journal article·2022-05-10Open access

Digital Twin for Human–Robot Collaboration in Manufacturing: Review and Outlook

Aswin K. Ramasubramanian, Robins Mathew, Matthew Kelly, Vincent Hargaden, Nikolaos Papakostas

Applied Sciences· Volume 12· Issue 10

Abstract

Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems · Manufacturing Process and Optimization

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

Affiliations

Laboratory for Advanced Manufacturing Simulation & Robotics (LAMS), School of Mechanical & Materials Engineering, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland

Funding

European Commission

References (100+)

Show source-provided references
1. Digital Twin in Manufacturing: A Categorical Literature Review and Classification10.1016/j.ifacol.2018.08.474 ↗
2. Industry 4.0, Digitization, and Opportunities for Sustainability10.1016/j.jclepro.2019.119869 ↗
3. Industry 4.0—A Glimpse10.1016/j.promfg.2018.02.034 ↗
4. Simulation in the Design and Operation of Manufacturing Systems: State of the Art and New Trends10.1080/00207543.2019.1636321 ↗
5. Smart Factory for Industry 4.0: A Review
6. A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems10.1016/j.mfglet.2014.12.001 ↗
7. Implementing Smart Factory of Industrie 4.0: An Outlook
8. (2018). Executive Summary World Robotics 2018 Industrial Robots, International Federation of Robotics IFR.
9. Effects of Robot Motion on Human-Robot Collaboration
10. Sadrfaridpour, B., Saeidi, H., Burke, J., Madathil, K., and Wang, Y. (2016). Modeling and Control of Trust in Human-Robot Collaborative Manufacturing. Robust Intelligence and Trust in Autonomous Systems, Springer.10.1007/978-1-4899-7668-0_7 ↗
11. Bragança, S., Costa, E., Castellucci, I., and Arezes, P.M. (2019). A Brief Overview of the Use of Collaborative Robots in Industry 4.0: Human Role and Safety. Occupational and Environmental Safety and Health, Springer.10.1007/978-3-030-14730-3_68 ↗
12. Autonomy in Surgical Robotics10.1146/annurev-control-062420-090543 ↗
13. A Review of the Roles of Digital Twin in CPS-Based Production Systems10.1016/j.promfg.2017.07.198 ↗
14. Digital Twin in Industry: State-of-the-Art10.1109/TII.2018.2873186 ↗
15. Kvasnica, M., and Fikar, M. (2017, January 6–9). The Digital Twin of an Industrial Production Line within the Industry 4.0 Concept. Proceedings of the 2017 21st International Conference on Process Control, PC, Strbske Pleso, Slovakia.
16. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems
17. Holler, M., Uebernickel, F., and Brenner, W. (2016, January 10–12). Digital Twin Concepts in Manufacturing Industries—A Literature Review and Avenues for Further Research. Proceedings of the 18th International Conference on Industrial Engineering (IJIE), Seoul, Korea.
18. (2022, March 31). Digital Twin Market Size, Share & Trends Analysis Report by End Use. Research and Markets. Available online: https://www.researchandmarkets.com/reports/5415584/digital-twin-market-size-share-and-trends.
19. Digital Twin-Driven Product Design, Manufacturing and Service with Big Data10.1007/s00170-017-0233-1 ↗
20. Savur, C., Kumar, S., Arora, S., Hazbar, T., and Sahin, F. (2019, January 19–22). HRC-SoS: Human Robot Collaboration Experimentation Platform as System of Systems. Proceedings of the 2019 14th Annual Conference System of Systems Engineering (SoSE), Anchorage, AK, USA.10.1109/SYSOSE.2019.8753881 ↗
21. Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing10.1109/ACCESS.2017.2756069 ↗
22. Digital Twins of Human Robot Collaboration in a Production Setting10.1016/j.promfg.2018.10.047 ↗
23. Boschert, S., and Rosen, R. (2016). Digital Twin-the Simulation Aspect. Mechatron. Futures: Challenges and Solutions for Mechatron. Systems and Their Designers, Springer International Publishing.10.1007/978-3-319-32156-1_5 ↗
24. Digital Manufacturing: History, Perspectives, and Outlook10.1243/09544054JEM1241 ↗
25. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models10.1109/JPROC.2020.2998530 ↗
26. Digital Manufacturing: Requirements and Challenges for Implementing Digital Surrogates
27. Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison10.1016/j.eng.2019.01.014 ↗
28. About the Importance of Autonomy and Digital Twins for the Future of Manufacturing10.1016/j.ifacol.2015.06.141 ↗
29. Digital Twin-Driven Product Design Framework10.1080/00207543.2018.1443229 ↗
30. Matheson, E., Minto, R., Zampieri, E.G.G., Faccio, M., and Rosati, G. (2019). Human–Robot Collaboration in Manufacturing Applications: A Review. Robotics, 8.10.3390/robotics8040100 ↗
31. Cobots for the Automobile Assembly Line10.1109/ROBOT.1999.770061 ↗
32. Dual Arm Robot in Cooperation with Humans for Flexible Assembly10.1016/j.cirp.2017.04.097 ↗
33. An Outlook on Future Assembly Systems Introducing Robotic Mobile Dual Arm Workers10.1016/j.procir.2018.03.130 ↗
34. Human-Robot Collaboration in Industrial Applications: Safety, Interaction and Trust10.1177/1729881417716010 ↗
35. Human–Robot Collaborative Assembly in Cyber-Physical Production: Classification Framework and Implementation10.1016/j.cirp.2017.04.101 ↗
36. A Conceptual Framework to Evaluate Human-Robot Collaboration10.1007/s00170-020-05363-1 ↗
37. Wang, L., Liu, S., Liu, H., and Wang, X.V. (2020, January 1–4). Overview of Human-Robot Collaboration in Manufacturing. Proceedings of the 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing, Belgrade, Serbia. ISSN 21954356.
38. Xiao, J., Wang, P., Lu, H., and Zhang, H. (2020). A Three-Dimensional Mapping and Virtual Reality-Based Human–Robot Interaction for Collaborative Space Exploration. Int. J. Adv. Robot. Syst., 17.10.1177/1729881420925293 ↗
39. Human–Robot Interaction Review and Challenges on Task Planning and Programming10.1080/0951192X.2015.1130251 ↗
40. Human-Robot Interaction: A Survey10.1561/1100000005 ↗
41. Collaborative Manufacturing with Physical Human-Robot Interaction10.1016/j.rcim.2015.12.007 ↗
42. Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design10.5772/5664 ↗
43. Survey on Human–Robot Collaboration in Industrial Settings: Safety, Intuitive Interfaces and Applications10.1016/j.mechatronics.2018.02.009 ↗
44. Human Somatosensory Processing and Artificial Somatosensation10.34133/2021/9843259 ↗
45. On Using Human Activity Recognition Sensors to Improve the Performance of Collaborative Mobile Manipulators: Review and Outlook10.1016/j.procir.2020.05.227 ↗
46. Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy10.34133/2021/9821787 ↗
47. Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review10.34133/2021/9863761 ↗
48. Digital Twin Driven Human–Robot Collaborative Assembly10.1016/j.cirp.2019.04.011 ↗
49. Shaping the Digital Twin for Design and Production Engineering10.1016/j.cirp.2017.04.040 ↗
50. (2022, March 31). What Is a Digital Twin?|IBM. Available online: https://www.ibm.com/topics/what-is-a-digital-twin.
51. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., and Wang, L. (2022, March 31). Modeling, Simulation, Information Technology & Processing Roadmap, Available online: https://www.nasa.gov/sites/default/files/501321main_TA11-ID_rev4_NRC-wTASR.pdf.
52. Past, Present, and Future Research of Digital Twin for Smart Manufacturing
53. An Application Framework of Digital Twin and Its Case Study10.1007/s12652-018-0911-3 ↗
54. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications10.1109/ACCESS.2019.2953499 ↗
55. Schirrmeister, F. (2019). System Emulation and Digital Twins in Aerospace Applications, Cadence Design Systems, Inc.
56. Grieves, M., and Vickers, J. (2016). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Springer International Publishing.10.1007/978-3-319-38756-7_4 ↗
57. Giese, H., Kounev, S., and Liu, J. (2016, January 17–22). A Simulation-Based Architecture for Smart Cyber-Physical Systems. Proceedings of the 2016 IEEE International Conference on Autonomic Computing (ICAC), Wuerzburg, Germany.
58. Boschert, S., Heinrich, C., and Rosen, R. (2018, January 7–11). Next Generation Digital Twin. Proceedings of the TMCE, Las Palmas de Gran Canaria, Spain.
59. Untangling the Digital Thread: The Challenge and Promise of Model-Based Engineering in Defense Acquisition10.1002/inst.12022 ↗
60. Engineering Design with Digital Thread10.2514/1.J057255 ↗
61. Survey on the Virtual Commissioning of Manufacturing Systems
62. Virtual Commissioning with Process Simulation (Tecnomatix)10.1080/16864360.2014.914400 ↗
63. Virtual Commissioning–Scientific Review and Exploratory Use Cases in Advanced Production Systems10.1016/j.procir.2019.03.278 ↗
64. Wincc-Based Process Simulation for Virtual Commissioning of Scada System Integration
65. Integrating the Digital Twin of the Manufacturing System into a Decision Support System for Improving the Order Management Process10.1016/j.procir.2018.03.192 ↗
66. Vermaak, H., and Niemann, J. (2017, January 24–26). Virtual Commissioning: A Tool to Ensure Effective System Integration. Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), Donostia, Spain.10.1109/ECMSM.2017.7945899 ↗
67. Virtual Commissioning of 6 DoF Pose Estimation and Robotic Bin Picking Systems for Industrial Parts10.1016/j.ifacol.2019.10.040 ↗
68. Schluse, M., and Rossmann, J. (2016, January 3–5). From Simulation to Experimentable Digital Twins: Simulation-Based Development and Operation of Complex Technical Systems. Proceedings of the 2016 IEEE International Symposium on Systems Engineering (ISSE), Edinburgh, UK.10.1109/SysEng.2016.7753162 ↗
69. Pérez, L., Rodríguez-Jiménez, S., Rodríguez, N., Usamentiaga, R., and García, D.F. (2020). Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning. Appl. Sci., 10.10.3390/app10103633 ↗
70. Virtual Reality in Manufacturing: Immersive and Collaborative Artificial-Reality in Design of Human-Robot Workspace10.1080/0951192X.2019.1690685 ↗
71. Integrating Digital Manufacturing and Simulation Tools in the Assembly Design Process: A Cooperating Robots Cell Case10.1016/j.cirpj.2011.06.016 ↗
72. Microservice-Based Architecture for Engineering Tools Enabling a Collaborative Multi-User Configuration of Robot-Based Automation Solutions10.1016/j.procir.2020.01.017 ↗
73. A Case Study of Digital Twin for a Manufacturing Process Involving Human Interactions
74. Simulation-Based Digital Twin of a Complex Shop-Floor Logistics System
75. Rolo, G.R., Rocha, A.D., Tripa, J., and Barata, J. (2021). Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance. Appl. Sci., 11.10.3390/app11052202 ↗
76. Martínez, G.S., Sierla, S., Karhela, T., and Vyatkin, V. (2018, January 21–23). Automatic Generation of a Simulation-Based Digital Twin of an Industrial Process Plant. Proceedings of the Proceedings: IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA.10.1109/IECON.2018.8591464 ↗
77. A Multi-Model Approach for Simulation-Based Digital Twin in Resilient Services10.37394/23203.2021.16.10 ↗
78. Flumerfelt, S., Alves, A., Calvo-Amodio, J., Hoyle, C., and Kahlen, F.-J. (2016). Managing Systems Complexity through Congruence. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Springer International Publishing.10.1007/978-3-319-38756-7_5 ↗
79. Smart Manufacturing Based on Digital Twin Technologies
80. Zheng, X., Lu, J., and Kiritsis, D. (2021). The Emergence of Cognitive Digital Twin: Vision, Challenges and Opportunities. Int. J. Prod. Res.10.1080/00207543.2021.2014591 ↗
81. Digital Twin-Driven Smart Manufacturing: Connotation, Reference Model, Applications and Research Issues10.1016/j.rcim.2019.101837 ↗
82. Xu, P., Csiszar, A., and Verl, A. (2018, January 20–22). Digital Twins of Manufacturing Systems as a Base for Machine Learning. Proceedings of the 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Stuttgart, Germany.
83. Zhang, Z., Lu, J., Xia, L., Wang, S., Zhang, H., and Zhao, R. (2020, January 12–14). Digital Twin System Design for Dual-Manipulator Cooperation Unit. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.10.1109/ITNEC48623.2020.9084652 ↗
84. A Data-And Knowledge-Driven Framework for Digital Twin Manufacturing Cell10.1016/j.procir.2019.04.084 ↗
85. A Reconfigurable Modeling Approach for Digital Twin-Based Manufacturing System10.1016/j.procir.2019.03.141 ↗
86. Digital Twin-Enabled Reconfigurable Modeling for Smart Manufacturing Systems
87. Development and Operation of Digital Twins for Technical Systems and Services10.1016/j.cirp.2019.04.024 ↗
88. The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.010.1016/j.procir.2016.11.152 ↗
89. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems10.1016/j.promfg.2017.04.043 ↗
90. A Six-Layer Digital Twin Architecture for a Manufacturing Cell
91. Azangoo, M., Taherkordi, A., Blech, J.O., and Vyatkin, V. (2021, January 20–23). Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments. Proceedings of the IEEE International Symposium on Industrial Electronics, Kyoto, Japan.10.1109/ISIE45552.2021.9576361 ↗
92. Latifah, A., Supangkat, S.H., Ramelan, A., Rahman, F.R., and Afandy, M. (2021, January 2–4). A Workspace Design Prediction: Concept Overview Using the Digital Twin. Proceedings of the 8th International Conference on ICT for Smart Society: Digital Twin for Smart Society, ICISS 2021-Proceeding, Bandung, Indonesia.10.1109/ICISS53185.2021.9533221 ↗
93. Zhang, H., Zhang, G., and Yan, Q. (2018, January 27–29). Dynamic Resource Allocation Optimization for Digital Twin-Driven Smart Shopfloor. Proceedings of the ICNSC 2018-15th IEEE International Conference on Networking, Sensing and Control, Zhuhai, China.10.1109/ICNSC.2018.8361283 ↗
94. Abburu, S., Berre, A.J., Jacoby, M., Roman, D., Stojanovic, L., and Stojanovic, N. (2020, January 15–17). COGNITWIN-Hybrid and Cognitive Digital Twins for the Process Industry. Proceedings of the Proceedings-2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020, Cardiff, UK.10.1109/ICE/ITMC49519.2020.9198403 ↗
95. Future Modeling and Simulation of CPS-Based Factories: An Example from the Automotive Industry10.1016/j.ifacol.2016.12.168 ↗
96. Digital Twin: Applying Emulation for Machine Reconditioning10.1016/j.procir.2018.03.139 ↗
97. Transformation of Robotic Workcells to Digital Twins10.1016/j.cirp.2020.03.003 ↗
98. Digital Twin for Adaptation of Robots’ Behavior in Flexible Robotic Assembly Lines
99. Sensor Data and Information Fusion to Construct Digital-Twins Virtual Machine Tools for Cyber-Physical Manufacturing10.1016/j.promfg.2017.07.094 ↗
100. The Digital Twin Implementation for Linking the Virtual Representation of Human-Based Production Tasks to Their Physical Counterpart in the Factory-Floor10.1080/0951192X.2018.1529430 ↗