Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review
The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the oper...
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| Published in: | Frontiers in robotics and AI Vol. 9; p. 799522 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
Frontiers Media S.A
03.02.2022
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| Subjects: | |
| ISSN: | 2296-9144, 2296-9144 |
| Online Access: | Get full text |
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| Summary: | The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the operator chooses to interact with and utilize the robot. This interaction can affect system performance and safety in dynamic ways. The theory of human factors in human-automation interaction has long been studied; however, the formal investigation of these HFs in shared space human-robot collaboration (HRC) and the potential interactive effects between covariate HFs (HF-HF) and HF-RF in shared space collaborative robotics requires additional investigation. Furthermore, methodological applications to measure or manipulate these factors can provide insights into contextual effects and potential for improved measurement techniques. As such, a systematic literature review was performed to evaluate the most frequently addressed operator HF states in shared space HRC, the methods used to quantify these states, and the implications of the states on HRC. The three most frequently measured states are: trust, cognitive workload, and anxiety, with subjective questionnaires universally the most common method to quantify operator states, excluding fatigue where electromyography is more common. Furthermore, the majority of included studies evaluate the effect of manipulating RFs on HFs, but few explain the effect of the HFs on system attributes or performance. For those that provided this information, HFs have been shown to impact system efficiency and response time, collaborative performance and quality of work, and operator utilization strategy. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Reviewed by: Satoshi Endo, Technical University of Munich, Germany This article was submitted to Human-Robot Interaction, a section of the journal Frontiers in Robotics and AI Claudia Latella, Italian Institute of Technology (IIT), Italy Edited by: Fanny Ficuciello, University of Naples Federico II, Italy |
| ISSN: | 2296-9144 2296-9144 |
| DOI: | 10.3389/frobt.2022.799522 |