Pedestrian Flow Optimization to Reduce the Risk of Crowd Disasters Through Human-Robot Interaction

Chao Jiang, Zhen Ni, Yi Guo, Haibo He

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Article number8805280
Pages (from-to)298-311
Number of pages14
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Volume4
Issue number3
DOIs
StatePublished - Jun 2020

Keywords

  • Pedestrian flow regulation
  • and pedestrian crowd pressure
  • human-robot interaction
  • learning-based optimal control

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