Hostname: page-component-7dd5485656-bvgqh Total loading time: 0 Render date: 2025-10-31T13:20:06.489Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards an ontology to capture human attributes in human-robot collaboration

Published online by Cambridge University Press:  16 May 2024

Stephanie Hall ,
Mandeep Dhanda  and
Vimal Dhokia
Stephanie Hall*
Affiliation:
University of Bath, United Kingdom
Mandeep Dhanda
Affiliation:
University of Bath, United Kingdom
Vimal Dhokia
Affiliation:
University of Bath, United Kingdom
*
sjh224@bath.ac.uk

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A core predicate of Industry 5.0 (I5.0) is the integration of human, environmental and social factors with new technologies. The integration of collaborative robots offers increased productivity but raises questions on safety and how robots can respond to varying cognitive and physical attributes. This paper discusses the significance of structured ontologies in managing complex information for proactive, safe, and productive human-robot collaboration. The paper highlights the future work to be undertaken to ensure the safe and fluid integration of humans and robots within I5.0.

Keywords

ontologyhuman-robot collaborationhuman behaviour

Information

Type
Systems Engineering and Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2585 - 2594
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Al-Yacoub, A., Buerkle, A., Flanagan, M., Ferreira, P., Hubbard, E.M. and Lohse, N. (2020), “Effective Human-Robot Collaboration through Wearable Sensors”, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Vol. 2020-Septe, pp. 651–658, https://dx.doi.org/10.1109/ETFA46521.2020.9212100.Google Scholar
Azevedo, H., Belo, J.P.R. and Romero, R.A.F. (2020), “Using Ontology as a Strategy for Modeling the Interface Between the Cognitive and Robotic Systems”, Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 99 No. 3–4, pp. 431–449, https://dx.doi.org/10.1007/s10846-019-01076-0.CrossRefGoogle Scholar
Bruno, G. (2015), “Semantic organization of product lifecycle information through a modular ontology”, International Journal of Circuits, Systems and Signal Processing, Vol. 9, pp. 16–26.Google Scholar
Buerkle, A., Matharu, H., Al-Yacoub, A., Lohse, N., Bamber, T. and Ferreira, P. (2022), “An adaptive human sensor framework for human–robot collaboration”, International Journal of Advanced Manufacturing Technology, Springer London, Vol. 119 No. 1–2, pp. 1233–1248, https://dx.doi.org/10.1007/s00170-021-08299-2.Google Scholar
Chiurco, A., Frangella, J., Longo, F., Nicoletti, L., Padovano, A., Solina, V., Mirabelli, G., et al. (2022), “Real-time Detection of Worker's Emotions for Advanced Human-Robot Interaction during Collaborative Tasks in Smart Factories”, Procedia Computer Science, Elsevier B.V., Vol. 200 No. 2019, pp. 1875–1884, https://dx.doi.org/10.1016/j.procs.2022.01.388.Google Scholar
David, J., Coatanéa, E. and Lobov, A. (2023), “Deploying OWL ontologies for semantic mediation of mixed-reality interactions for human–robot collaborative assembly”, Journal of Manufacturing Systems, Elsevier Ltd, Vol. 70 No. August, pp. 359–381, https://dx.doi.org/10.1016/j.jmsy.2023.07.013.Google Scholar
Dussard, B., Sarthou, G. and Clodic, A. (2023), “Ontological component-based description of robot capabilities”.Google Scholar
Fernandez, M., Gomez-Perez, A. and Juristo, N. (2007), “METHONTOLOGY: From Ontological Art Towards Ontological Engineering”, Proceedings - 3rd International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution, AXMEDIS 2007, pp. 115–122, https://dx.doi.org/10.1109/AXMEDIS.2007.19.Google Scholar
Fluently. (2022), “Fluently - The Essence of Human-Robot Interaction”, available at: https://www.fluently-horizonproject.eu/ (accessed 21 November 2023).Google Scholar
Gangemi, A. (2010), “Ontology:DOLCE+DnS Ultralite”, available at: http://ontologydesignpatterns.org/wiki/Ontology:DOLCE+DnS_Ultralite (accessed 30 March 2023).Google Scholar
Gayathri, R. and Uma, V. (2018), “Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey”, ICT Express, Elsevier B.V., Vol. 4 No. 2, pp. 69–74, https://dx.doi.org/10.1016/j.icte.2018.04.008.Google Scholar
Gervasi, R., Mastrogiacomo, L. and Franceschini, F. (2023), “An experimental focus on learning effect and interaction quality in human–robot collaboration”, Production Engineering, Springer Berlin Heidelberg, Vol. 17 No. 3–4, pp. 355–380, https://dx.doi.org/10.1007/s11740-023-01188-5.Google Scholar
Gil, R., Virgili-Gomá, J., García, R. and Mason, C. (2015), “Emotions ontology for collaborative modelling and learning of emotional responses”, Computers in Human Behavior, Elsevier Ltd, Vol. 51, pp. 610–617, https://dx.doi.org/10.1016/j.chb.2014.11.100.Google Scholar
Graterol, W., Diaz-Amado, J., Cardinale, Y., Dongo, I., Lopes-Silva, E. and Santos-Libarino, C. (2021), “Emotion detection for social robots based on nlp transformers and an emotion ontology”, Sensors (Switzerland), Vol. 21 No. 4, pp. 1–19, https://dx.doi.org/10.3390/s21041322.CrossRefGoogle Scholar
Gualtieri, L., Fraboni, F., De Marchi, M. and Rauch, E. (2022), “Development and evaluation of design guidelines for cognitive ergonomics in human-robot collaborative assembly systems”, Applied Ergonomics, Elsevier Ltd, Vol. 104 No. May, p. 103807, https://dx.doi.org/10.1016/j.apergo.2022.103807.Google Scholar
Gyrard, A. and Boudaoud, K. (2022), “Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health”, Applied Sciences (Switzerland), Vol. 12 No. 19, pp. 1–34, https://dx.doi.org/10.3390/app12199712.Google Scholar
Hart, S.G. and Staveland, L.E. (1988), “Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research”, pp. 139–183, https://dx.doi.org/10.1016/S0166-4115(08)62386-9.CrossRefGoogle Scholar
Lemaignan, S., Warnier, M., Sisbot, E.A. and Alami, R. (2014), “Human-Robot Interaction: Tackling the AI Challenges”, Artificial Intelligence.Google Scholar
Li, S., Zheng, P., Liu, S., Wang, Z., Wang, X.V., Zheng, L. and Wang, L. (2023), “Proactive human–robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives”, Robotics and Computer-Integrated Manufacturing, Elsevier Ltd, Vol. 81 No. December 2022, p. 102510, https://dx.doi.org/10.1016/j.rcim.2022.102510.Google Scholar
Malik, A.A. and Bilberg, A. (2019), “Complexity-based task allocation in human-robot collaborative assembly”, Industrial Robot, Vol. 46 No. 4, pp. 471–480, https://dx.doi.org/10.1108/IR-11-2018-0231.CrossRefGoogle Scholar
Martin-Guillerez, D., Guiochet, J., Powell, D. and Zanon, C. (2010), “A UML-based method for risk analysis of human-robot interactions”, Proceedings of the 2nd International Workshop on Software Engineering for Resilient Systems, SERENCE 2010, pp. 32–41, https://dx.doi.org/10.1145/2401736.2401740.CrossRefGoogle Scholar
Masolo, C., Borgo, S., Gangemi, A., Guarino, N. and Oltramari, A. (2003), “WonderWeb Deliverable D18”, Communities, Vol. 2003, p. 343.Google Scholar
Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L. and Sierra, B. (2017), “Human-robot collaboration in industrial applications: Safety, interaction and trust”, International Journal of Advanced Robotic Systems, Vol. 14 No. 4, pp. 1–10, https://dx.doi.org/10.1177/1729881417716010.CrossRefGoogle Scholar
Niles, I. and Pease, A. (2001), “Towards a standard upper ontology”, Formal Ontology in Information Systems: Collected Papers from the Second International Conference, pp. 2–9, https://dx.doi.org/10.1145/505168.505170.CrossRefGoogle Scholar
Olivares-Alarcos, A., Foix, S., Borgo, S. and Alenyà, Guillem. (2022), “OCRA – An ontology for collaborative robotics and adaptation”, Computers in Industry, Elsevier, Vol. 138, p. 103627, https://dx.doi.org/10.1016/j.compind.2022.103627.Google Scholar
Perugini, M. and Conner, M. (2000), “Predicting and understanding behavioral volitions: The interplay between goals and behaviors”, European Journal of Social Psychology, Vol. 30 No. 5, pp. 705–731, https://doi.org/10.1002/1099-0992(200009/10)30:5<705::AID-EJSP18>3.0.CO;2-%23.3.0.CO;2-#>CrossRefGoogle Scholar
Prestes, E., Carbonera, J.L., Rama Fiorini, S., Vitor, V.A., Abel, M., Madhavan, R., Locoro, A., et al. (2013), “Towards a core ontology for robotics and automation”, Robotics and Autonomous Systems, Elsevier B.V., Vol. 61 No. 11, pp. 1193–1204, https://dx.doi.org/10.1016/j.robot.2013.04.005.Google Scholar
Skaramagkas, V., Ktistakis, E., Manousos, D., Tachos, N.S., Kazantzaki, E., Tripoliti, E.E., Fotiadis, D.I., et al. (2021), “Cognitive workload level estimation based on eye tracking: A machine learning approach”, 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE), IEEE, pp. 1–5, https://dx.doi.org/10.1109/BIBE52308.2021.9635166.CrossRefGoogle Scholar
Tenorth, M. and Beetz, M. (2013), “KnowRob: A knowledge processing infrastructure for cognition-enabled robots”, International Journal of Robotics Research, Vol. 32 No. 5, pp. 566–590, https://dx.doi.org/10.1177/0278364913481635.CrossRefGoogle Scholar
Trauer, J., Schweigert-Recksiek, S., Schenk, T., Baudisch, T., Mörtl, M. and Zimmermann, M. (2022), “A Digital Twin Trust Framework for Industrial Application”, Proceedings of the Design Society, Vol. 2, pp. 293–302, https://dx.doi.org/10.1017/pds.2022.31.CrossRefGoogle Scholar
Umbrico, A., Orlandini, A. and Cesta, A. (2020), “An ontology for human-robot collaboration”, Procedia CIRP, Elsevier B.V., Vol. 93, pp. 1097–1102, https://dx.doi.org/10.1016/j.procir.2020.04.045.Google Scholar
Umbrico, A., Orlandini, A., Cesta, A., Faroni, M., Beschi, M., Pedrocchi, N., Scala, A., et al. (2022), “Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations”, Applied Sciences (Switzerland), Vol. 12 No. 14, https://dx.doi.org/10.3390/app12146839.Google Scholar
Vanneste, P., Raes, A., Morton, J., Bombeke, K., Van Acker, B.B., Larmuseau, C., Depaepe, F., et al. (2021), “Towards measuring cognitive load through multimodal physiological data”, Cognition, Technology & Work, Vol. 23 No. 3, pp. 567–585, https://dx.doi.org/10.1007/s10111-020-00641-0.CrossRefGoogle Scholar
Vogel-Heuser, B. and Hess, D. (2016), “Guest Editorial Industry 4.0-Prerequisites and Visions”, IEEE Transactions on Automation Science and Engineering, Vol. 13 No. 2, pp. 411–413, https://dx.doi.org/10.1109/TASE.2016.2523639.CrossRefGoogle Scholar
W3C OWL, Working Group. (2012), “OWL 2 web ontology language document overview”, http://www.W3.Org/TR/Owl2-Overview/, World Wide Web Consortium (W3C).Google Scholar
Winkel, J. and Mathiassen, S.E. (1994), “Assessment of physical work load in epidemiologic studies: concepts, issues and operational considerations”, Ergonomics, Vol. 37 No. 6, pp. 979–988, https://dx.doi.org/10.1080/00140139408963711.CrossRefGoogle ScholarPubMed
Xu, X., Lu, Y., Vogel-Heuser, B. and Wang, L. (2021), “Industry 4.0 and Industry 5.0—Inception, conception and perception”, Journal of Manufacturing Systems, Elsevier Ltd, Vol. 61 No. September, pp. 530–535, https://dx.doi.org/10.1016/j.jmsy.2021.10.006.Google Scholar
Zhao, Y., Li, D., Chen, X. and Qie, W. (2010), “Astronaut physical load research and applications”, 2010 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, pp. 2453–2459, https://dx.doi.org/10.1109/IEEM.2010.5674361.CrossRefGoogle Scholar