Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human-robot interaction (HRI). This paper formulates a robot motion planning problem for the optimization of two merging pedestrian flows moving through a bottleneck exit. To address the challenge of feature representation of complex human motion dynamics under the effect of HRI, we propose using a deep neural network to model the mapping from the image input of pedestrian environments to the output of robot motion decisions. The robot motion planner is trained end-to-end using a deep reinforcement learning algorithm, which avoids hand-crafted feature detection and extraction, thus improving the learning capability for complex dynamic problems. Our proposed approach is validated in simulated experiments, and its performance is evaluated. The results demonstrate that the robot is able to find optimal motion decisions that maximize the pedestrian outflow in different flow conditions, and the pedestrian-accumulated outflow increases significantly compared to cases without robot regulation and with random robot motion.

中文翻译：

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基于深度强化学习的机器人辅助行人调节


行人监管可以预防人群事故，提高人口密集地区的人群安全。最近的研究使用移动机器人通过被动人机交互（HRI）的效果来调节行人流量，以实现所需的集体运动。本文提出了一个机器人运动规划问题，用于优化穿过瓶颈出口的两个合并行人流。为了解决 HRI 影响下复杂人体运动动力学特征表示的挑战，我们建议使用深度神经网络来建模从行人环境的图像输入到机器人运动决策的输出的映射。机器人运动规划器使用深度强化学习算法进行端到端训练，避免了手工特征检测和提取，从而提高了复杂动态问题的学习能力。我们提出的方法在模拟实验中得到验证，并对其性能进行了评估。结果表明，机器人能够找到在不同流动条件下最大化行人流出的最佳运动决策，并且与没有机器人调节和随机机器人运动的情况相比，行人累积流出显着增加。