Robot Studio for Builders
This project aims to fill the gap of ICT technology between the building industry and the robot industry. The building industry often adopts either design-bid-build or design-build workflows. In both workflows, the designers put their wisdom in a set of drawings, and the builders read the drawings and build a house. While involving robots in the workflow, a new method, transferring the drawings into a series of robot commands, is required. We develop a software package, tentatively coined as Robotic Studio for Builders (RS4B) to link the BIM and robot controllers. RS4B includes four software: BIM Exporter (Be), Assembly Planner (Ap), Robot Simulator (Rs) and Motion Planner (Mp).
RLab
AIRCon-Lab
Artificial Intelligence and Robotics in Construction
ACID: Alberta Construction Image Dataset
The Alberta Construction Image Dataset (ACID) is a construction machine detection dataset developed by AIRCon-Lab at the University of Alberta. The images in ACID were collected from construction sites that are from all over the world. ACID focuses on integrating deep learning technology in the construction industry and can be downloaded from the University of Alberta library. It is the resource for the community to develop deep learning applications in the construction automation field.
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There are ten types of machines in the ACID dataset, which are excavator, compactor, dozer, grader, dump truck, cement truck, wheel loader, backhoe loader, tower crane, and mobile crane. These machines are common in modern construction sites. ACID includes 10,000 labeled images, 15,767 construction machine objects for training deep learning detection algorithms.
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More information about ACID can be acquired in the ACID website (www.acidb.ca)
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