| id |
ecaade2024_306 |
| authors |
Gu, Sijia; Yuan, Philip F. |
| year |
2024 |
| title |
Research on Autonomous Recognition and Gripping Method for Robotic Fabrication of Heterogeneous Masonry Based on Computer Vision |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 127–136 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.127
|
| summary |
The emphasis on material diversity in robotic fabrication processes enhances the freedom of design in form and function, enabling the possibility of masonry working as functionally graded materials. However, in the robotic fabrication process based on offline programming, the lack of autonomous judgment of brick materials restricts the fabrication of multi-material masonry, resulting in additional labor and equipment costs. In this context, improving the autonomous judgment ability of construction robots on materials becomes an important breakthrough point, for which computer vision is a possible solution. However, current research on brick materials based on object detection mainly focuses on crack inspection and cannot distinguish multiple types of bricks in the same fabrication process. Therefore, the research aims to establish a methodology for an automatic multi-material brick grasping process based on the plane. The method consists of three parts: target detection, data conversion, and robot grasping. In this process, the research aims to innovate in four aspects: targets of object detection, derivation of dataset structure, introduction of design models, and real-world physical validation. Based on the proposal, a full-stage validation experiment was conducted. The experimental results validate the feasibility of the proposed method, hoping to bring new insights to robotic fabrication and parametric masonry design. |
| keywords |
Robotic Fabrication, Heterogeneous Masonry, Computer Vision, Deep Learning |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (2,622,420 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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