Advancing Sustainable Construction: Discrete Modular Systems & Robotic Assembly

This research explores the SL-Block system within an architecture framework by embracing building modularity, combinatorial design, topological interlocking, machine learning, and tactile sensor-based robotic assembly. The SL-Block, composed of S and L-shaped tetracubes, possesses a unique self-inte...

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Bibliographic Details
Published in:Sustainability 2024-08, Vol.16 (15), p.6678
Main Authors: Liu, Yuxi, Belousov, Boris, Schneider, Tim, Harsono, Kevin, Cheng, Tsung-Wei, Shih, Shen-Guan, Tessmann, Oliver, Peters, Jan
Format: Article
Language:English
Subjects:
Adaptability
Architecture
Automation
Buildings
Construction
Design
Geometry
Green building (Construction)
Green buildings
Labor shortages
Machine learning
Masonry
Modularity
Robotics
Robots
Savings and loan associations
Skilled workers
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Summary:This research explores the SL-Block system within an architecture framework by embracing building modularity, combinatorial design, topological interlocking, machine learning, and tactile sensor-based robotic assembly. The SL-Block, composed of S and L-shaped tetracubes, possesses a unique self-interlocking feature that allows for reversible joining and the creation of various 2D or 3D structures. In architecture modularity, the high degree of reconfigurability and adaptability of the SL-Block system introduces a new element of interest. Unlike modularization strategies that emphasize large-scale volumetric modules or standardized building components, using small-scale generic building blocks provides greater flexibility in maximizing design variations and reusability. Furthermore, the serial repetition and limited connectivity of building elements reduce the efforts required for bespoke manufacturing and automated assembly. In this article, we present our digital design and robotic assembly strategies for developing dry-jointed modular construction with SL-Blocks. Drawing on combinatorics and graph theory, we propose computational design methods that can automatically generate hierarchical SL-Block assemblies from given shapes. To address the physical complexities of contact-rich assembly tasks, we develop robotics using two distinct methods: pre-programmed assembly and sensor-based reinforcement learning. Through a series of demonstrators, we showcase the ability of SL-Blocks not only to reconfigure conventional building tectonics but also to create new building configurations.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16156678