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<p>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. Temporal sequences of LiDAR data and sub-goal were used as input, and action output is generated via an end-to-end network. We designed an adaptive heading reward that guides the robot to proactively avoid pedestrians while efficiently moving toward its target. Through continuous interaction with a dynamic environment, the robot learns an optimal decision-making strategy by maximizing cumulative rewards. A series of simulation experiments and real-world validations demonstrate that the proposed strategy achieves an effective balance between collision avoidance and real-time performance in robotic navigation. Furthermore, extensive results confirm that the method remains robust in unfamiliar environments and in varying crowd densities. Finally, tests on a hardware platform indicate that the strategy offers strong stability and adaptability in practical applications, effectively meeting obstacle avoidance requirements and validating its reliability in complex dynamic settings.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

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  • Jin Meng,
  • Jiahui Zou,
  • Shifeng Wang,
  • Ruibing Yang,
  • Aakash Kumar,
  • Jonghyuk Kim

摘要

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. Temporal sequences of LiDAR data and sub-goal were used as input, and action output is generated via an end-to-end network. We designed an adaptive heading reward that guides the robot to proactively avoid pedestrians while efficiently moving toward its target. Through continuous interaction with a dynamic environment, the robot learns an optimal decision-making strategy by maximizing cumulative rewards. A series of simulation experiments and real-world validations demonstrate that the proposed strategy achieves an effective balance between collision avoidance and real-time performance in robotic navigation. Furthermore, extensive results confirm that the method remains robust in unfamiliar environments and in varying crowd densities. Finally, tests on a hardware platform indicate that the strategy offers strong stability and adaptability in practical applications, effectively meeting obstacle avoidance requirements and validating its reliability in complex dynamic settings.