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, the authors 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. The authors' 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.

• Record URL:
  https://doi.org/10.1109/TCYB.2018.2878977
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21682267
• Supplemental Notes:
  • Copyright © 2018, IEEE
• Authors:
  • Wan, Zhiqiang
  • Jiang, Chao
  • Fahad, Muhammad
  • Ni, Zhen
  • Guo, Yi
  • He, Haibo
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1-14
• Serial:
  • IEEE Transactions on Cybernetics
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2168-2267
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036

Subject/Index Terms

• TRT Terms: Crash avoidance systems; Machine learning; Pedestrian flow; Regulation; Robots; Trajectory control; User interfaces (Computer science)
• Uncontrolled Terms: Feature extraction
• Subject Areas: Data and Information Technology; Law; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01692180
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 4 2019 2:46PM