01 Fakultät Architektur und Stadtplanung
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/2
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Item Open Access Spatial winding : cooperative heterogeneous multi-robot system for fibrous structures(2020) Duque Estrada, Rebeca; Kannenberg, Fabian; Wagner, Hans Jakob; Yablonina, Maria; Menges, AchimThis research presents a cooperative heterogeneous multi-robot fabrication system for the spatial winding of filament materials. The system is based on the cooperation of a six-axis robotic arm and a customized 2 + 2 axis CNC gantry system. Heterogeneous multi-robot cooperation allows to deploy the strategy of Spatial Winding: a new method of sequential spatial fiber arrangement, based on directly interlocking filament-filament connections, achieved through wrapping one filament around another. This strategy allows to create lightweight non-regular fibrous space frame structures. The new material system was explored through physical models and digital simulations prior to deployment with the proposed robotic fabrication process. An adaptable frame setup was developed which allows the fabrication of a variety of geometries within the same frame. By introducing a multi-step curing process that integrates with the adaptable frame, the iterative production of continuous large-scale spatial frame structures is possible. This makes the structure’s scale agnostic of robotic reach and reduces the necessary formwork to the bare minimum. Through leveraging the capacities of two cooperating machines, the system allows to counteract some of their limitations. A flexible, dynamic and collaborative fabrication system is presented as a strategy to tailor the fiber in space and expand the design possibilities of lightweight fiber structures. The artifact of the proposed fabrication process is a direct expression of the material tectonics and the robotic fabrication system.Item Open Access Autonomous robotic additive manufacturing through distributed model‐free deep reinforcement learning in computational design environments(2022) Felbrich, Benjamin; Schork, Tim; Menges, AchimThe objective of autonomous robotic additive manufacturing for construction in the architectural scale is currently being investigated in parts both within the research communities of computational design and robotic fabrication (CDRF) and deep reinforcement learning (DRL) in robotics. The presented study summarizes the relevant state of the art in both research areas and lays out how their respective accomplishments can be combined to achieve higher degrees of autonomy in robotic construction within the Architecture, Engineering and Construction (AEC) industry. A distributed control and communication infrastructure for agent training and task execution is presented, that leverages the potentials of combining tools, standards and algorithms of both fields. It is geared towards industrial CDRF applications. Using this framework, a robotic agent is trained to autonomously plan and build structures using two model-free DRL algorithms (TD3, SAC) in two case studies: robotic block stacking and sensor-adaptive 3D printing. The first case study serves to demonstrate the general applicability of computational design environments for DRL training and the comparative learning success of the utilized algorithms. Case study two highlights the benefit of our setup in terms of tool path planning, geometric state reconstruction, the incorporation of fabrication constraints and action evaluation as part of the training and execution process through parametric modeling routines. The study benefits from highly efficient geometry compression based on convolutional autoencoders (CAE) and signed distance fields (SDF), real-time physics simulation in CAD, industry-grade hardware control and distinct action complementation through geometric scripting. Most of the developed code is provided open source.