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_id	acadia19_130
id	acadia19_130
authors	Devadass, Pradeep; Heimig, Tobias; Stumm, Sven; Kerber, Ethan; Cokcan, Sigrid Brell
year	2019
title	Robotic Constraints Informed Design Process
doi	https://doi.org/10.52842/conf.acadia.2019.130
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 130-139
summary	Promising results in efficiently producing highly complex non-standard designs have been accomplished by integrating robotic fabrication with parametric design. However, the project workflow is hampered due to the disconnect between designer and robotic fabricator. The design is most often developed by the designer independently from fabrication process constraints. This results in fabrication difficulties or even non manufacturable components. In this paper we explore the various constraints in robotic fabrication and assembly processes, analyze their influence on design, and propose a methodology which bridges the gap between parametric design and robotic production. Within our research we investigate the workspace constraints of robots, end effectors, and workpieces used for the fabrication of an experimental architectural project: “The Twisted Arch.” This research utilizes machine learning approaches to parameterize, quantify, and analyze each constraint while optimizing how those parameters impact the design output. The research aims to offer a better planning to production process by providing continuous feedback to the designer during early stages of the design process. This leads to a well-informed “manufacturable” design.
keywords	Robotic Fabrication and Assembly, Mobile Robotics, Machine Learning, Parametric Design, Constraint Based Design.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

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