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<p>This work presents a low-cost, vision-based safety monitoring system for human–robot collaboration (HRC) in industrial environments. A power-bank assembly case study involving two robots and a human operator is used for validation. One robot performs pick-and-place operations, while the second robot executes screwing tasks, and a human assistant performs soldering. Human presence is detected using a YOLOv8 instance-segmentation model with a single overhead RGB camera. Based on the detected human position, the workspace is divided into three adaptive safety zones: <i>red, yellow</i>, and <i>green</i>. The red zone is derived from the robot’s reachable workspace in RoboDK and triggers a functional stop when a human enters the hazardous region. The yellow zone reduces robot speed by 50 %, while the green zone allows full operation. A RoboDK digital twin is used for real-time monitoring of robot motion, enabling additional collision awareness during operation. Experimental results using real robotic systems demonstrate that integrating lightweight vision-based detection with digital-twin monitoring provides an effective and scalable approach for improving safety awareness in collaborative robotic work cells.</p>

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A vision-based safety system for human–robot collaboration using a real-time digital twin in RoboDK

  • 期刊论文

  • 发表日期：

    2026年8月1日

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  • Birzhanuly Dosymzhan,
  • Akniyazov Esimzhan,
  • Azamatkyzy Ainur,
  • Gholibjon Qasobov,
  • Muhammad Ilyas

摘要

This work presents a low-cost, vision-based safety monitoring system for human–robot collaboration (HRC) in industrial environments. A power-bank assembly case study involving two robots and a human operator is used for validation. One robot performs pick-and-place operations, while the second robot executes screwing tasks, and a human assistant performs soldering. Human presence is detected using a YOLOv8 instance-segmentation model with a single overhead RGB camera. Based on the detected human position, the workspace is divided into three adaptive safety zones: red, yellow, and green. The red zone is derived from the robot’s reachable workspace in RoboDK and triggers a functional stop when a human enters the hazardous region. The yellow zone reduces robot speed by 50 %, while the green zone allows full operation. A RoboDK digital twin is used for real-time monitoring of robot motion, enabling additional collision awareness during operation. Experimental results using real robotic systems demonstrate that integrating lightweight vision-based detection with digital-twin monitoring provides an effective and scalable approach for improving safety awareness in collaborative robotic work cells.