Authors:
Marco Rosano
1
;
Danilo Leocata
1
;
Antonino Furnari
1
;
2
and
Giovanni Farinella
1
;
2
Affiliations:
1
FPV@IPLAB, Department of Mathematics and Computer Science, University of Catania, Catania, Italy
;
2
Next Vision s.r.l., Catania, Italy
Keyword(s):
Crowd Navigation, Reinforcement Learning, Obstacle Avoidance, Crowd Simulator.
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 wi
th 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.
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