在人群中导航：用于人类感知机器人导航的具有社会力动力学的非线性模型预测控制
Navigating the Crowd: Non-linear MPC with Social Forces Dynamics for Human-Aware Robot Navigation
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中文总结 AI 辅助
研究针对自主机器人在人群中安全合规导航的挑战，提出基于社会力模型的非线性模型预测控制框架SFM-NMPC，将人类运动预测嵌入优化循环，能联合预测人机轨迹，经测试在社会合规性上优于基线，有效用于现实社会导航。
中文摘要 AI 辅助
在人类居住环境中运行的自主机器人，安全且符合社会规范的导航仍是一项基本挑战。除避碰外，机器人还须预测人类运动并尊重个人空间。模型预测控制（MPC）虽有效，但依赖准确的人类运动预测和高效计算。本文介绍了SFM-NMPC，一种基于社会力模型的非线性模型预测控制框架，将人类运动预测直接嵌入优化循环。通过将社会力模型纳入周围智能体的动态模型，控制器在预测范围内联合预测人类和机器人轨迹，实现符合社会意识的规划。一组定制的社会成本函数引导优化向符合人类的行为。尽管模型复杂度增加，但该公式能以20Hz实时运行。在拥挤环境中的大量模拟测试表明，SFM-NMPC在社会合规指标上优于现有基线，同时保持高效平稳导航。视觉轨迹分析和消融研究进一步突出了嵌入式SFM动力学和社会成本项的贡献，证实了该方法在现实世界社会导航中的有效性。
英文摘要
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.
发表机构
- Department of Electronics and Telecommunications, Politecnico di Torino（电子与电信系，都灵理工大学）
- School of Engineering, Pablo de Olavide University（工程学院，巴勃罗·德奥拉维德大学）
机构由 AI 辅助整理，请以论文原文为准。
