Emotional Experience in Human–Robot Collaboration: Suitability of Virtual Reality Scenarios to Study Interactions beyond Safety Restrictions

Author:

Legler Franziska1,Trezl Jonas1,Langer Dorothea1ORCID,Bernhagen Max1,Dettmann Andre1ORCID,Bullinger Angelika C.1

Affiliation:

1. Chair of Ergonomics and Innovation, Department of Mechanical Engineering, Chemnitz University of Technology, 09111 Chemnitz, Germany

Abstract

Today’s research on fenceless human–robot collaboration (HRC) is challenged by a relatively slow development of safety features. Simultaneously, design recommendations for HRC are requested by the industry. To simulate HRC scenarios in advance, virtual reality (VR) technology can be utilized and ensure safety. VR also allows researchers to study the effects of safety-restricted features like close distance during movements and events of robotic malfunctions. In this paper, we present a VR experiment with 40 participants collaborating with a heavy-load robot and compare the results to a similar real-world experiment to study transferability and validity. The participant’s proximity to the robot, interaction level, and occurring system failures were varied. State anxiety, trust, and intention to use were used as dependent variables, and valence and arousal values were assessed over time. Overall, state anxiety was low and trust and intention to use were high. Only simulated failures significantly increased state anxiety, reduced trust, and resulted in reduced valence and increased arousal. In comparison with the real-world experiment, non-significant differences in all dependent variables and similar progression of valence and arousal were found during scenarios without system failures. Therefore, the suitability of applying VR in HRC research to study safety-restricted features can be supported; however, further research should examine transferability for high-intensity emotional experiences.

Funder

German Federal Ministry of Education and Research

Deutsche Forschungsgemeinschaft

Publisher

MDPI AG

Subject

Artificial Intelligence,Control and Optimization,Mechanical Engineering

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