Dynamic Pedestrian Flow Guidance through Multi-robot Interaction along Human Crowd Boundaries

This study proposes a boundary-centric multi-robot pedestrian flow guidance design that enables soft, human-centered flow alignment along a guidance line with only a few robots. Flow boundaries are estimated from the crowd’s outer contour via a convex hull, and boundary pedestrians are classified as head, side, and corner. Guidance priority integrates boundary-type importance with the predicted time to intersect the guidance line. A reachability-aware score enables optimal robot allocation via the Hungarian algorithm. Assigned robots employ a parallel-interaction strategy with a side-offset distance perpendicular to a target’s walking direction to elicit anticipatory avoidance without overtly forcing heading changes. A unidirectional guidance simulation with 15 pedestrians and three robots demonstrates feasibility, showing reachability-driven role sharing, improved alignment, and guidance without pedestrians crossing the guidance line under the tested condition. Future work will evaluate safety, trust, and acceptability in opposing and crossing flows with real robots.