Towards Automatic Path Planning for Robotically Assembled Spatial Structures

Gandia, Augusto; Parascho, Stefana; Rust, Romana; Casas, Gonzalo Galiano; Gramazio, Fabio; Köhler, Matthias

doi:10.1007/978-3-319-92294-2_5

Cited by 18 publications

(9 citation statements)

References 7 publications

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“…Along similar lines, KUKA robots introduced one of the early uses of a robotic pick-and-place assembly unit, entitled The SequentialWall project, where timbers are laid down on top of each other (Gramazio, 2010). Gandia et al presented work demonstrating the integration of automatic path planning into the design environment to enable the multi-robotic assembly of spatial structures (Gandia, 2018).…”

Section: Robotic Fabricationmentioning

confidence: 99%

[AR]OBOT: the AR-Assisted Robotic Fabrication System for Parametric Architectural Structures

Song¹,

Koeck²,

Luo³

2021

Blucher Design Proceedings

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OBOT tries to assist the robotic fabrication process for parametric architectural structures with Augmented Reality (AR) technology to explore new possibilities for easy architectural robotic operations. Due to the lack of computer programming skills and the disconnection between design and fabrication, architects are hampered in the robotic operation process. As part of our project, we create a visualization prototype in which robotic and on-site related information is being shown through AR devices overlapping on the physical world; followed by a robotic trajectory planning method in which designers' gestures are being identified by AR as location nodes and calculated with the obstacle avoidance system; and an operation process in which robots are being controlled by human gestures and interactions with holographic simulation to enhance the robotic fabrication process efficiency and safety. In this paper, we share the preliminary results to demonstrate a new kind of AR-assisted workflow for the architects to perform the robotic fabrication of parametric architectural structures intuitively.

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Section: Robotic Fabricationmentioning

confidence: 99%

[AR]OBOT: the AR-Assisted Robotic Fabrication System for Parametric Architectural Structures

Song¹,

Koeck²,

Luo³

2021

Blucher Design Proceedings

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“…The designer is required to have advance knowledge of all potential obstacle positions and draw paths the robot should follow to avoid collisions. While approaches to circumvent the problem through global path planning sequence search have been implemented (Gandia et al, 53 the solution is often computationally heavy, and cannot be implemented in real time).…”

Section: Contextmentioning

confidence: 99%

Towards machine learning for architectural fabrication in the age of industry 4.0

Thomsen

Nicholas

Tamke

et al. 2020

International Journal of Architectural Computing

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Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.

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“…Existen algoritmos que resuelven eficientemente la secuencia de extrusión, las poses del efector final del robot, la posición de las articulaciones y las trayectorias transitorias para cerchas espaciales (Huang, Garret y Mueller, 2018). Un ejemplo más puntual es que mientras un robot sostiene la estructura temporalmente, el otro posiciona una pieza dentro de esta (Gandia et al, 2019).…”

Section: Sistemas Retroalimentados Y Algoritmos De Planificaciónunclassified

Una revisión de plataformas robóticas para el sector de la construcción

Becerra

2020

Tecnura

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Este artículo presenta una revisión de plataformas robóticas para el sector de la construcción; enfocándose en aquellas que fabrican piezas o módulos con geometrías complejas en entornos fuera del área de construcción; sistemas retroalimentados para entornos cambiantes y algoritmos de planificación de rutas utilizados para el posicionamiento del efector final; y por último, robots que realizan tareas pesadas o repetitivas para el beneficio del ser humano. Además, el artículo también presenta una serie de ventajas y desafíos que tienen los robots en el sector de la construcción para los años venideros.

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Towards Automatic Path Planning for Robotically Assembled Spatial Structures

Cited by 18 publications

References 7 publications

[AR]OBOT: the AR-Assisted Robotic Fabrication System for Parametric Architectural Structures

[AR]OBOT: the AR-Assisted Robotic Fabrication System for Parametric Architectural Structures

Towards machine learning for architectural fabrication in the age of industry 4.0

Una revisión de plataformas robóticas para el sector de la construcción

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