Additive manufacturing methods1-4 using static and mobile robots are being developed for both on-site construction5-8 and off-site prefabrication9,10. Here we introduce a method of additive manufacturing, referred to as aerial additive manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders11 such as wasps who use collective building methods12,13. We present a scalable multi-robot three-dimensional (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 three-dimensional 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 the 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 the manufacturing accuracy to five millimetres 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 2.05 metres high consisting of 72 layers of a rapid-curing insulation foam material and a cylinder 0.18 metres high consisting of 28 layers of structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations. Aerial-AM allows manufacturing in-flight and offers future possibilities for building in unbounded, at-height or hard-to-access locations.


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    Titel :

    Aerial additive manufacturing with multiple autonomous robots.


    Beteiligte:
    Zhang, Ketao (Autor:in) / Chermprayong, Pisak (Autor:in) / Xiao, Feng (Autor:in) / Tzoumanikas, Dimos (Autor:in) / Dams, Barrie (Autor:in) / Kay, Sebastian (Autor:in) / Kocer, Basaran Bahadir (Autor:in) / Burns, Alec (Autor:in) / Orr, Lachlan (Autor:in) / Choi, Christopher (Autor:in)

    Erscheinungsdatum :

    2022-09-22


    Anmerkungen:

    Nature , 609 pp. 709-717. (2022)


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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