Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges
•Propose a four-layer Robot Digital Twin (RDT) architecture that demonstrates how key Industry 4.0 technologies collectively enhance RDT’s capabilities in manufacturing systems.•Provide an in-depth analysis of current RDT systems’ applications from domain-specific and system-level perspectives, offe...
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| Published in: | Robotics and computer-integrated manufacturing Vol. 97; no. February 2026; p. 103103 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.02.2026
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| ISSN: | 0736-5845, 1879-2537 |
| Online Access: | Get full text |
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| Abstract | •Propose a four-layer Robot Digital Twin (RDT) architecture that demonstrates how key Industry 4.0 technologies collectively enhance RDT’s capabilities in manufacturing systems.•Provide an in-depth analysis of current RDT systems’ applications from domain-specific and system-level perspectives, offering valuable insights into their implementation and potential impacts.•Identify critical trends, practical challenges, and promising future research directions for advancing RDT systems towards smarter and more adaptive manufacturing paradigms.
The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels. |
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| AbstractList | •Propose a four-layer Robot Digital Twin (RDT) architecture that demonstrates how key Industry 4.0 technologies collectively enhance RDT’s capabilities in manufacturing systems.•Provide an in-depth analysis of current RDT systems’ applications from domain-specific and system-level perspectives, offering valuable insights into their implementation and potential impacts.•Identify critical trends, practical challenges, and promising future research directions for advancing RDT systems towards smarter and more adaptive manufacturing paradigms.
The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels. The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels. |
| ArticleNumber | 103103 |
| Author | Liu, Zhihao Wang, Lihui Wiktorsson, Magnus Wang, Wei Zhong, Ruirui Qin, Qiang Wang, Xi Vincent |
| Author_xml | – sequence: 1 givenname: Qiang surname: Qin fullname: Qin, Qiang organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 2 givenname: Zhihao surname: Liu fullname: Liu, Zhihao organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 3 givenname: Ruirui surname: Zhong fullname: Zhong, Ruirui organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 4 givenname: Xi Vincent orcidid: 0000-0001-9694-0483 surname: Wang fullname: Wang, Xi Vincent email: wangxi@kth.se organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 5 givenname: Lihui surname: Wang fullname: Wang, Lihui organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 6 givenname: Magnus surname: Wiktorsson fullname: Wiktorsson, Magnus organization: Department of Production Engineering, KTH Royal Institute of Technology, Stockholm 10044, Sweden – sequence: 7 givenname: Wei surname: Wang fullname: Wang, Wei organization: School of Engineering Sciences, University of Skövde, Skövde 54134, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-25761$$DView record from Swedish Publication Index (Högskolan i Skövde) https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-369277$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) |
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| Keywords | Advanced robotics Industry 4.0 Digital twin Smart manufacturing |
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| Snippet | •Propose a four-layer Robot Digital Twin (RDT) architecture that demonstrates how key Industry 4.0 technologies collectively enhance RDT’s capabilities in... The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework.... |
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| SubjectTerms | Adaptive control systems Advanced robotic Advanced robotics Digital production system Digital twin Flexible manufacturing systems Flexible production systems Human robot interaction Industrial research Industry 4.0 Intelligent robots Internet of things Man machine systems Manufacturing applications Manufacturing challenges Manufacturing industries Manufacturing technologies Materials handling Predictive analytics Robotic assembly Smart manufacturing Technology application Technology challenges Virtual Manufacturing Processes (VMP) |
| Title | Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges |
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