Pedestrian flow in densely-populated or congested
areas usually presents irregular or turbulent motion state due to
competitive behaviors of individual pedestrians, which reduces
flow efficiency and raises the risk of crowd accidents. Effective
pedestrian flow regulation strategies are highly valuable for
flow optimization. Existing studies seek for optimal design of
indoor architectural features and spatial placement of pedestrian
facilities for the purpose of flow optimization. However, once
placed, the stationary facilities are not adaptive to real-time
flow changes. In this paper, we investigate the problem of
regulating two merging pedestrian flows in a bottleneck area
using a mobile robot moving among the pedestrian flows. The
pedestrian flows are regulated through dynamic human-robot
interaction (HRI) during their collective motion. We adopt an
adaptive dynamic programming (ADP) method to learn the
optimal motion parameters of the robot in real time, and the
resulting outflow through the bottleneck is maximized with the
crowd pressure reduced to avoid potential crowd disasters. The
proposed algorithm is a data-driven approach that only uses
camera observation of pedestrian flows without explicit models
of pedestrian dynamics and HRI. Extensive simulation studies
are performed in both Matlab and a robotic simulator to verify
the proposed approach and evaluate the performances
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Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning
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.
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- NSF-PAR ID:
- 10109298
- Date Published:
- Journal Name:
- IEEE Transactions on Cybernetics
- ISSN:
- 2168-2267
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCYB.2018.2878977
