Adaptive Multi-Robot Timber Construction Dataset

Ruan, Daniel
File(s) associated with this object are embargoed until 2024-06-04.
Issue date: 2024
Creative Commons Attribution 4.0 International (CC BY)
Cite as:
Ruan, Daniel. (2024). Adaptive Multi-Robot Timber Construction Dataset [Data set]. Princeton University.
  author      = {Ruan, Daniel},
  title       = {{Adaptive Multi-Robot Timber Construction
  publisher   = {{Princeton University}},
  year        = 2024,
  url         = {}

The deposit contains data related to the adaptive multi-robotic assembly of timber structures. This data was collected across two experiments; the first experiment utilizes a pose-based adaptive fabrication method applied to a nail-laminated timber module, and the second experiment utilizes a topology-based adaptive fabrication method applied to a spatial wall frame module. The first experiment data contains the pose measurements for each element in the assembly structure and overall point cloud scans of the final as-built assemblies. Pose measurements were derived from a defined set of profile scans using a profile scanner attached to the end of an industrial robot arm, using the methods detailed in the corresponding paper. This scanner has a profile resolution of 0.150 mm, a depth resolution of 0.019 mm, and a linearity of ±0.01% of the measurement range. These pose measurements were used as inputs to the adaptive control loop, and also served as measures of deviation when compared to a benchmark case assembly with no adaptation. The point cloud scans were likewise obtained using the profile scanner by controlling the industrial robot to perform a linear sweep across the final structure, capturing a profile of points every 2 mm. Since the field of view of the sensor was insufficient to scan the entire structure in a single pass, multiple passes were made with a slight overlap, and these point cloud segments were stitched together using the calibrated end-effector position of the industrial robot. The point clouds were manually filtered to remove background elements, such as the assembly platform and clamping elements. These point clouds were utilized to compute surface deviations of the adaptive and benchmark assembly cases against an idealized digital model. The deviation data corresponds to the order of points in their corresponding point cloud files. The second experiment did not utilize pose measurements, and, therefore, the database only includes the point cloud scan and deviation calculations of the as-built wall module. For more details, please see the corresponding paper.

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File(s) associated with this object are embargoed until 2024-06-04.