Mixed Crowd Navigation: Perception, Interaction, Planning, and Control

We comprehensively survey mixed crowd navigation, focusing on the integration of robotic agents with volitional crowds (humans or human-driven vehicles) to achieve system-wide benefits. The survey is organized following the perception–interaction–planning–control pipeline, examining four core components: (a) perceiving global crowd behavior from local robot observations through nonparticipant and participant observation methods; (b) modeling volitional agent responses via rule-based and data-driven interaction frameworks; (c) predicting crowd dynamics across microscopic, mesoscopic, and macroscopic scales using both traditional and machine learning approaches; and (d) synthesizing control policies that guide crowds toward desired states. Wed address critical challenges such as complex interaction modeling under partial observability, constrained robotic influence, and the need for multiscale behavioral consistency. Key applications span pedestrian crowd management and mixed traffic control. We also highlight emerging trends in mixed crowd navigation, including the use of deep reinforcement learning and foundation models, while identifying persistent challenges in human irrationality modeling, compliance prediction, and privacy-preserving algorithms for real-world deployment.