Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 3
Abstract
Despite the increased interest in automation and the expanded deployment of robots in the construction industry, using robots in a dynamic and unstructured working environment has caused safety concerns in operating construction robots. Improving human–robot interaction (HRI) can increase the adoption of robots on construction sites; for example, increasing trust in robots could help construction workers to accept new technologies. Confidence in operation (or self-efficacy), mental workload, and situational awareness are among other key factors that help such workers to remote operate robots safely. However, construction workers have very few opportunities to practice with robots to build trust, self-efficacy, and situational awareness, as well as resistance against increasing mental workload, before interacting with them on job sites. Virtual reality (VR) could afford a safer place to practice with the robot; thus, we tested if VR-based training could improve these four outcomes during the remote operation of construction robots. We measured trust in the robot, self-efficacy, mental workload, and situational awareness in an experimental study where construction workers remote-operated a demolition robot. Fifty workers were randomly assigned to either VR-based training or traditional in-person training led by an expert trainer. Results show that VR-based training significantly increased trust in the robot, self-efficacy, and situational awareness, compared to traditional in-person training. Our findings suggest that VR-based training can allow for significant increases in beneficial cognitive factors over more traditional methods and has substantial implications for improving HRI using VR, especially in the construction industry.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study [experiment data (unidentifiable personal information), developed codes that enable interaction with the robot in VR-based training] are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Science Foundation under Grant No. 1822724 and the US Army Research Office under Grant No. W911NF2020053. The assistance of Mr. Mike Martin, Mr. Michael Peschka, and Brokk Inc. throughout this research study is greatly appreciated. Any opinion, content, or information presented does not necessarily reflect the position or the policy of the National Science Foundation’s views, and no official endorsement should be inferred.
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Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 3 • May 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Sep 2, 2021
Accepted: Dec 7, 2021
Published online: Feb 28, 2022
Published in print: May 1, 2022
Discussion open until: Jul 28, 2022
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