Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images

Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images

Zandavali, Bárbara Andrade; García, Manuel Jiménez

Article:

Brickwork is the oldest construction method still in use. Digital technologies, inturn, enabled new methods of representation and automation for bricklaying.While automation explored different approaches, representation was limited todeclarative methods, as parametric filling algorithms. Alternatively, this workproposes a framework for automated brickwork using a machine learning modelbased on image-to-image translation (Conditional Generative AdversarialNetworks). The framework consists of creating a dataset, training a model foreach bond, and converting the output images into vectorial data for roboticassembly. Criteria such as: reaching wall boundary accuracy, avoidance ofunsupported bricks, and brick's position accuracy were individually evaluated foreach bond. The results demonstrate that the proposed framework fulfils boundaryfilling and respects overall bonding structural rules. Size accuracy demonstratedinferior performance for the scale tested. The association of this method with`self-calibrating' robots could overcome this problem and be easily implementedfor on-site.

Brickwork is the oldest construction method still in use. Digital technologies, inturn, enabled new methods of representation and automation for bricklaying.While automation explored different approaches, representation was limited todeclarative methods, as parametric filling algorithms. Alternatively, this workproposes a framework for automated brickwork using a machine learning modelbased on image-to-image translation (Conditional Generative AdversarialNetworks). The framework consists of creating a dataset, training a model foreach bond, and converting the output images into vectorial data for roboticassembly. Criteria such as: reaching wall boundary accuracy, avoidance ofunsupported bricks, and brick's position accuracy were individually evaluated foreach bond. The results demonstrate that the proposed framework fulfils boundaryfilling and respects overall bonding structural rules. Size accuracy demonstratedinferior performance for the scale tested. The association of this method with`self-calibrating' robots could overcome this problem and be easily implementedfor on-site.

Palavras-chave:

DOI: 10.5151/proceedings-ecaadesigradi2019_605

Referências bibliográficas

• [1] .

Como citar:

Zandavali, Bárbara Andrade; García, Manuel Jiménez; "Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images", p-217-226. In: Proceedings of 37 eCAADe and XXIII SIGraDi Joint Conference, “Architecture in the Age of the 4Th Industrial Revolution”, Porto 2019, Sousa, José Pedro; Henriques, Gonçalo Castro; Xavier, João Pedro (eds.). São Paulo: Blucher, 2019.
ISSN 23186968, DOI 10.5151/proceedings-ecaadesigradi2019_605