Venue: 2024 IEEE-RAS International Conference on Humanoid Robots, Nancy, France
Authors: Sebastian Ægidius, Rodrigo Chacón-Quesada, Andromachi Maria Delfaki, Dimitrios Kanoulas, and Yiannis Demiris
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: rpl-cs-ucl.git...
Негізгі бет ASFM: Augmented Social Force Model for Legged Robot Social Navigation
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