The seamless integration of mobile robots into daily life hinges on addressing the challenge of human–robot interaction within dynamic environments. Existing research has extensively explored the harmonious path planning of robots with individual pedestrians. However, the practical scenario often involves robots navigating among pedestrian groups. In such contexts, due to inadequate consideration of collective pedestrian dynamics, conventional approaches frequently lead to group disruptions, structural instability, and rigid interactions. This study introduces a novel human–robot collaborative navigation framework on the basis of the interactive information entropy, which is proposed to quantify information about pedestrian groups, assesses the capacity of robots to acquire group-related information from different positions, and offers a robust decision-making basis for identifying optimal interaction points. The methodology comprises three key phases: first, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is employed to identify pedestrian groups by analyzing real-time positional coordinates and kinematic parameters; second, scenarios are categorized into dynamic and static types based on pedestrian states, with candidate interaction points generated according to the distribution and orientation of pedestrians; finally, an interactive information entropy model is designed to evaluate and select the optimal interaction point, guiding trajectory planning of the robot while respecting group coherence and social navigation conventions. Extensive evaluations in both simulated and real-world environments verify the feasibility and effectiveness of this approach.
Liu et al. (Sun,) studied this question.