用深度学习动态调整人机协作速度，提升效率同时保障安全
Analysis of Deep-Learning Methods in an ISO/TS 15066-Compliant Human-Robot Safety Framework
- 基于四种深度学习方法识别人体部位，区分于普通物体
- 实测循环时间缩短最高达15%，优于传统安全系统
- 适合智能制造中需高效人机协同的场景
近年来，协作机器人在制造领域广泛应用，人机在近距离协同作业。然而，当前符合ISO/TS 15066标准的安全实现常因保守的速度限制而降低协作效率。为此，本文提出一种基于深度学习的人机安全框架（HRSF），可根据人机间距动态调整机器人速度，同时满足最大生物力学力和压强限制。研究评估了四种可用于人体提取的深度学习方法：人体识别、人体分割、姿态估计和人体部位分割。与传统工业安全系统不同，该框架能区分个体人体部位与其他物体，从而优化机器人作业流程。实验表明，相比传统安全技术，循环时间最多可减少15%。
原文摘要 · Abstract (English)
Over the last years collaborative robots have gained great success in manufacturing applications where human and robot work together in close proximity. However, current ISO/TS-15066-compliant implementations often limit the efficiency of collaborative tasks due to conservative speed restrictions. For this reason, this paper introduces a deep-learning-based human-robot-safety framework (HRSF) that aims at a dynamical adaptation of robot velocities depending on the separation distance between human and robot while respecting maximum biomechanical force and pressure limits. The applicability of the framework was investigated for four different deep learning approaches that can be used for human body extraction: human body recognition, human body segmentation, human pose estimation, and human body part segmentation. Unlike conventional industrial safety systems, the proposed HRSF differentiates individual human body parts from other objects, enabling optimized robot process execution. Experiments demonstrated a quantitative reduction in cycle time of up to 15% compared to conventional safety technology.
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