In congested environments, achieving safe and efficient robot navigation without disturbing crowd flow is a significant challenge. Previous studies have not sufficiently validated motion control algorithms that account for the dynamic characteristics of pedestrian flows, leading to degraded navigation performance. In this paper, a merging and following control algorithm based on the real-time understanding of pedestrian flow dynamics is proposed to address this issue. The advanced recognition of pedestrian flows is enabled by the use of an overhead-view RGB camera and 2D-LiDAR, through which crowd dynamics are accurately captured. Based on the acquired dynamic flow information, merging motions that preserve crowd movement and following motions adapted to nonuniform flows are generated. The proposed system is evaluated in both nonuniform flow and separate flow scenes, which represent general crowd behavior. Multiple simulations and real-world experiments have demonstrated that the proposed system enhances navigation performance compared with conventional systems while ensuring human safety and comfort. In particular, reductions in travel time and path length, decreases in average acceleration and collision count, and the minimization of social force on pedestrians are achieved. These validation results indicate that the system enables robots to navigate crowds in a human-like manner, thereby supporting its potential deployment in practical environments.

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