Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell
Abstract
:1. Introduction
1.1. Related Work
1.2. Game Engines for DT
2. Methodology
2.1. Setup
2.1.1. Physical Layer
2.1.2. Digital Layer
2.1.3. Communication Layer
2.2. Creation of DT
- Robot Controller: This manages the physical movements of the robotic arm, ensuring precise execution of tasks. It interacts with the actuators and sensors to implement control algorithms developed in MoveIt 1 - Noetic.
- ROS Publisher/Subscriber: The ROS Publisher broadcasts critical data such as status updates, sensor readings, and commands. The ROS Subscriber listens for incoming messages, including simulation results and new instructions for the DT, maintaining a continuous feedback loop.
- TCP Server: The Transmission Control Protocol (TCP) Server facilitates a Transmission Control Protocol/Internet Protocol (TCP/IP)-based communication protocol for the exchange of information between ROS and Unity. This server ensures reliable and efficient data transfer, minimizing latency and maximizing synchronization accuracy.
- Unity’s Articulation Body: This acts as the digital counterpart of the physical robot, where the robot’s behaviours and potential movements are simulated with high fidelity by subscribing to the ROS messages. It leverages URDF to accurately replicate the robot’s physical properties and motion dynamics within the simulation environment. The articulation body ensures that the DT behaves in a manner that is consistent with the physical robot, providing a realistic simulation environment.
- MoveIt: Within ROS, MoveIt orchestrates complex maneuvers by enabling collective planning and movement control of multiple robotic joints. MoveIt’s advanced algorithms allow for precise motion planning, collision avoidance, and task execution, which are critical for complex robotic operations.
2.2.1. Importing URDF to Unity
2.2.2. Integration of Unity and ROS
2.2.3. MoveIt for Motion-Planning
3. Experimentation and Results
3.1. Latency
3.2. Accuracy
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Singh, M.; Fuenmayor, E.; Hinchy, E.; Qiao, Y.; Murray, N.; Devine, D. Digital Twin: Origin to Future. Appl. Syst. Innov. 2021, 4, 36. [Google Scholar] [CrossRef]
- Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 2018, 51, 1016–1022. [Google Scholar] [CrossRef]
- Singh, M.; Srivastava, R.; Fuenmayor, E.; Kuts, V.; Qiao, Y.; Murray, N.; Devine, D. Applications of Digital Twin across Industries: A Review. Appl. Sci. 2022, 12, 5727. [Google Scholar] [CrossRef]
- Bratchikov, S.; Abdullin, A.; Demidova, G.L.; Lukichev, D.V. Development of Digital Twin for Robotic Arm. In Proceedings of the 2021 IEEE 19th International Power Electronics and Motion Control Conference, Gliwice, Poland, 25–29 April 2021. [Google Scholar] [CrossRef]
- González-Herbón, R.; González-Mateos, G.; Rodríguez-Ossorio, J.R.; Domínguez, M.; Alonso, S.; Fuertes, J.J. An Approach to Develop Digital Twins in Industry. Sensors 2024, 24, 998. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C.; Zhou, G.; He, J.; Li, Z.; Cheng, W. A data- and knowledge-driven framework for digital twin manufacturing cell. Procedia CIRP 2019, 83, 345–350. [Google Scholar] [CrossRef]
- Tao, F.; Cheng, J.; Qi, Q.; Zhang, M.; Zhang, H.; Sui, F. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. [Google Scholar] [CrossRef]
- Renard, D.; Saddem, R.; Annebicque, D.; Riera, B. From Sensors to Digital Twins toward an Iterative Approach for Existing Manufacturing Systems. Sensors 2024, 24, 1434. [Google Scholar] [CrossRef]
- Li, L.; Lei, B.; Mao, C. Digital twin in smart manufacturing. J. Ind. Inf. Integr. 2022, 26, 100289. [Google Scholar] [CrossRef]
- Qi, Q.; Tao, F.; Zuo, Y.; Zhao, D. Digital Twin Service towards Smart Manufacturing. Procedia CIRP 2018, 72, 237–242. [Google Scholar] [CrossRef]
- Zhang, Z.; Wen, F.; Sun, Z.; Guo, X.; He, T.; Lee, C. Artificial Intelligence-Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin. Adv. Intell. Syst. 2022, 4, 2100228. [Google Scholar] [CrossRef]
- Laaki, H.; Miche, Y.; Tammi, K. Prototyping a Digital Twin for Real Time Remote Control Over Mobile Networks: Application of Remote Surgery. IEEE Access 2019, 7, 20325–20336. [Google Scholar] [CrossRef]
- Liang, C.-J.; McGee, W.; Menassa, C.; Kamat, V. Bi-Directional Communication Bridge for State Synchronization between Digital Twin Simulations and Physical Construction Robots. In Proceedings of the 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, Kitakyushu, Japan, 27–28 October 2020. [CrossRef]
- Wang, X.; Liang, C.-J.; Menassa, C.C.; Kamat, V.R. Interactive and Immersive Process-Level Digital Twin for Collaborative Human–Robot Construction Work. J. Comput. Civil. Eng. 2021, 35, 04021023. [Google Scholar] [CrossRef]
- Luca, L.; Cristina, C.; Daniele, M.; Sébastien, B.; Lépine, P.; Marcello, P. Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task. Int. J. Adv. Manuf. Technol. 2020, 111, 1813–1829. [Google Scholar] [CrossRef]
- Liu, Y.K.; Ong, S.K.; Nee, A.Y.C. State-of-the-art survey on digital twin implementations. Adv. Manuf. 2022, 10, 1–23. [Google Scholar] [CrossRef]
- Al-Geddawy, T. A Digital Twin Creation Method for an Opensource Low-cost Changeable Learning Factory. Procedia Manuf. 2020, 51, 1799–1805. [Google Scholar] [CrossRef]
- Sharma, A.; Kosasih, E.; Zhang, J.; Brintrup, A.; Calinescu, A. Digital Twins: State of the art theory and practice, challenges, and open research questions. J. Ind. Inf. Integr. 2022, 30, 100383. [Google Scholar] [CrossRef]
- Baidya, S.; Das, S.K.; Uddin, M.H.; Kosek, C.; Summers, C. Digital Twin in Safety-Critical Robotics Applications: Opportunities and Challenges. In Proceedings of the 2022 IEEE International Performance, Computing, and Communications Conference, Austin, TX, USA, 11–13 November 2022. [Google Scholar] [CrossRef]
- Kousi, N.; Gkournelos, C.; Aivaliotis, S.; Lotsaris, K.; Bavelos, A.C.; Baris, P.; Michalos, G.; Makris, S. Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines. Appl. Sci. 2021, 11, 4620. [Google Scholar] [CrossRef]
- Garg, G.; Kuts, V.; Anbarjafari, G. Digital Twin for FANUC Robots: Industrial Robot Programming and Simulation Using Virtual Reality. Sustainability 2021, 13, 10336. [Google Scholar] [CrossRef]
- Erdei, T.I.; Krakó, R.; Husi, G. Design of a Digital Twin Training Centre for an Industrial Robot Arm. Appl. Sci. 2022, 12, 8862. [Google Scholar] [CrossRef]
- Wang, Z.; OuYang, Y.; Kochan, O. Bidirectional Linkage Robot Digital Twin System Based on ROS. In Proceedings of the 2023 17th International Conference on the Experience of Designing and Application of CAD Systems, Jaroslaw, Poland, 22–25 February 2023. [Google Scholar] [CrossRef]
- Diachenko, D.; Partyshev, A.; Pizzagalli, S.L.; Bondarenko, Y.; Otto, T.; Kuts, V. Industrial Collaborative Robot Digital Twin integration and Control Using Robot Operating System. J. Mach. Eng. 2022, 22, 57–67. [Google Scholar] [CrossRef]
- Kousi, N.; Gkournelos, C.; Aivaliotis, S.; Giannoulis, C.; Michalos, G.; Makris, S. Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf. 2019, 28, 121–126. [Google Scholar] [CrossRef]
- Havard, V.; Jeanne, B.; Lacomblez, M.; Baudry, D. Digital twin and virtual reality: A co-simulation environment for design and assessment of industrial workstations. Prod. Manuf. Res. 2019, 7, 472–489. [Google Scholar] [CrossRef]
- Pérez, L.; Rodríguez-Jiménez, S.; Rodríguez, N.; Usamentiaga, R.; García, D.F. Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning. Appl. Sci. 2020, 10, 3633. [Google Scholar] [CrossRef]
- Liu, C.; Gao, J.; Bi, Y.; Shi, X.; Tian, D. A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control. Sensors 2020, 20, 3515. [Google Scholar] [CrossRef]
- Matulis, M.; Harvey, C. A robot arm digital twin utilising reinforcement learning. Comput. Graph 2021, 95, 106–114. [Google Scholar] [CrossRef]
- Hassel, T.; Hofmann, O. Reinforcement Learning of Robot Behavior based on a Digital Twin. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, Valletta, Malta, 22–24 February 2020. [Google Scholar] [CrossRef]
- Dröder, K.; Bobka, P.; Germann, T.; Gabriel, F.; Dietrich, F. A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration. Procedia CIRP 2018, 76, 187–192. [Google Scholar] [CrossRef]
- Mullican, K. How Video Game Development Drives Digital Twins Technology for Manufacturers. Available online: https://accelerationeconomy.com/cloud-wars/how-video-game-development-drives-digital-twins-technology-for-manufacturers/ (accessed on 31 August 2023).
- Jungherr, A.; Schlarb, D.B. The Extended Reach of Game Engine Companies: How Companies Like Epic Games and Unity Technologies Provide Platforms for Extended Reality Applications and the Metaverse. Soc. Media Soc. 2022, 8, 20563051221107640. [Google Scholar] [CrossRef]
- Wang, Z.; Han, K.; Tiwari, P. Digital Twin Simulation of Connected and Automated Vehicles with the Unity Game Engine. In Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence, Beijing, China, 15 July–15 August 2021. [Google Scholar] [CrossRef]
- Liu, Y.; Wang, Z.; Han, K.; Shou, Z.; Tiwari, P.; Hansen, J.H.L. Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles. In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA, 19 October–13 November 2020. [Google Scholar] [CrossRef]
- Kuts, V.; Otto, T.; Tähemaa, T.; Bondarenko, Y. Digital twin based synchronised control and simulation of the industrial robotic cell using virtual reality. J. Mach. Eng. 2019, 19, 128–145. [Google Scholar] [CrossRef]
- White, G.; Zink, A.; Codecá, L.; Clarke, S. A digital twin smart city for citizen feedback. Cities 2021, 110, 103064. [Google Scholar] [CrossRef]
- Sta. Ana, R.R.; Escoto, J.E.; Fargas, D., Jr.; Panlilio, K.; Jerez, M.; Sarmiento, C.J. Development of A Digital Twin for The Monitoring of Smart Cities Using Open-Source Software. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2021, 46, 281–288. [Google Scholar] [CrossRef]
- Matsas, E.; Vosniakos, G.-C. Design of a virtual reality training system for human–robot collaboration in manufacturing tasks. Int. J. Interact. Des. Manuf. 2017, 11, 139–153. [Google Scholar] [CrossRef]
- Katara, P.; Khanna, M.; Nagar, H.; Panaiyappan, A. Open Source Simulator for Unmanned Underwater Vehicles using ROS and Unity3D. In Proceedings of the 2019 IEEE Underwater Technology, Kaohsiung, Taiwan, 16–19 April 2019. [Google Scholar] [CrossRef]
- Singh, M.; Srivastava, R.; Lee, S.D.; Murray, N.; Qiao, Y.; Devine, D. Digital Twin of Polymer Processing Pilot Line. In Proceedings of the 37th International Manufacturing Conference by Irish Manufacturing Council, Athlone, Ireland, 7–8 September 2021. [Google Scholar]
- Product specification IRB 1200. Available online: https://search.abb.com/library/Download.aspx?DocumentID=3HAC081417-001&LanguageCode=en&DocumentPartId=&Action=Launch (accessed on 10 August 2023).
- Unity Technologies. Available online: https://github.com/Unity-Technologies (accessed on 18 January 2024).
- Allspaw, J.; LeMasurier, G.; Yanco, H. Comparing Performance between Different Implementations of ROS for Unity. 2023. Available online: https://openreview.net/forum?id=WH3yhsbBjj (accessed on 15 August 2023).
- Joseph, L.; Cacace, J. Mastering ROS for Robotics Programming: Design, Build, and Simulate Complex Robots Using the Robot Operating System; Packt Publishing Ltd.: Birmingham, UK, 2018; ISBN 178847452X. [Google Scholar]
- O’Kane, J.M. A Gentle Introduction to ROS. 2014. Available online: https://www.cs.rpi.edu/~trink/Courses/AlgorithmicRobotics/fall2017/agitr-letter.pdf (accessed on 27 July 2023).
- Gallala, A.; Kumar, A.A.; Hichri, B.; Plapper, P. Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies. Sensors 2022, 22, 4950. [Google Scholar] [CrossRef]
- XML Robot Description Format (URDF). Available online: http://wiki.ros.org/urdf/XML/model (accessed on 31 August 2023).
- Unity Robotics Hub. Available online: https://github.com/Unity-Technologies/Unity-Robotics-Hub/blob/main/tutorials/pick_and_place/2_ros_tcp.md (accessed on 31 August 2023).
- ROS–Unity Integration. Available online: https://github.com/Unity-Technologies/Unity-Robotics-Hub/blob/main/tutorials/ros_unity_integration/README.md (accessed on 24 December 2023).
- Coleman, D.M.; Sucan, I.A.; Chitta, S.; Correll, N. Reducing the Barrier to Entry of Complex Robotic Software: A MoveIt! Case Study. arXiv 2014. [Google Scholar] [CrossRef]
- Mazumder, A.; Sahed, M.; Tasneem, Z.; Das, P.; Badal, F.; Ali, M.; Ahamed, M.H.; Abhi, S.; Sarker, S.; Das, S.; et al. Towards Next Generation Digital Twin in Robotics: Trends, Scopes, Challenges, and Future. Heliyon 2023, 9, e13359. [Google Scholar] [CrossRef] [PubMed]
- Mihai, S.; Yaqoob, M.; Hung, D.; Davis, W.; Towakel, P.; Raza, M.; Karamanoglu, M.; Barn, B.; Shetve, D.; Prasad, R.; et al. Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Commun. Surv. Tutor. 2022, 24, 2255–2291. [Google Scholar] [CrossRef]
- Saavedra Sueldo, C.; Perez Colo, I.; De Paula, M.; Villar, S.A.; Acosta, G.G. ROS-based architecture for fast digital twin development of smart manufacturing robotized systems. Ann. Oper. Res. 2023, 322, 75–99. [Google Scholar] [CrossRef]
- Tao, F.; Zhang, M.; Liu, Y.; Nee, A.Y.C. Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 2018, 67, 169–172. [Google Scholar] [CrossRef]
- Kober, C.; Fette, M.; Wulfsberg, J.P. Challenges of Digital Twin Application in Manufacturing. In Proceedings of the 2022 IEEE International Conference on Industrial Engineering and Engineering Management, Kuala Lumpur, Malaysia, 7–10 December 2022. [Google Scholar] [CrossRef]
- Nayernia, H.; Bahemia, H.; Papagiannidis, S. A systematic review of the implementation of industry 4.0 from the organisational perspective. Int. J. Prod. Res. 2022, 60, 4365–4396. [Google Scholar] [CrossRef]
- Rebentisch, E.; Rhodes, D.H.; Soares, A.L.; Zimmerman, R.; Tavares, S. The digital twin as an enabler of digital transformation: A sociotechnical perspective. In Proceedings of the 2021 IEEE 19th International Conference on Industrial Informatics, Palma de Mallorca, Spain, 21–23 July 2021. [Google Scholar] [CrossRef]
- Korotkova, N.; Benders, J.; Mikalef, P.; Cameron, D. Maneuvering between skepticism and optimism about hyped technologies: Building trust in digital twins. Inf. Manag. 2023, 60, 103787. [Google Scholar] [CrossRef]
- Fett, M.; Wilking, F.; Goetz, S.; Kirchner, E.; Wartzack, S. A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems. Sensors 2023, 23, 9786. [Google Scholar] [CrossRef]
- Zafar, M.H.; Langås, E.F.; Sanfilippo, F. Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review. Robot Comput. Integr. Manuf. 2024, 89, 102769. [Google Scholar] [CrossRef]
- Khan, L.U.; Han, Z.; Saad, W.; Hossain, E.; Guizani, M.; Hong, C.S. Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities. IEEE Commun. Surv. Tutor. 2022, 24, 2230–2254. [Google Scholar] [CrossRef]
- Alcaraz, C.; Lopez, J. Digital Twin: A Comprehensive Survey of Security Threats. Commun. Surv. Tutor. 2022, 24, 1475–1503. [Google Scholar] [CrossRef]
Engine Used | Key Contributions | Pros | Cons | Reference |
---|---|---|---|---|
Gazebo | Autonomous and remote operations for safety | Manages environmental uncertainties | Limited to specific robot models | [19] |
Gazebo | Improved efficiency with multitasking mobile dual-arm robots | Enhances factory productivity | Complexity in integration | [20] |
Unity | Trajectory programming and data for ML applications | Suitable for complex environments | High computational requirements | [21] |
Gazebo | Safe training environment for students | Cost-effective training | Limited to educational use | [22] |
Unreal Engine 4 | Real-time monitoring and control for educational purposes | Enhances learning experience | High system requirements | [23] |
Unity | Synchronization and advanced user interfaces | Improved teleoperation | Initial setup complexity | [24] |
Gazebo | Reconfigurable assembly systems for automotive industry | Facilitates human–robot collaboration | High complexity in dynamic environments | [25] |
Robot Specifications | Axis Max. Speed | Working Range | Range of Motion |
---|---|---|---|
Axes: 6 | |||
Payload: 5 kg | Axis 1: 288°/s | Axis 1: +170° to −170° | Axis 1: 340° |
Armload: 0.3 kg | Axis 2: 240°/s | Axis 2: +130° to −100° | Axis 2: 230° |
Reach: 0.9 m | Axis 3: 300°/s | Axis 3: +70° to −200° | Axis 3: 270° |
Position repeatability: 0.025 mm | Axis 4: 400°/s | Axis 4: +270° to −270° | Axis 4: 540° |
Robot weight: 54 kg | Axis 5: 405°/s | Axis 5: +130° to −130° | Axis 5: 260° |
Robot height: 967 mm | Axis 6: 600°/s | Axis 6: +400° to −400° | Axis 6: 800° |
Mounting: Any angle |
Package | Functionality |
---|---|
ROS-TCP-Connector | Unity package for sending, receiving, and visualizing messages from ROS |
ROS TCP-Endpoint | ROS node for sending/receiving messages from Unity |
URDF-Importer | Unity package for loading URDF files |
Joints | Cycle 1 | Cycle 2 | Cycle 3 | Overall | ||||
---|---|---|---|---|---|---|---|---|
MAE | MNE | MAE | MNE | MAE | MNE | MAE | MNE | |
Joint_1 | 0.0024 | 0.0000 | 0.0022 | 0.0000 | 0.0018 | 0.0000 | 0.0021 | 0.0000 |
Joint_2 | 0.1633 | 0.0007 | 0.1650 | 0.0007 | 0.1720 | 0.0007 | 0.1666 | 0.0007 |
Joint_3 | 0.0073 | 0.0000 | 0.0075 | 0.0000 | 0.0071 | 0.0000 | 0.0073 | 0.0000 |
Joint_4 | 0.0022 | 0.0000 | 0.0022 | 0.0000 | 0.0024 | 0.0000 | 0.0023 | 0.0000 |
Joint_5 | 0.0005 | 0.0000 | 0.0005 | 0.0000 | 0.0004 | 0.0000 | 0.0005 | 0.0000 |
Joint_6 | 0.0002 | 0.0000 | 0.0003 | 0.0000 | 0.0002 | 0.0000 | 0.0002 | 0.0000 |
Joints | Cycle 1 | Cycle 2 | Cycle 3 | Overall | ||||
---|---|---|---|---|---|---|---|---|
SDAE | SDNE | SDAE | SDNE | SDAE | SDNE | SDAE | SDNE | |
Joint_1 | 0.0206 | 0.0001 | 0.0203 | 0.0001 | 0.0113 | 0.0000 | 0.0181 | 0.0001 |
Joint_2 | 0.1072 | 0.0005 | 0.1065 | 0.0005 | 0.1066 | 0.0005 | 0.1069 | 0.0005 |
Joint_3 | 0.0084 | 0.0000 | 0.0148 | 0.0001 | 0.0070 | 0.0000 | 0.0105 | 0.0000 |
Joint_4 | 0.0144 | 0.0000 | 0.0116 | 0.0000 | 0.0194 | 0.0000 | 0.0154 | 0.0000 |
Joint_5 | 0.0044 | 0.0000 | 0.0030 | 0.0000 | 0.0030 | 0.0000 | 0.0036 | 0.0000 |
Joint_6 | 0.0038 | 0.0000 | 0.0024 | 0.0000 | 0.0012 | 0.0000 | 0.0028 | 0.0000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Singh, M.; Kapukotuwa, J.; Gouveia, E.L.S.; Fuenmayor, E.; Qiao, Y.; Murry, N.; Devine, D. Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell. Sensors 2024, 24, 5680. https://doi.org/10.3390/s24175680
Singh M, Kapukotuwa J, Gouveia ELS, Fuenmayor E, Qiao Y, Murry N, Devine D. Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell. Sensors. 2024; 24(17):5680. https://doi.org/10.3390/s24175680
Chicago/Turabian StyleSingh, Maulshree, Jayasekara Kapukotuwa, Eber Lawrence Souza Gouveia, Evert Fuenmayor, Yuansong Qiao, Niall Murry, and Declan Devine. 2024. "Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell" Sensors 24, no. 17: 5680. https://doi.org/10.3390/s24175680