Clark, R.A., Punzo, G. orcid.org/0000-0003-4246-9045, MacLeod, C.N. et al. (5 more authors) (2017) Autonomous and scalable control for remote inspection with multiple aerial vehicles. Robotics and Autonomous Systems, 87. pp. 258-268. ISSN 0921-8890
Abstract
© 2016 Elsevier B.V.A novel approach to the autonomous generation of trajectories for multiple aerial vehicles is presented, whereby an artificial kinematic field provides autonomous control in a distributed and highly scalable manner. The kinematic field is generated relative to a central target and is modified when a vehicle is in close proximity of another to avoid collisions. This control scheme is then applied to the mock visual inspection of a nuclear intermediate level waste storage drum. The inspection is completed using two commercially available quadcopters, in a laboratory environment, with the acquired visual inspection data processed and photogrammetrically meshed to generate a three-dimensional surface-meshed model of the drum. This paper contributes to the field of multi-agent coverage path planning for structural inspection and provides experimental validation of the control and inspection results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier. This is an author produced version of a paper subsequently published in Robotics and Autonomous Systems. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Artificial kinematic field; Automatic optical inspection; Photogrammetry; Swarm; Unmanned aerial vehicle control |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Dec 2016 09:35 |
Last Modified: | 17 Jan 2020 12:49 |
Published Version: | https://doi.org/10.1016/j.robot.2016.10.012 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.robot.2016.10.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109395 |