Dynamic Pedestrian Flow Control Using Flow-Robot Interaction Model of Mobile Robot and Human Crowd

This study addresses congestion caused by dense populations, promoting safe, efficient, and comfortable mobility in sustainable societies. It focuses on mobile robots for crowd management, proposing a novel method using Flow-Robot Interaction (FRI) and a heuristic behavior-based Social Force Model (SFM) to dynamically control pedestrian overtaking flows. Robots are strategically positioned at overtaking points to interact effectively with pedestrians, restricting paths and minimizing trajectory disruptions. Unlike fixed obstacles, the robots can flexibly reposition to obstruct pedestrians in their desired direction. Simulations show that placing robots at overtaking points stabilizes flows and reduces trajectory changes. Real-world experiments with 11 participants validate these findings, showing alignment of flow velocity with slower pedestrians and prevention of flow expansion. Results demonstrate the potential of human-robot interaction to mitigate congestion, stabilize flows, and enhance mobility. This scalable method offers an effective solution for managing crowds in sustainable urban environments.