On Using Neural Networks to Learn Safety Speed Reduction in Human-Robot Collaboration: A Comparative Analysis

TL;DR

This paper introduces a neural network-based method to predict safety-induced robot speed reductions in human-robot collaboration, enhancing cycle time estimation and scheduling accuracy.

Contribution

It presents a deep learning approach that directly predicts safety scaling factors from process data, improving over traditional safety model-based estimations.

Findings

A simple feed-forward network effectively estimates robot slowdown.

The approach improves cycle time prediction accuracy.

Enhances scheduling algorithms in collaborative robotics.

Abstract

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.