用学习模型提升机器人在人群中的导航安全与平滑性。
Model Predictive Control for Crowd Navigation via Learning-Based Trajectory Prediction
- 将社交隐式预测模型融入模型预测控制框架
- 低密度下轨迹误差降低76%，高密度下更安全平滑
- 适合需要动态人群适应能力的机器人导航场景
自主机器人在行人密集环境中的安全导航仍是关键挑战。本文在物理机器人Continental Corriere上评估了基于深度学习的社交隐式（SI）行人轨迹预测模型与模型预测控制（MPC）框架的集成效果。在不同行人密度下，对比了SI-MPC与传统恒定速度（CV）模型在开环预测和闭环导航中的表现。结果表明，SI模型可将低密度场景下的轨迹预测误差降低76%，并在拥挤环境中提升安全性和运动平滑性。此外，真实部署显示开环指标与闭环性能存在差异，因SI模型生成更宽泛、更谨慎的预测。研究强调系统级评估的重要性，凸显SI-MPC框架在动态人机交互环境中的应用潜力。
原文摘要 · Abstract (English)
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.
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