Editorial: Human factors and cognitive ergonomics in advanced industrial human-robot interactionShow others and affiliations
2025 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 12, article id 1564948Article in journal, Editorial material (Other academic) Published
Abstract [en]
Collaborative robotics is a very promising technology for many industrial processes, including e.g., manufacturing, logistics, or construction. This new technology are also changing the environment for workers in industry. Research on human-robot interaction (HRI) will be crucial for enhancing the operator’s work conditions and wellbeing, as well as production performance. In that regard, human factors, with a special emphasis on cognitive ergonomics are fundamental to implementing safe, fluent, and efficient collaborative applications.
This Research Topic gathers a range of contributions on the study of Human Factors and Cognitive ergonomics in user-centered and collaborative applications in industrial settings. Here, we summarize these studies from the perspective of three pivotal areas impacted by collaborative robotics: workers’ safety, performance, and wellbeing.
Place, publisher, year, edition, pages
Frontiers Media S.A., 2025. Vol. 12, article id 1564948
Keywords [en]
cognitive ergonomics, human factors, human-robot collaboration, human-robot interaction, industry 5.0
National Category
Production Engineering, Human Work Science and Ergonomics Robotics and automation
Research subject
Interaction Lab (ILAB); User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-24932DOI: 10.3389/frobt.2025.1564948ISI: 001444687800001PubMedID: 40093857Scopus ID: 2-s2.0-86000612579OAI: oai:DiVA.org:his-24932DiVA, id: diva2:1941354
Funder
AFA Insurance, 220226Vinnova, 2022-01279
Note
CC BY 4.0
Editorial on the Research Topic Human factors and cognitive ergonomics in advanced industrial human-robot interaction
Correspondence: Erik Billing, erik.billing@his.se
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Parts of this work has been financially supported by Swedish insurance agency AFA Försäkring (grant #220226) and the Swedish innovation agency Vinnova (grant #2022-01279).
2025-02-282025-02-282025-04-15Bibliographically approved