Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning
Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning
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DOI：
10.1109/tcyb.2018.2878977
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发表时间：
2020-04
影响因子：
11.8
通讯作者：
Zhiqiang Wan;Chao Jiang;M. Fahad;Z. Ni;Yi Guo;Haibo He
中科院分区：
文献类型：
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作者：
Zhiqiang Wan;Chao Jiang;M. Fahad;Z. Ni;Yi Guo;Haibo He
在人口稠密地区，行人管制可以防止人群事故，提高人群安全。最近的研究使用移动机器人通过被动的人-机器人交互(HRI)效应来调节行人流量以实现期望的集体运动。针对两个汇合的人流通过瓶颈出口的优化问题，提出了一个机器人运动规划问题。为了解决HRI影响下复杂人体运动动力学特征表示的挑战，我们提出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输出的映射。机器人运动规划器采用深度强化学习算法进行端到端的训练，避免了人工特征检测和提取，提高了对复杂动态问题的学习能力。通过仿真实验验证了该方法的有效性，并对其性能进行了评估。结果表明，在不同的流量条件下，机器人能够找到最大化行人流量的最优运动决策，与无机器人规则和随机运动的情况相比，行人累积流量显著增加。
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.