| id |
ecaade2024_249 |
| authors |
Gong, Yuemin; Li, Yangzhi; Fingrut, Adam |
| year |
2024 |
| title |
Repurposing Material Through Real-Time Point Cloud Reconstruction and Human-Robot Collaboration |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.183
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 183–192 |
| summary |
Human-robot collaboration in construction has become an emerging field in computer-aided construction due to robots' high efficiency and precision performance in simple processes and repetitive tasks. However, the complex construction site environments and diverse shapes of construction materials present challenges for human-robot collaborative construction. Most construction robots lack computer vision recognition and perception of preassembled parts, which require proper positioning and lack interactive capabilities. In this paper, we propose a system called the Human-Robot Collaboration (HRC)to address these challenges and enhance the assembly of construction walls using recycled materials from construction and demolition waste. The HRC system leverages dual Kinect Azure devices for point cloud fusion and employs a segmentation algorithm specifically designed for this purpose. This enables fast and accurate point cloud reconstruction of recycled parts. The control terminal, implemented using the Grasshopper plugin Machina, provides a real-time display of the 3D geometric model of the target structure and the reference grasping points to achieve UR10e robotic assembly. Additionally, the Kinect Azure human body tracking algorithm ensures workers are alerted and stop in time when entering the workspace. Overall, this research presents an enhanced collaborative and sustainable solution for human-robot construction scenarios, ensures the safety of robotics construction, and offers feasibility and guidance for future automation in construction sites. |
| keywords |
3D Scanning, Point Cloud Reconstruction, Human-Robot Interaction, Robotic Construction, Kinect Azure DK |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,788,726 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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