Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction

2025-08-09

AI Summary

To address the challenge of safe navigation for autonomous robots in dense pedestrian environments, this paper proposes a closed-loop navigation framework integrating Social-Implicit trajectory prediction with Model Predictive Control (MPC). The method introduces the Social-Implicit model to enhance the foresight and robustness of pedestrian motion prediction. Crucially, it systematically analyzes the discrepancy between open-loop prediction metrics and actual closed-loop navigation performance, underscoring the necessity of end-to-end system evaluation. Experimental validation in real-world scenarios demonstrates that the proposed approach reduces trajectory prediction error by 76% in low-density settings, while significantly improving obstacle avoidance safety, motion smoothness, and overall navigation reliability. This work advances the integration of socially aware prediction models into real-time control pipelines and highlights the importance of holistic performance assessment beyond isolated prediction accuracy.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Security and Privacy: Large-scale security measurements

Abstract

Safe navigation in pedestrian-rich environments remains a key challenge for autonomous robots. This work evaluates the integration of a deep learning-based Social-Implicit (SI) pedestrian trajectory predictor within a Model Predictive Control (MPC) framework on the physical Continental Corriere robot. Tested across varied pedestrian densities, the SI-MPC system is compared to a traditional Constant Velocity (CV) model in both open-loop prediction and closed-loop navigation. Results show that SI improves trajectory prediction - reducing errors by up to 76% in low-density settings - and enhances safety and motion smoothness in crowded scenes. Moreover, real-world deployment reveals discrepancies between open-loop metrics and closed-loop performance, as the SI model yields broader, more cautious predictions. These findings emphasize the importance of system-level evaluation and highlight the SI-MPC framework's promise for safer, more adaptive navigation in dynamic, human-populated environments.

Problem

• Improving autonomous robot navigation in crowded pedestrian environments
• Integrating learning-based trajectory prediction with Model Predictive Control
• Evaluating safety and adaptability in dynamic human-populated settings

Innovation

• Deep learning-based Social-Implicit trajectory predictor
• Integration with Model Predictive Control framework
• Real-world testing on Continental Corriere robot