Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments
Sangseok You,
Jeong-Hwan Kim,
Sanghyun Lee,
Vineet Kamat and
Lionel Robert
Additional contact information
Jeong-Hwan Kim: OIST - Okinawa Institute of Science and Technology Graduate University
Sanghyun Lee: ICES - Institute for Computational Engineering and Sciences [Austin] - University of Texas at Austin [Austin]
Working Papers from HAL
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.
Keywords: Safety; Trust; Team; Identification; Intention to Work with Robot; Human–Robot Work Collaboration (HRWC); Immersive Virtual; Environment (IVE); Robot Acceptance Safety Model (RASM); Masonry (search for similar items in EconPapers)
Date: 2018-09-12
References: Add references at CitEc
Citations: View citations in EconPapers (2)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-02895952
DOI: 10.2139/ssrn.3260634
Access Statistics for this paper
More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().