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Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden ; School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
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2026 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 97, no February 2026, article id 103103Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2026. Vol. 97, no February 2026, article id 103103
Keywords [en]
Advanced robotics, Digital twin, Industry 4.0, Smart manufacturing, Adaptive control systems, Flexible manufacturing systems, Human robot interaction, Industrial research, Intelligent robots, Internet of things, Man machine systems, Materials handling, Predictive analytics, Robotic assembly, Advanced robotic, Digital production system, Flexible production systems, Manufacturing applications, Manufacturing challenges, Manufacturing industries, Manufacturing technologies, Technology application, Technology challenges
National Category
Production Engineering, Human Work Science and Ergonomics Robotics and automation
Research subject
Virtual Manufacturing Processes (VMP)
Identifiers
URN: urn:nbn:se:his:diva-25761DOI: 10.1016/j.rcim.2025.103103ISI: 001582099600001Scopus ID: 2-s2.0-105013503596OAI: oai:DiVA.org:his-25761DiVA, id: diva2:1992764
Funder
EU, Horizon 2020, 101079398XPRES - Initiative for excellence in production research
Note

CC BY 4.0

© 2025 The Author(s)

Correspondence Address: X.V. Wang; Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, 10044, Sweden; email: wangxi@kth.se; CODEN: RCIME

This research was supported by the EU Horizon Europe NEPTUN project (Grant Agreement: 101079398), the Swedish Digital Futures project: Towards Safe Smart Construction (VF 2020-0315), Swedish research centre of eXcellence in PRoduction RESearch (XPRES), China Scholarship Council (CSC 202308430011).

Available from: 2025-08-28 Created: 2025-08-28 Last updated: 2025-12-30Bibliographically approved

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Wang, LihuiWang, Wei

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