From smart factories to service applications, human-robot interaction is crucial to the development of collaborative settings in which humans and robots operate side by side. Since robots in shared spaces must take into consideration variables such as human motion unpredictability and potential workplace risks, effective risk management is essential. When building these systems, striking a balance between factors such as safety, ergonomics, and operational flexibility becomes crucial. Misalignment between human purpose and robot behaviors might result in accidents or increased injury risks, especially in environments with high physical demands. One of the key challenges in human-robot interaction is handling uncertainty.
The main aim of the current study is to introduce a conceptual framework for safety/risk analysis, including a hierarchy tree of the risk criteria and risks. Both human- and robot-related factors are considered. The multi-criteria decision-making procedure developed for autonomous vehicle systems is adapted for risk analysis in human-robot interaction. As a final result, the prioritized risk criteria and risks are identified. These results lay the foundation for reducing risks in the future.
1. Raffik, R., Vaishali, V., Balavedhaa, S., Jyothi Lakshmi, N. and Sathya, R. R. Industry 5.0: enhancing human-robot collaboration through collaborative robots – a review. In 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 16–17 June 2023. IEEE, 2023, 1–6.
https://doi.org/10.1109/ICAECA56562.2023.10201120
2. Baratta, A., Cimino, A., Gnoni, M. G. and Longo, F. Human robot collaboration in Industry 4.0: a literature review. Procedia Comput. Sci., 2023, 217, 1887–1895.
https://doi.org/10.1016/j.procs.2022.12.389
3. Zhang, W., Jia, X., Liu, J., Zhang, S. and Li, T. Dynamic risk assessment and active response strategy of human-robot collaboration based on fuzzy comprehensive evaluation. Robot. Comput. Integr. Manuf., 2024, 88, 102732.
https://doi.org/10.1016/j.rcim.2024.102732
4. Papetti, A., Ciccarelli, M., Scoccia, C. and Germani, M. A multi-criteria method to design the collaboration between humans and robots. Procedia CIRP, 2021, 104, 939–944.
https://doi.org/10.1016/j.procir.2021.11.158
5. Bhalaji, R. K. A., Bathrinath, S., Ponnambalam, S. G. and Saravanasankar, S. Analyze the factors influencing human-robot interaction using MCDM method. Mater. Today Proc., 2021, 39(P1), 100–104.
https://doi.org/10.1016/j.matpr.2020.06.316
6. Pikner, H., Sell, R., Majak, J. and Karjust, K. Safety system assessment case study of automated vehicle shuttle. Electronics, 2022, 11(7), 1162.
https://doi.org/10.3390/electronics11071162
7. Jahanmahin, R., Masoud, S., Rickli, J. and Djuric, A. Human-robot interactions in manufacturing: a survey of human behavior modeling. Robot. Comput. Integr. Manuf., 2022, 78, 102404.
https://doi.org/10.1016/j.rcim.2022.102404
8. Villani, V., Pini, F., Leali, F. and Secchi, C. Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics, 2018, 55, 248–266.
https://doi.org/10.1016/j.mechatronics.2018.02.009
9. Ajoudani, A., Zanchettin, A. M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K. and Khatib, O. Progress and prospects of the human–robot collaboration. Auton. Robots, 2018, 42(5), 957–975.
https://doi.org/10.1007/s10514-017-9677-2
10. Boyce, P. R. Human Factors in Lighting. 3rd ed. CRC Press, 2014.
https://doi.org/10.1201/b16707
11. Dul, J. and Neumann, W. P. Ergonomics contributions to company strategies. Appl. Ergon., 2009, 40(4), 745–752.
https://doi.org/10.1016/j.apergo.2008.07.001
12. Gekara, V. and Snell, D. Designing and delivering skills transferability and employment mobility: the challenges of a market-driven vocational education and training system. J. Voc. Educ. Train., 2018, 70(1), 107–129.
https://doi.org/10.1080/13636820.2017.1392996
13. Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J. and Parasuraman, R. A meta-analysis of factors affecting trust in human-robot interaction. Hum. Factors, 2011, 53(5), 517–527.
https://doi.org/10.1177/0018720811417254
14. Voigt, P. and von dem Bussche, A. The EU General Data Protection Regulation (GDPR). A Practical Guide. Springer, 2017.
https://doi.org/10.1007/978-3-319-57959-7
15. Floridi, L. and Taddeo, M. What is data ethics? Phil. Trans. R. Soc. A, 2016, 374, 20160360.
https://doi.org/10.1098/rsta.2016.0360
16. Di Pasquale, V., Farina, P., Fera, M., Gerbino, S., Miranda, S. and Rinaldi, M. Human robot-interaction: a conceptual framework for task performance analysis. IFAC PapersOnLine, 2024, 58(19), 79–84.
https://doi.org/10.1016/J.IFACOL.2024.09.096
17. Mehrparvar, M., Majak, J. and Karjust, K. A comparative analysis of Fuzzy AHP and Fuzzy VIKOR methods for prioritization of the risk criteria of an autonomous vehicle system. Proc. Estonian Acad. Sci., 2024, 73(2), 116–123.
https://doi.org/10.3176/proc.2024.2.04
18. Paat, A., Majak, J., Karu, V. and Hitch, M. Fuzzy analytical hierarchy process based environmental, social and governance risks assessment for the future phosphorite mining in Estonia. Extr. Ind. Soc., 2024, 17, 101438.
https://doi.org/10.1016/j.exis.2024.101438
19. Kaganski, S., Majak, J. and Karjust, K. Fuzzy AHP as a tool for prioritization of key performance indicators. Procedia CIRP, 2018, 72, 1227–1232.
https://doi.org/10.1016/j.procir.2018.0 3.097
20. Paavel, M., Karjust, K. and Majak, J. Development of a product lifecycle management model based on the fuzzy analytic hierarchy process. Proc. Estonian Acad. Sci., 2017, 66(3), 279–286.
https://doi.org/10.3176/proc.2017.3.05
21. Bassir, D., Lodge, H., Chang, H., Majak, J. and Chen, G. Application of artificial intelligence and machine learning for BIM: review. Int. J. Simul. Multidisci. Des. Optim., 2023, 14, 5.
https://doi.org/10.1051/smdo/2023005
22. EVS-EN ISO 45001:2023. Occupational health and safety management systems – Requirements with guidance for use.
23. ISO/TS 15066:2016. Robots and robotic devices – Collaborative robots.
