Ægidius, Sebastian;
Chacón-Quesada, Rodrigo;
Delfaki, Andromachi Maria;
Kanoulas, Dimitrios;
Demiris, Yiannis;
(2024)
ASFM: Augmented Social Force Model for Legged Robot Social Navigation.
In:
2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids).
(pp. pp. 37-44).
IEEE: Nancy, France.
(In press).
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Abstract
Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive. In this paper, a social force model for legged robots is developed, utilizing visual perception for human localization. In particular, an augmented social force model is introduced, incorporating refined interpretations of repulsive forces and avoidance behaviors based on pedestrian actions, alongside a target following mechanism. Experimental evaluation on a quadruped robot, through various scenarios, including interactions with oncoming pedestrians, crowds, and obstructed paths, demonstrates that the proposed augmented model significantly improves upon previous baseline methods in terms of chosen path length, average velocity, and time-to-goal for effective and efficient social navigation. The code is open-source, while video demonstrations can be found on the project’s webpage: https://rpl-cs-ucl.github.io/ASFM/
| Type: | Proceedings paper |
|---|---|
| Title: | ASFM: Augmented Social Force Model for Legged Robot Social Navigation |
| Event: | 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) |
| ISBN-13: | 979-8-3503-7357-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/Humanoids58906.2024.10769845 |
| Publisher version: | https://doi.org/10.1109/Humanoids58906.2024.107698... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10202839 |
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