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Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments

Author

Listed:
  • You, Sangseok

    (HEC Paris)

  • Kim, Jeong-Hwan

    (University of Michigan at Ann Arbor - Department of Civil and Environmental Engineering)

  • Lee, SangHyun

    (University of Michigan at Ann Arbor - Department of Civil and Environmental Engineering)

  • Kamat, Vineet

    (University of Michigan at Ann Arbor - Department of Civil and Environmental Engineering)

  • Robert, Lionel

    (University of Michigan at Ann Arbor - School of Information)

Abstract

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

Suggested Citation

  • You, Sangseok & Kim, Jeong-Hwan & Lee, SangHyun & Kamat, Vineet & Robert, Lionel, 2018. "Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments," HEC Research Papers Series 1308, HEC Paris.
    • Handle: RePEc:ebg:heccah:1308
    Note: This article is a preprint. The final version is published in Automation in Construction, Volume 96, December 2018, Pages 161-170, https://doi.org/10.1016/j.autcon.2018.09.008
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    Cited by:

    1. Chen, Fangyu & Wang, Hongwei & Xu, Gangyan & Ji, Hongchang & Ding, Shanlei & Wei, Yongchang, 2020. "Data-driven safety enhancing strategies for risk networks in construction engineering," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. Vishnu Sivarudran Pillai & Kira Matus, 2019. "Regulation of AI Technologies in the Construction Industry," HKUST IEMS Working Paper Series 2019-65, HKUST Institute for Emerging Market Studies, revised May 2019.
    3. Webster, Craig & Ivanov, Stanislav, 2021. "Tourists’ perceptions of robots in passenger transport," Technology in Society, Elsevier, vol. 67(C).

    More about this item

    Keywords

    Human–Robot Work Collaboration (HRWC); Immersive Virtual; Environment (IVE); Robot Acceptance Safety Model (RASM); Masonry; Safety; Trust; Team; Identification; Intention to Work with Robot;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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