CrowdSim++: Unifying Crowd Navigation and Obstacle Avoidance

Marco Rosano, Danilo Leocata, Antonino Furnari, Antonino Furnari, Giovanni Farinella, Giovanni Farinella

2025

Abstract

In recent years, significant advancements in learning-based technologies have propelled the development of autonomous robotic systems designed to assist humans in challenging scenarios during their daily activities. This research focuses on enhancing robotic perception and control, particularly in navigating complex, crowded environments. Traditional approaches often treat static and dynamic components separately, limiting the robots’ real-world performance. We propose CrowdSim++, an extension of the open-source CrowdSim simulator (Chen et al., 2019), to unify crowd navigation and obstacle avoidance. CrowdSim++ enables training navigation policies in dynamically generated environments or real-world floor plans, using a 2D lidar sensor and a “person sensor” for enhanced perception. Our experiments demonstrate that Reinforcement Learning-based navigation policies trained in complex environments with humans outperform those trained in simpler scenarios. Additionally, providing robots with specialized sensors to accurately distinguish between static and dynamic obstacles is essential for achieving superior performance. To advance research in autonomous navigation, the source code and dataset of realistic floor plans are available at the following link.

Paper Citation

in Harvard Style

Rosano M., Leocata D., Furnari A. and Farinella G. (2025). CrowdSim++: Unifying Crowd Navigation and Obstacle Avoidance. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 533-542. DOI: 10.5220/0013192300003890

in Bibtex Style

@conference{icaart25,
author={Marco Rosano and Danilo Leocata and Antonino Furnari and Giovanni Farinella},
title={CrowdSim++: Unifying Crowd Navigation and Obstacle Avoidance},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={533-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013192300003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - CrowdSim++: Unifying Crowd Navigation and Obstacle Avoidance
SN - 978-989-758-737-5
AU - Rosano M.
AU - Leocata D.
AU - Furnari A.
AU - Farinella G.
PY - 2025
SP - 533
EP - 542
DO - 10.5220/0013192300003890
PB - SciTePress