Asif, S. orcid.org/0000-0001-7048-0183, Izuwa, E. orcid.org/0009-0005-4822-6783, Sawyer, D. orcid.org/0009-0001-0056-2578 et al. (1 more author) (2025) Automated USMN integration for precision robotics and large-scale metrology. In: Cavalcanti, A., Foster, S. and Richardson, R., (eds.) Towards Autonomous Robotic Systems. 26th TAROS Conference 2025, 20-22 Aug 2025, York, United Kingdom. Lecture Notes in Computer Science, 16045. Springer Nature Switzerland, pp. 68-79. ISBN: 9783032014856.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2025 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Towards Autonomous Robotic Systems is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Information and Computing Sciences; Networking and Information Technology R&D (NITRD) |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield) The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/W037009/1 Engineering and Physical Sciences Research Council EP/X528493/1 |
Date Deposited: | 06 Oct 2025 11:42 |
Last Modified: | 06 Oct 2025 11:42 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-032-01486-3_7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:232596 |