journal article·2021-06-19Open access

Seamless Human–Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices

Nikos Dimitropoulos, Theodoros Togias, Natalia Zacharaki, George Michalos, Sotiris Makris

Applied Sciences· Volume 11· Issue 12

Abstract

Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator’s needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Manufacturing and Logistics Optimization · Elevator Systems and Control · Assembly Line Balancing Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece

Funding

European Commission

References (31)

Show source-provided references
1. Papakostas, N., Constantinescu, C., and Mourtzis, D. (2020). Novel Industry 4.0 Technologies and Applications. Appl. Sci., 10.10.3390/app10186498 ↗
2. Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation10.1016/j.cirp.2017.04.101 ↗
3. Matheson, E., Minto, R., Zampieri, E.G., Faccio, M., and Rosati, G. (2019). Human–robot collaboration in manufacturing applications: A review. Robotics, 8.10.3390/robotics8040100 ↗
4. An outlook on future hybrid assembly systems-the Sherlock approach10.1016/j.procir.2020.08.004 ↗
5. Chryssolouris, G. (2006). Manufacturing Systems: Theory and Practice, Springer. [2nd ed.].
6. Fischetti, C., Bhatter, P., Frisch, E., Sidhu, A., Helmy, M., Lungren, M., and Duhaime, E. (2021). The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education. Acad. Radiol.10.1016/j.acra.2021.03.023 ↗
7. Haleem, A.; Artificial Intelligence (AI) applications for COVID-19 pandemic10.1016/j.dsx.2020.04.012 ↗
8. Systematic review of research on artificial intelligence applications in higher education–where are the educators?
9. Abduljabbar, R., Dia, H., Liyanage, S., and Bagloee, S.A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11.10.3390/su11010189 ↗
10. Artificial intelligence in engineering10.1016/S0890-6955(98)00076-5 ↗
11. An approach for task and action planning in Human–Robot Collaborative cells using AI10.1016/j.procir.2020.08.006 ↗
12. Operator support in human–robot collaborative environments using AI enhanced wearable devices10.1016/j.procir.2020.07.006 ↗
13. Cooperation of human and machines in assembly lines10.1016/j.cirp.2009.09.009 ↗
14. Dual arm robot in cooperation with humans for flexible assembly10.1016/j.cirp.2017.04.097 ↗
15. Progress and challenges in fabrication of wearable sensors for health monitoring10.1016/j.sna.2020.112105 ↗
16. Digital twin driven human–robot collaborative assembly10.1016/j.cirp.2019.04.011 ↗
17. Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study
18. An approach for monitoring the execution of human based assembly operations using machine learning10.1016/j.procir.2020.01.040 ↗
19. Digitisation of a moving assembly operation using multiple depth imaging sensors10.1007/s00170-015-7883-7 ↗
20. Recurrent neural network for motion trajectory prediction in human robot collaborative assembly10.1016/j.cirp.2020.04.077 ↗
21. Motion planning and scheduling for human and industrial-robot collaboration10.1016/j.cirp.2017.04.095 ↗
22. Workplace analysis and design using virtual reality techniques10.1016/j.cirp.2018.04.120 ↗
23. Augmented reality system for operator support in human–robot collaborative assembly10.1016/j.cirp.2016.04.038 ↗
24. Gai, S.N., Sun, R., Chen, S.J., and Ji, S. (2019, January 24–27). 6-DOF Robotic Obstacle Avoidance Path Planning Based on Artificial Potential Field Method. Proceedings of the 2019 16th International Conference on Ubiquitous Robots (UR), Jeju, Korea.10.1109/URAI.2019.8768792 ↗
25. Real-time order dispatching for a fleet of autonomous mobile robots using multi-agent reinforcement learning10.1016/j.cirp.2020.04.001 ↗
26. (2021, May 28). MoveIt. Available online: https://moveit.ros.org/.
27. (2021, May 28). Microsoft HoloLens. Available online: https://www.microsoft.com/en-us/hololens.
28. (2021, May 28). Mixed Reality Companion Kit library. Available online: https://github.com/microsoft/MixedRealityCompanionKit/tree/master/MixedRemoteViewCompositor/Samples/LowLatencyMRC.
29. (2021, May 28). Kinect Azure. Available online: https://azure.microsoft.com/en-us/services/kinect-dk/.
30. A Decision Making Framework for Human Robot Collaborative Workplace Generation10.1016/j.procir.2016.02.103 ↗
31. (2021, May 28). COMAU AURA. Available online: https://www.comau.com/en/our-competences/robotics/automation-products/collaborativerobotsaura.