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This research investigates the precision and repeatability of industrial robots, focusing on a Universal Robot (UR) model, using both direct and indirect measurement approaches. The study employs direct data collection methods, including the use of a high-precision CTrack 780 measurement system, to analyze various metrics such as joint position, temperature, torque, velocity, and current. The findings reveal significant correlations between joint temperature, current consumption, and the robot's precision. Notably, temperature fluctuations adversely affect absolute accuracy, highlighting the necessity for automated adjustments in robot programming. Future work includes replicating the study on different machines and developing machine learning models for real-time adjustments to enhance performance and adaptability. The methodology involves a three-step process: data collection using the UR software development kit (SDK), application development for real-time data visualization and maintenance alerts, and data analysis to identify positional and control system inaccuracies. This comprehensive approach is validated through experimental studies and aims to improve the reliability and maintenance of collaborative robot systems. The research underscores the critical role of adaptive strategies in mitigating environmental impacts and maintaining high precision in robotic operations.

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Evaluating Precision and Repeatability of Industrial Robots Using Direct and Indirect Measurement Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Stelian Brad,
  • Vlad Florian,
  • Eyas Deeb,
  • Bogdan Balog,
  • Vasile-Dragoș Bartoș,
  • Stefan Bodi,
  • Ovidiu Stan

摘要

This research investigates the precision and repeatability of industrial robots, focusing on a Universal Robot (UR) model, using both direct and indirect measurement approaches. The study employs direct data collection methods, including the use of a high-precision CTrack 780 measurement system, to analyze various metrics such as joint position, temperature, torque, velocity, and current. The findings reveal significant correlations between joint temperature, current consumption, and the robot's precision. Notably, temperature fluctuations adversely affect absolute accuracy, highlighting the necessity for automated adjustments in robot programming. Future work includes replicating the study on different machines and developing machine learning models for real-time adjustments to enhance performance and adaptability. The methodology involves a three-step process: data collection using the UR software development kit (SDK), application development for real-time data visualization and maintenance alerts, and data analysis to identify positional and control system inaccuracies. This comprehensive approach is validated through experimental studies and aims to improve the reliability and maintenance of collaborative robot systems. The research underscores the critical role of adaptive strategies in mitigating environmental impacts and maintaining high precision in robotic operations.