodo.10255241.

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Authors and Affiliations

  1. University of Modena and Reggio Emilia, 42122, Reggio Emilia, RE, Italy

    Andrea Ruo, Lorenzo Sabattini & Valeria Villani

Authors
  1. Andrea Ruo
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  2. Lorenzo Sabattini
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  3. Valeria Villani
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Corresponding author

Correspondence to Andrea Ruo .

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Editors and Affiliations

  1. University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Cristian Secchi

  2. University of Bologna, Bologna, Italy

    Lorenzo Marconi

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Ruo, A., Sabattini, L., Villani, V. (2024). CBF-Based STL Motion Planning for Social Navigation in Crowded Environment. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-76428-8_23

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