IEEE Trans Cybern. 2020 Apr;50(4):1669-1682. doi: 10.1109/TCYB.2018.2878977. Epub 2018 Nov 20.
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.
行人管理可以预防人群事故并提高人口密集地区的人群安全性。最近的研究使用移动机器人通过被动人机交互（HRI）的影响来调节行人流量以实现期望的集体运动。本文针对通过瓶颈出口的两个合并行人流的优化问题，提出了机器人运动规划问题。为了解决 HRI 作用下复杂行人运动动力学的特征表示挑战，我们提出使用深度神经网络来建立从行人环境的图像输入到机器人运动决策输出的映射。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，从而避免了手工制作的特征检测和提取，从而提高了对复杂动态问题的学习能力。我们的方法在模拟实验中进行了验证，并对其性能进行了评估。结果表明，机器人能够在不同的流量条件下找到最佳的运动决策，使行人的累积流量显著增加，与没有机器人调节和机器人随机运动的情况相比。
