id |
ecaade2022_384 |
authors |
Naboni, Roberto, Breseghello, Luca and Sanin, Sandro |
year |
2022 |
title |
Environment-Aware 3D Concrete Printing through Robot-Vision |
doi |
https://doi.org/10.52842/conf.ecaade.2022.2.409
|
source |
Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 409–418 |
summary |
In the 2020s, large scale 3D concrete printing (3DCP) is one of the most important areas of development for research and industry in construction automation. However, the available technology fails to adapt to the complexity of a real construction site and building process, oversimplifying design, production, and products to fit the current state of technology. We hypothesise that by equipping printing machinery with sensing devices and adaptive design algorithms we can radically expand the range of applications and effectiveness of 3DCP. In this paper we prove this concept through a full-scale design-to- fabrication experiment, SENS-ENV, consisting of three main phases: (i) we equip and calibrate an existing robotic setup for 3DCP with a camera which collects geometric data; (ii) building upon the collected information, we use environment-aware generative design algorithms to conceive a toolpath design tailored for the specific environment with a quasi-real-time workflow; (iii) we successfully prove this approach with a number of fabrication test-elements printed on unknown environment configurations and by monitoring the fabrication process to apply printing corrections. The paper describes the implementation and the successful experiments in terms of technology setup, process development, and documenting the outcomes. SENS-ENV opens a new agenda for context-aware autonomous additive construction robots. |
keywords |
3D Concrete Printing, Robot Vision, Environment Mapping, Adaptive Design |
series |
eCAADe |
email |
|
full text |
file.pdf (4,354,480 bytes) |
references |
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2024/04/22 07:10 |
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