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Additively Manufactured Facade
Timber Assembly with Distributed Architectural Robotics
Human-Machine Collaboration
Eggshell
CantiBox
Robotic Plaster Spraying
Autonomous Dry Stone
RIBB3D
Data Driven Acoustic Design
COMPAS FAB
Mesh Mould Prefabrication
Data Science Enabled Acoustic Design
Thin Folded Concrete Structures
FrameForm
Adaptive Detailing
Deep Timber
Robotic Fabrication Simulation for Spatial Structures
Jammed Architectural Structures
RobotSculptor
Digital Ceramics
On-site Robotic Construction
Mesh Mould Metal
Smart Dynamic Casting and Prefabrication
Spatial Timber Assemblies
Robotic Lightweight Structures
Mesh Mould und In situ Fabricator
Complex Timber Structures
Spatial Wire Cutting
Robotic Integral Attachment
Mobile Robotic Tiling
Software Environments
Aerial Construction
Smart Dynamic Casting
Topologie-Optimierung
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Acoustic Bricks
TailorCrete
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Echord
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Additive Fabrikation
Raumakustik


Autonomous Dry Stone, 2019-2022
Forschungsprojekt
This research is focused on the autonomous, on-site planning and construction of large-scale dry stack structures (walls constructed with irregular stones, without mortar). The project develops an adaptive planning and fabrication pipeline that steers construction towards digitally defined global geometries, while allowing for the use of abundantly available—and highly varied—locally-sourced rock and recycled demolition materials that are extremely low in embodied energy. The process provides a fully reversible alternative to concrete that can be applied, for example, to the construction of retaining walls, load-bearing structures, and revetments for civil infrastructure and landscaping.

The core component of the research is a parallelized planning algorithm and custom software interface that combines feature-based candidate seeding with heuristics adapted from traditional masonry methods, constrained registration, rigid body simulation, and learned classifiers in order to select and position stones from a limited inventory of scanned objects such that they align with a designer-specified target surface.

The construction process has been tailored to the use of HEAP (Hydraulic Excavator for an Autonomous Purpose), a modified 12-ton Menzi Muck M545 walking excavator developed by the Robotic Systems Lab (RSL). The mobile machine uses GNSS and cabin- and arm-mounted LiDAR sensors to provide models of the environment, the available stones, and the in-progress wall — enabling the planner to adapt to the local terrain and account for any settling and unexpected deviations throughout construction. The high payload capacity, reach, and maneuverability of the excavator have facilitated the production of several massive demonstration structures, consisting of tens or hundreds of elements (boulders and concrete debris, approximately 1000 kg each) and reaching heights up to 6 meters.

This research project is pursued in the framework of the National Competence Centre of Research (NCCR) Digital Fabrication.

Publications
Johns, Ryan Luke, Martin Wermelinger, Ruben Mascaro, Dominic Jud, Fabio Gramazio, Matthias Kohler, Margarita Chli, and Marco Hutter. “Autonomous Dry Stone.” Construction Robotics 4, no. 3 (2020): 127–40.
Link

Wermelinger, Martin, Ryan Luke Johns, Fabio Gramazio, Matthias Kohler, and Marco Hutter. “Grasping and Object Reorientation for Autonomous Construction of Stone Structures.” IEEE Robotics and Automation Letters 6, no. 3 (2021): 5105–12.
Link

Credits:
Gramazio Kohler Research, ETH Zurich

Collaborators: Ryan Luke Johns (project lead), Martin Wermelinger, Dominic Jud, Ruben Mascaro, Varin Buff, Vuk Pajovic, Mads Albers, Jomana Baddad, Indra Santosa Dr. Kathrin Dörfler, Dr. Aleksandra Anna Apolinarska, Dr. Lauren Vasey, Prof. Dr. Margarita Chli, Prof. Dr. Marco Hutter
Co-Supervisors: Prof. Dr. Olga Sorkine-Hornung, Prof. Dr. Marco Hutter
Research Programme: NCCR Digital Fabrication 1C2: Mobile Robotic Aggregation of Found Objects
In cooperation with: Robotic Systems Lab (RSL), Vision for Robotics Lab (V4RL)
Sponsors: Eberhard Unternehmungen AG
Copyright 2022, Gramazio Kohler Research, ETH Zurich, Switzerland
Gramazio Kohler Research
Professur für Architektur und Digitale Fabrikation
ETH Zürich HIB E 43
Stefano-Franscini Platz 1 / CH-8093 Zürich

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