Navigating the Crowd: Non-linear MPC with Social Forces Dynamics for Human-Aware Robot Navigation

2026-07-11

AI Summary

This work addresses the challenge of enabling autonomous robots to navigate crowded environments while simultaneously avoiding collisions and adhering to human social norms. The authors propose the SFM-NMPC framework, which uniquely integrates the Social Force Model (SFM) directly into the optimization loop of Nonlinear Model Predictive Control (NMPC). This integration allows for joint prediction of human and robot trajectories and leverages a socially aware cost function to generate navigation policies consistent with human behavioral conventions. The resulting approach achieves end-to-end socially compliant trajectory planning, running in real time at 20 Hz in dense simulated environments. Experimental results demonstrate significant improvements over existing methods in terms of social compliance, trajectory smoothness, and navigation efficiency.

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.

Problem

• social navigation
• human-aware robotics
• collision avoidance
• personal space
• autonomous robots

Innovation

• Social Force Model
• Non-linear MPC
• Human-aware Navigation
• Social Compliance
• Real-time Optimization