Enhancing smart city crowd management through behavioral data-driven human-robot collaborative navigation

Smart cities face increasing challenges in managing crowd dynamics while ensuring public safety and maintaining quality of life. The development of artificial intelligence and robotics technology has opened new avenues for addressing these challenges. This paper presents a behavioral data-driven approach to human-robot collaborative navigation that enhances crowd management in smart urban environments. The proposed approach leverages real-time behavioral data from pedestrian movements and crowd patterns to enable intelligent human-robot collaboration. The system dynamically adjusts robot guidance through a rule-based state transition mechanism that ensures decision transparency and predictability, and employs a multi-robot collaborative scheduling algorithm to control crowd density in key areas. Additionally, it utilizes a dynamic instruction system to provide pedestrians with directional guidance and behavioral recommendations, thereby enhancing the effectiveness of human-robot collaborative navigation. Through empirical validation in a virtual reality environment, we demonstrate significant improvements in navigation efficiency, crowd flow control, pedestrian safety, and user satisfaction. The results indicate that integrating real-time behavioral data with human-robot collaboration offers a promising pathway for addressing complex crowd management challenges in smart cities, demonstrating how rule-based systems can balance automated efficiency with human-centered design principles.