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  4. Aerial additive manufacturing with multiple autonomous robots
 
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Aerial additive manufacturing with multiple autonomous robots
File(s)
nature_accepted.pdf (12.44 MB)
Accepted version
Author(s)
Zhang, Ketao
Chermprayong, pisak
Xiao, feng
Tzoumanikas, dimos
Dams, Barrie
more
Type
Journal Article
Abstract
Additive manufacturing methods 1–4 using static and mobile robots are being
developed for both on-site construction 5–8 and off-site prefabrication 9, 10. Here we introduce a new method of additive manufacturing, referred to as Aerial Additive Manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired
by natural builders 11 such as wasps who use collective building methods 12, 13. We present a scalable multi-robot 3D printing and path planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous 3D printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta
manipulator with the BuilDrone to further improve manufacturing accuracy to 5mm for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder of 2.05m with a rapid curing insulation foam material and a cylinder of 0.18m with strutural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations. Aerial-AM allows manufacturing in-flight
2 and offers future possibilities for building in unbounded, at height, or hard to access locations.
Date Issued
2022-09-21
Date Acceptance
2022-07-06
Citation
Nature, 2022, 609, pp.709-717
URI
http://hdl.handle.net/10044/1/99674
URL
https://www.nature.com/articles/s41586-022-04988-4
DOI
https://www.dx.doi.org/10.1038/s41586-022-04988-4
ISSN
0028-0836
Publisher
Nature Research
Start Page
709
End Page
717
Journal / Book Title
Nature
Volume
609
Copyright Statement
© The Author(s), under exclusive licence to Springer Nature Limited 2022. The final publication is available at Springer via https://doi.org/10.1038/s41586-022-04988-4
Identifier
https://www.nature.com/articles/s41586-022-04988-4
Publication Status
Published
Date Publish Online
2022-09-22
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