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
AI and Robotics in Construction
Sensors, Actuators and Artificial Intelligence in Construction Sites
Dr. Shih-Chung (Jessy) Kang recently joined the Nasseri School of Building Science and Engineering at University of Alberta. He is leading a research team to develop robotics solutions for the building industry. He is designing a new AI and robotics lab (AIRCon-Lab) to support the interdisciplinary research to introduce robotics to construction sites. Equipped with multiple robots, sensor networks, virtual reality devices and computation units, the lab allows to simulate a variety of construction scenarios and develop the innovative construction methods by integrating the sensors, artificial intelligence, and automatic machine. The robotics solutions will significantly increase the productivities of construction projects, improve the safety of the sites and reduce the impact from labour shortage especially in Northern America.
Research
Construction automation advances worker safety, productivity analysis and construction processes at construction sites. Dr. Kang’s research team are constantly working towards innovating existing methods to combat the various challenges that exist in the construction automation field. The recent projects developed are: ACID, construction machine tracking method and robotics in construction.
ACID
Alberta Construction Image Dataset (ACID) is a non-profitable detection dataset of construction machines that focuses on integrating deep learning technology in the construction industry. It is an excellent resource for the public to develop deep learning applications in the construction area. (...more)
Dr. Kang obtained his Ph.D. at Stanford in 2005 and worked in National Taiwan University (NTU) from 2006 to 2018. In 2019, Dr. Kang is selected as Distal Fellow of NRCN for his extensive promising work in research. He published five textbooks, 100+ papers and eight patents on construction technologies. He led research projects to develop a smart robo-crane, pavement inspection robots and autonomous erection system. He is co-editor-in-chief of a Springer Journal, Visualization in Engineering. Dr. Kang obtained excellent teaching award in NTU. His courses on Coursera, an online learning platform, attract more than 30,000 online learners. Dr. Kang is also an entrepreneur, co-founding Smart-Aging Tech, a company offering data services for health cares. More information please visit: http://sckang.caece.net
Dr. Shih-Chung Jessy Kang
TEAM
Staff
Students
Publication
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B. Xiao, & S. C. Kang (reviewing). A Multiple Object Tracking Method for Identifying Construction Machines from Videos. Automation in Construction.
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C. H. Yang, T. H. Wu, & S. C. Kang (2019, Jun.). The Design of Future Construction Lab. The 2019 ASCE International Conference on Computing in Civil Engineering (i3CE), Atlanta, Georgia, U.S.A.
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B. Xiao, K. Y. Lam, J. Cui, & S. C. Kang (2019, Jun.). Perceptions for Crane Operations.The 2019 ASCE International Conference on Computing in Civil Engineering (i3CE), Atlanta, Georgia, U.S.A.
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C. H. Yang, T. H. Wu, B. Xiao, & S. C. Kang (2019, May). Design of a Robotic Software Package for Builders of Modular Home Manufacturing. 2019 International Symposium on Automation and Robotics in Construction (ISARC), Banff, AB, Canada.
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B. Xiao, & S. C. Kang (2019, May). Deep Learning Detection for Real-time Construction Machine Checking. 2019 International Symposium on Automation and Robotics in Construction (ISARC), Banff, AB, Canada.
Join Us
R-Lab is a new lab in University of Alberta. We are looking for brave students who have the courage to move the construction industry forward to a robotics era. They are expected to develop solutions for solving real problems. Students need to have strong engineering backgrounds and also programming skills. The current research topics include:
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BIM-enabled design for robotics in construction
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Artificial Intelligence for robot controls in the building industry
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Simulation methods for the assembly line for modular home and modular constructions
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Reasoning vision information to identify construction problem proactively.
We offer scholarships for MSc and Ph.D. students. We also provide internship and volunteer opportunity for undergrad students. If you are interested in the position, please contact Prof. Kang with a description of your background and how the background supports the research in R-Lab.
More information please visit:
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Prof. Kang's personal website: http://sckang.caece.net
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Department of Civil and Environmental Engineering, University of Alberta: https://www.ualberta.ca/civil-environmental-engineering/graduate-studies
Or email to: sckang@ualberta.ca