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This study introduces a novel automation framework for the integration of the Unified Spatial Metrology Network (USMN) across Spatial Analyzer (SA) and PolyWorks (PW), addressing critical inefficiencies in manual metrology workflows. Traditional methods for USMN execution and data translation between platforms are labor-intensive, error-prone, and time-consuming. The proposed system automates data transfer, reference point alignment, and coordinate calibration, incorporating real-time error detection to ensure spatial coherence and enhance measurement accuracy. This approach significantly reduces processing time from days to minutes, mitigates human error, and standardizes inter-software interoperability, while maintaining residual RMS error within ≤0.02 mm. Application of this framework to large-scale robotic systems—common in aerospace, shipbuilding, and automotive manufacturing—demonstrates improved precision in automated tasks such as assembly, drilling, and alignment. By enabling seamless integration of multiple spatial instruments, the framework enhances the robustness and repeatability of high-precision measurements. This advancement represents a pivotal contribution to metrology automation and scalable, real-time calibration in complex industrial environments.

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Automated USMN Integration for Precision Robotics and Large-Scale Metrology

  • 会议论文

  • 发表日期：

    2026年

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  • Seemal Asif,
  • Emmanuel Izuwa,
  • Daniela Sawyer,
  • Christopher Burkinshaw

摘要

This study introduces a novel automation framework for the integration of the Unified Spatial Metrology Network (USMN) across Spatial Analyzer (SA) and PolyWorks (PW), addressing critical inefficiencies in manual metrology workflows. Traditional methods for USMN execution and data translation between platforms are labor-intensive, error-prone, and time-consuming. The proposed system automates data transfer, reference point alignment, and coordinate calibration, incorporating real-time error detection to ensure spatial coherence and enhance measurement accuracy. This approach significantly reduces processing time from days to minutes, mitigates human error, and standardizes inter-software interoperability, while maintaining residual RMS error within ≤0.02 mm. Application of this framework to large-scale robotic systems—common in aerospace, shipbuilding, and automotive manufacturing—demonstrates improved precision in automated tasks such as assembly, drilling, and alignment. By enabling seamless integration of multiple spatial instruments, the framework enhances the robustness and repeatability of high-precision measurements. This advancement represents a pivotal contribution to metrology automation and scalable, real-time calibration in complex industrial environments.