Navigating the Crowd: Non-linear MPC with Social Forces Dynamics for Human-Aware Robot Navigation
2026-07-11 • Robotics
RoboticsHuman-Computer Interaction
AI summaryⓘ
The authors developed a method to help robots move safely and politely around people by predicting where people will go and planning accordingly. They combined a Social Force Model, which simulates human movement and personal space, with a type of control called Non-linear Model Predictive Control, allowing the robot to plan paths that respect human comfort. Their system runs quickly enough to work in real time and was tested in simulations with many people, showing better results than previous methods. The authors also analyzed which parts of their design were most important for success.
Social Force ModelNon-linear Model Predictive Controlhuman motion predictioncollision avoidancesocial navigationdynamic modelingoptimizationpersonal spacereal-time controltrajectory planning
Authors
Stefano Trepella, Andrea Ostuni, Mauro Martini, Pablo Pueyo, Noé Pérez-Higueras, Marcello Chiaberge, Fernando Caballero, Luis Merino
Abstract
Safe and socially compliant navigation remains a fundamental challenge for autonomous robots operating in human-populated environments. Beyond collision avoidance, robots must anticipate human motion and respect personal space to ensure human comfort. Model Predictive Control (MPC) offers a robust alternative to classical and data-driven methods, although its effectiveness strongly depends on accurate human motion prediction and efficient computation. This paper introduces SFM-NMPC, a Social Force Model-based Non-linear Model Predictive Control framework that embeds human motion prediction directly within the optimization loop. By incorporating the Social Force Model into the dynamic model of surrounding agents, the controller jointly predicts the trajectories of humans and robots over the prediction horizon, thereby enabling socially-aware planning. A tailored set of social cost functions guides the optimization toward human-compliant behaviors. Despite the increased model complexity, the proposed formulation runs in real time at 20 Hz. Extensive simulated testing in crowded environments demonstrates that SFM-NMPC outperforms state-of-the-art baselines in social compliance metrics while maintaining efficient and smooth navigation. Visual trajectory analysis and an ablation study further highlight the contribution of the embedded SFM dynamics and social cost terms, confirming the effectiveness of the proposed approach for real-world social navigation.