用神经网络直接预测人机协作中的安全降速因子，提升调度效率。
On Using Neural Networks to Learn Safety Speed Reduction in Human-Robot Collaboration: A Comparative Analysis
- 基于执行数据训练神经网络，直接预测机器人安全降速比例。
- 简单前馈网络即可准确估计实际运行中的降速行为。
- 适合需要精准周期时间预测的工业自动化调度场景。
在人机协作中，速度与间距监控、功率与力限制等安全机制会根据人员接近程度动态调整机器人速度。尽管这些机制对降低风险至关重要，但带来的减速使周期时间估算变得困难，影响作业调度效率。现有周期时间估算或调度设计方法多依赖预设安全模型，而这些模型可能无法准确反映实际安全实施情况，因后者依赖具体场景的风险评估。本文提出一种深度学习方法，直接从过程执行数据中预测机器人的安全缩放因子。我们对比多种神经网络架构，发现简单的前馈网络能有效估计机器人减速行为。该能力对于改进协作机器人环境中的周期时间预测和设计更高效的调度算法具有重要意义。
原文摘要 · Abstract (English)
In Human-Robot Collaboration, safety mechanisms such as Speed and Separation Monitoring and Power and Force Limitation dynamically adjust the robot's speed based on human proximity. While essential for risk reduction, these mechanisms introduce slowdowns that makes cycle time estimation a hard task and impact job scheduling efficiency. Existing methods for estimating cycle times or designing schedulers often rely on predefined safety models, which may not accurately reflect real-world safety implementations, as these depend on case-specific risk assessments. In this paper, we propose a deep learning approach to predict the robot's safety scaling factor directly from process execution data. We analyze multiple neural network architectures and demonstrate that a simple feed-forward network effectively estimates the robot's slowdown. This capability is crucial for improving cycle time predictions and designing more effective scheduling algorithms in collaborative robotic environments.
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