UAV inspection of large components: determination of alternative inspection points and online route optimization

  • Automation is playing an increasing role in the field of quality assurance. For the visual inspection of larger assemblies such as aircraft fuselages or ship hulls, the use of UAVs is an option. This paper deals with one aspect of the UAV-supported inspection of assemblies in production. Here, newly added components have to be checked for correct assembly. The planning of the shortest possible route from which all components to be inspected can be viewed as well as the estimation of the UAV’s position relative to the component have already been presented in previous work. We propose strategies that can be used if an inspection point cannot be reached by the UAV or the component to be inspected cannot be seen by the UAV’s camera from the inspection point. For this purpose, we generate alternative inspection points that can be used if errors occur during the inspection from the original inspection point. To achieve this, we present a metric that can be used to select an alternativeAutomation is playing an increasing role in the field of quality assurance. For the visual inspection of larger assemblies such as aircraft fuselages or ship hulls, the use of UAVs is an option. This paper deals with one aspect of the UAV-supported inspection of assemblies in production. Here, newly added components have to be checked for correct assembly. The planning of the shortest possible route from which all components to be inspected can be viewed as well as the estimation of the UAV’s position relative to the component have already been presented in previous work. We propose strategies that can be used if an inspection point cannot be reached by the UAV or the component to be inspected cannot be seen by the UAV’s camera from the inspection point. For this purpose, we generate alternative inspection points that can be used if errors occur during the inspection from the original inspection point. To achieve this, we present a metric that can be used to select an alternative inspection point that is as suitable as possible. We conclude by demonstrating how this strategy works by evoking different failure cases in a simulated environment.show moreshow less

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Metadaten
Author:Martin SchörnerORCiDGND, Constantin WanningerORCiDGND, Raphael Katschinsky, Simon Hornung, Christian EymüllerORCiDGND, Alexander PoeppelGND, Wolfgang ReifORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1087745
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/108774
ISBN:979-8-3503-0185-4OPAC
Parent Title (English):2023 IEEE/ACM 5th International Workshop on Robotics Software Engineering (RoSE), 15 May 2023, Melbourne, Australia
Publisher:IEEE
Place of publication:Piscataway, NJ
Type:Conference Proceeding
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/10/30
First Page:45
Last Page:52
DOI:https://doi.org/10.1109/RoSE59155.2023.00012
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht