Advancements in Measurement Assisted Assembly Using iGPS: From Robotic Assembly to Augmented Reality Guidance

Assembly using dedicated jigs is recognized as a process offering excellent performance in terms of accuracy and repeatability. However, it is often criticized for its lack of flexibility and high cost, especially in low-volume production. Measurement-Assisted Assembly (MAA) replaces these physical jigs with real-time metrological feedback, enabling jigless assembly while maintaining geometric and dimensional requirements. Among the available technologies, indoor Global Positioning Systems (iGPS) provide large-scale coverage suitable for assembling medium- to large-scale components. Nevertheless, their metrological capabilities, particularly when combined with robotic systems and augmented reality, remain insufficiently characterized. This study presents two complementary contributions aimed at enhancing iGPS-based Measurement- Assisted Assembly (MAA). First, a robotic test bench was designed to empirically infer the positional repeatability of iGPS-guided operations (with iGPS acting as an external encoder), enabling the establishment of statistical confidence intervals and thereby providing indicators of process capability under controlled conditions inspired by ISO 9283. Second, the study integrates a reproducibility assessment of an MAA operation assisted by an augmented reality (AR) interface: the iGPS reference frame was linked to a Microsoft HoloLens 2 headset via a calibrated mount equipped with i5 sensors and ArUco markers. An additional module developed for PolyWorks™ enabled the integration of iGPS data into the AR environment, providing simultaneous metrological feedback and visual guidance for the precise positioning of parts. These implementations demonstrate the versatility of iGPS-based MAA across automation levels. The framework enables both quantitative assessment of geometric compliance and practical deployment for guided assembly tasks. We further discuss the complementary advantages and potential use cases of robotic versus AR-assisted MAA, providing insight into conditions where each approach is most effective for tolerance-critical jigless assembly.