Automated USMN integration for precision robotics and large-scale metrology

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

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Cavalcanti, A.
  • Foster, S.
  • Richardson, R.
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:
  • Published (online): 13 August 2025
  • Published: 13 August 2025
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):

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