Zhiqiang Wan
Papers
1
Total Citations
80
H-Index
1
About
Zhiqiang Wan is a leading researcher at the intersection of robotics, artificial intelligence, and crowd dynamics, with a primary focus on enhancing public safety through intelligent human-robot interaction. His most influential work, "Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning" (2018, 80 citations), pioneers a novel approach to managing pedestrian flows in densely populated areas. By formulating robot motion planning as a deep reinforcement learning problem, Wan demonstrates how mobile robots can passively influence crowd behavior to prevent accidents and improve collective safety. This foundational contribution bridges the gap between autonomous navigation and crowd psychology, offering a scalable, real-time solution for urban environments. Beyond this landmark paper, Wan’s research continues to advance the fields of multi-agent systems and socially-aware robotics, where his work on deep reinforcement learning for pedestrian regulation has become a key reference for researchers developing safer, more responsive autonomous systems. His achievements highlight a commitment to translating cutting-edge AI into practical tools for public safety, making him a notable figure in modern robotics and human-robot interaction research.
Research Focus
Key Achievements
Top Papers
- 1Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning80 citations · 2018