Deep reinforcement learning for robust robot navigation in complex and crowded environments

Abstract In complex environments with dense pedestrian traffic, mobile robots often experience errors and instability during trajectory tracking and dynamic obstacle avoidance tasks. This paper presents a scene perception and decision-making strategy combined with deep reinforcement learning. Tempor...

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Autors principals:	Jin Meng, Jiahui Zou, Shifeng Wang, Ruibing Yang, Aakash Kumar, Jonghyuk Kim
Format:	Artigo
Idioma:	Inglês
Publicat:	Springer 2025-11-01
Col·lecció:	Journal of King Saud University: Computer and Information Sciences
Matèries:	Deep reinforcement learning Adaptive collision avoidance Sim-to-real transfer Dense pedestrian environments
Accés en línia:	https://doi.org/10.1007/s44443-025-00357-z
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