Stakeholder Perspectives on Safety Issues in Collaborative Mobile Robots: A Case Study of Quadruped Robot Applications in a Smart Factory
Source abstract
Abstract
With the development of Industry 4.0, collaborative mobile robots are becoming increasingly prevalent in industrial settings, raising important safety considerations in human–robot interaction environments. This study examines the safety issues in collaborative mobile robotics through a case study of a smart factory utilizing quadruped robots. This research aims to contribute to the development of safety management strategies by identifying potential risk factors and analyzing the differences in risk perception among stakeholders. A survey was conducted among 93 operators in the factory to identify the main risk factors, followed by a Delphi study with four groups of experts: robot operators, safety management experts, robot developers, and academic experts. The Kruskal–Wallis and Mann–Whitney U tests were used to analyze the statistical significance of differences in perception between the groups. The results showed that collision and deviation from the path were the most concerning risk factors. Significant differences were found in the perceptions of several hazards between expert groups, with academic experts rating most hazards highly while robot developers rated them relatively low. The findings highlight how background knowledge and experience influence risk perception in collaborative robotics. These varying perspectives should be considered when developing safety management strategies for mobile robots in industrial settings, suggesting the importance of multi-stakeholder collaboration and targeted educational programs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · AI in Service Interactions · Persona Design and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, Jung-gu, Seoul 04620, Republic of Korea
Department of Police Administration, Dongguk University, Jung-gu, Seoul 04620, Republic of Korea
Development of SMART Community Policing System (Googi) Research Center, Dongguk University, Jung-gu, Seoul 04620, Republic of Korea
SK Hynix Corporation, Icheon 17336, Republic of Korea
Funding
National Research Foundation of Korea (NRF)
References (31)
Show source-provided references
1. Hermann, M., Pentek, T., and Otto, B. (2016, January 5–8). Design Principles for Industrie 4.0 Scenarios. Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA.10.1109/HICSS.2016.488 ↗
2. Substantial capabilities of robotics in enhancing industry 4.0 implementation10.1016/j.cogr.2021.06.001 ↗
3. Valori, M., Scibilia, A., Fassi, I., Saenz, J., Behrens, R., Herbster, S., Bidard, C., Lucet, E., Magisson, A., and Schaake, L. (2021). Validating Safety in Human–Robot Collaboration: Standards and New Perspectives. Robotics, 10.10.3390/robotics10020065 ↗
4. Significant applications of Cobots in the field of manufacturing10.1016/j.cogr.2022.10.001 ↗
5. Hanna, A., Bengtsson, K., Götvall, P.L., and Ekström, M. (2020, January 8–11). Towards safe human robot collaboration—Risk assessment of intelligent automation. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.10.1109/ETFA46521.2020.9212127 ↗
6. Fast and Safe Trajectory Planning: Solving the Cobot Performance/Safety Trade-Off in Human-Robot Shared Environments10.1109/LRA.2021.3076968 ↗
7. (2011). Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 2: Robot Systems and Integration (Standard No. ISO 10218-2:2011).
8. (2016). Robots and Robotic Devices—Collaborative Robots (Standard No. ISO/TS 15066:2016).
9. Berx, N., Adriaensen, A., Decré, W., and Pintelon, L. (2022). Assessing System-Wide Safety Readiness for Successful Human–Robot Collaboration Adoption. Safety, 8.10.3390/safety8030048 ↗
10. Identification and classification of risk factors for human-robot collaboration from a system-wide perspective10.1016/j.cie.2021.107827 ↗
11. Chiriatti, G., Palmieri, G., Scoccia, C., Palpacelli, M.C., and Callegari, M. (2021). Adaptive Obstacle Avoidance for a Class of Collaborative Robots. Machines, 9.10.3390/machines9060113 ↗
12. Patalas-Maliszewska, J., Dudek, A., Pajak, G., and Pajak, I. (2024). Working Toward Solving Safety Issues in Human–Robot Collaboration: A Case Study for Recognising Collisions Using Machine Learning Algorithms. Electronics, 13.10.3390/electronics13040731 ↗
13. Leveraging autonomous mobile robots for Industry 4.0 warehouses: A multiple case study analysis10.1108/IJLM-09-2022-0362 ↗
14. Desai, N., and Punnekkat, S. (2019, January 1–3). Safety-oriented flexible design of Autonomous Mobile Robot systems. Proceedings of the 2019 International Symposium on Systems Engineering (ISSE), Edinburgh, UK.10.1109/ISSE46696.2019.8984481 ↗
15. A Review of Quadruped Robots: Structure, Control, and Autonomous Motion10.1002/aisy.202300783 ↗
16. He, J., Shao, J., Sun, G., and Shao, X. (2019). Survey of Quadruped Robots Coping Strategies in Complex Situations. Electronics, 8.10.3390/electronics8121414 ↗
17. Majithia, A., Shah, D., Dave, J., Kumar, A., Rathee, S., Dogra, N., HM, V., Chiniwar, D.S., and Hiremath, S. (2024). Design, motions, capabilities and applications of quadruped robots: A comprehensive review. Front. Mech. Eng., 10.10.3389/fmech.2024.1448681 ↗
18. Go, E., Jeon, H.-C., Lee, J.-S., and Lim, J.-Y. (2024). Enhancing Urban Public Safety Through UAS Integration: A Comprehensive Hazard Analysis with the STAMP/STPA Framework. Appl. Sci., 14.10.3390/app14114609 ↗
19. Occupational health and safety issues in human-robot collaboration: State of the art and open challenges10.1016/j.ssci.2023.106313 ↗
20. A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction10.1007/s12369-023-01027-8 ↗
21. Arents, J., Abolins, V., Judvaitis, J., Vismanis, O., Oraby, A., and Ozols, K. (2021). Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review. J. Sens. Actuator Netw., 10.10.3390/jsan10030048 ↗
22. Use of Delphi in health sciences research: A narrative review10.1097/MD.0000000000032829 ↗
23. The Delphi method as a research tool: An example, design considerations and applications10.1016/j.im.2003.11.002 ↗
24. Linstone, H.A., and Turoff, M. (1975). The Delphi Method, Addison-Wesley.
25. Chai, H., Li, Y., Song, R., Zhang, G., Zhang, Q., Liu, S., Hou, J., Xin, Y., Yuan, M., and Zhang, G. (2022). A survey of the development of quadruped robots: Joint configuration, dynamic locomotion control method and mobile manipulation approach. Biomim. Intell. Robot., 2.10.1016/j.birob.2021.100029 ↗
26. Biases in future-oriented Delphi studies: A cognitive perspective10.1016/j.techfore.2016.01.021 ↗
27. McKight, P.E., and Najab, J. (2010). Kruskal-Wallis Test. The Corsini Encyclopedia of Psychology, John Wiley & Sons.10.1002/9780470479216.corpsy0491 ↗
28. Power study of anova versus Kruskal-Wallis test
29. The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution10.20982/tqmp.04.1.p013 ↗
30. Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences, Routledge.10.4324/9780203771587 ↗
31. When to use the B onferroni correction10.1111/opo.12131 ↗