Impacts of Collaborative Robots on Construction Work Performance and Worker Perception: Experimental Analysis of Human–Robot Collaborative Wood Assembly
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 8
Abstract
Collaborative robots are increasingly recognized as potential assistants to relieve workers from repetitive and physically demanding tasks on construction jobsites. Despite the great potential, most efforts have focused on developing various artificial intelligence (AI) and robotic technologies to achieve specific human–robot collaboration (HRC) functions. However, there is a significant lack of research regarding the impacts of such collaboration on construction work performance and workers’ perception and acceptance of collaborative robots, which could be a critical influence factor on the feasibility and effectiveness of HRC on construction jobsites. To this end, this study aims to evaluate the multidimensional impacts of collaborative robots on work efficiency, quality, and workload as well as workers’ perception and acceptance. HRC experiments on sample construction tasks (i.e., wood assembly) were conducted in conjunction with quantitative measurements and subject surveys. Through comparison between HRC experiments and human–human collaboration (HHC) experiments based on this case study, it was found that HRC could improve up to 29.3% and 88.6% in work efficiency and assembly accuracy, respectively, and reduce workers’ workload by up to 20.3%. Furthermore, workers’ perception of HRC is found to be positive overall with higher acceptance after HRC experience, characterized by questionnaires designed based on the technology acceptance model. Through physical experiments, this research is expected to produce more reliable results compared with conventional approaches where participants are simply provided with imaginary scenarios. The findings will also guide the development of robotic technologies to enhance the practical application of HRC in construction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the US National Science Foundation (NSF) via Grants 2138514 and 2222670. The authors gratefully acknowledge NSF’s support. Any opinions, findings, recommendations, and conclusions in this paper are those of the authors and do not necessarily reflect the views of NSF and The University of Texas at San Antonio.
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Journal of Construction Engineering and Management
Volume 150 • Issue 8 • August 2024
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© 2024 American Society of Civil Engineers.
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Received: Aug 14, 2023
Accepted: Jan 9, 2024
Published online: May 31, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 31, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Automation and robotics
- Business management
- Case studies
- Comparative studies
- Computer programming
- Computing in civil engineering
- Construction (by type)
- Construction engineering
- Construction management
- Employment
- Engineering fundamentals
- Labor
- Methodology (by type)
- Personnel management
- Practice and Profession
- Research methods (by type)
- Systems engineering
- Wood construction
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