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Towards Visual Inspection of Wind Turbines: A Case of Visual Data Acquisition using Autonomous Aerial Robots
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8870-6718
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-5735-5484
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-2001-7171
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 181650-181661Article in journal (Refereed) Published
Abstract [en]

This article presents a novel framework for acquiring visual data around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial system reaches high level of autonomy on a large scale, while pushing to the boundaries the real life deployment of aerial robotics. In the presented approach, the MAVs deployed around the structure rely only on their onboard computer and sensory systems. The developed framework envisions a modular system, combining open research challenges in the fields of localization, path planning and mapping, with an overall capability for a fast on site deployment and a reduced execution time that can repeatably perform the mission according to the operator needs. The architecture of the established system includes: 1) a geometry-based path planner for coverage of complex structures by multiple MAVs, 2) an accurate yet flexible localization component, which provides an accurate pose estimation for the MAVs by utilizing an Ultra Wideband fused inertial estimation scheme, and 3) visual data post-processing scheme for the 3D model building. The performance of the proposed framework has been experimentally demonstrated in multiple realistic outdoor field trials, all focusing on the challenging structure of a wind turbine as the main test case. The successful experimental results, depict the merits of the proposed autonomous navigation system as the enabling technology towards aerial robotic inspectors.

Place, publisher, year, edition, pages
IEEE, 2020. Vol. 8, p. 181650-181661
Keywords [en]
Collaborative Aerial Infrastructure Inspection, Collaborative Coverage, Dense Reconstruction, Micro Aerial Vehicles, Ultra WideBand inertial state estimation
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-81075DOI: 10.1109/ACCESS.2020.3028195ISI: 000577890200001Scopus ID: 2-s2.0-85102809727OAI: oai:DiVA.org:ltu-81075DiVA, id: diva2:1474600
Note

Validerad;2020;Nivå 2;2020-10-29 (alebob)

Available from: 2020-10-09 Created: 2020-10-09 Last updated: 2021-04-12Bibliographically approved

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Kanellakis, ChristoforosFresk, EmilMansouri, Sina SharifKominiak, DariuszNikolakopoulos, George

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