用视觉判断工人是否注意到机器人，让仓库里的机器人更安全高效
Vision-Based Human Awareness Estimation for Enhanced Safety and Efficiency of AMRs in Industrial Warehouses
- 通过单目相机结合人体姿态与头部朝向，实时判断人是否注意到机器人
- 在模拟环境中验证，系统可准确识别人员位置与注意力方向
- 适合工业自动化中需提升人机协作效率的场景
在人机混行的工业仓库中，保障人员安全至关重要。现有方法常将人视为通用动态障碍物，导致机器人即使面对已察觉的工人也采取保守行为（如减速或绕行）。本文提出一种基于单目RGB相机的实时视觉方法，通过融合先进的3D人体姿态重建与头部朝向估计，判断人员相对于机器人的位置及其视线范围，从而确定其是否意识到机器人存在。整个流程在NVIDIA Isaac Sim这一高保真物理仿真环境中进行验证。实验结果表明，该系统能可靠地实现实时人员定位与注意力检测，使机器人可根据人类意识状态动态调整运动策略，显著提升工业与工厂自动化场景中的安全性和运行效率。
原文摘要 · Abstract (English)
Ensuring human safety is of paramount importance in warehouse environments that feature mixed traffic of human workers and autonomous mobile robots (AMRs). Current approaches often treat humans as generic dynamic obstacles, leading to conservative AMR behaviors like slowing down or detouring, even when workers are fully aware and capable of safely sharing space. This paper presents a real-time vision-based method to estimate human awareness of an AMR using a single RGB camera. We integrate state-of-the-art 3D human pose lifting with head orientation estimation to ascertain a human's position relative to the AMR and their viewing cone, thereby determining if the human is aware of the AMR. The entire pipeline is validated using synthetically generated data within NVIDIA Isaac Sim, a robust physics-accurate robotics simulation environment. Experimental results confirm that our system reliably detects human positions and their attention in real time, enabling AMRs to safely adapt their motion based on human awareness. This enhancement is crucial for improving both safety and operational efficiency in industrial and factory automation settings.
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