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Cited by 38Digital Transformation in IndustryImpact of AI and Big Data on Business and SocietyApplied Advanced Technologies

Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse

Open access
Lazaroiu, George, Gedeon, Tom, Valaskova, Katarina, Vrbka, Jaromír, Šuleř, Petr, Zvarikova, Katarina, Kramarova, Katarina, Rowland, Zuzana +8 more·2024·Equilibrium. Quarterly Journal of Economics and Economic Policy
DOI 10.24136/eq.3131
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Abstract

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

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  • Digital Twins operating at a 10 Hz simulation rate reduce testing cycles and improve human-robot collaboration planning accuracy, despite minor synchronization lags.

    “Digital Twins, operating at a 10 Hz simulation rate, reduce testing cycles and improve HRC planning accuracy [45], despite minor synchronization lags [11].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [45]

    The section on interconnected IoRT and cloud-connected devices describes digital-twin simulation for robotic manufacturing and integrated process planning. It does not report a 10 Hz simulation rate, shorter testing cycles, or improved human–robot collaboration planning accuracy. The synchronization-lag clause is cited to [11], not this source. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Digital Twins with a 10 Hz simulation rate improve maintenance planning in manufacturing.

    “Digital Twins with a 10 Hz simulation rate improve maintenance planning in manufacturing [45]”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [45]

    The manufacturing-systems section links digital twins with simulation, production planning, and predictive maintenance, and another passage links industrial robot digital twins with predictive-maintenance accuracy. Neither passage specifies a 10 Hz simulation rate or shows that operating at that rate improves maintenance planning. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Approximately 23% of reported fault-tolerant strategies have been physically validated.

    “The field remains constrained by limited physical validation-approximately 23% of reported strategies [3]-prompting a need for future efforts to bridge simulation and practice.”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [45]

    The Introduction discusses digital-twin modeling and simulation, and the section on interconnected IoRT discusses monitoring and predictive maintenance, but neither addresses physical validation of fault-tolerant strategies. Searches for the claimed 23% figure, fault-tolerant strategies, and physical validation found no supporting passage in our copy; the retrieved passages have no page numbers. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • There are discrepancies between simulation and real-world performance in fault-tolerant cooperative robotics.

    “The validation gap-only 23% of strategies physically tested [45]underscores discrepancies between simulation and real-world performance, despite efforts with blockchain-enhanced data flow [37] and terrain-adaptive dynamics [19].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [45]

    The Conclusions discuss simulation modeling and robotic cooperation, but do not compare simulated with real-world performance. Searches for physical validation, a simulation-to-real gap, and the cited 23% figure found no passage addressing those points. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cites

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Sorted by citations

Authors
Lazaroiu, G., Gedeon, T., Valaskova, K., Vrbka, J., Šuleř, P., Zvarikova, K., Kramarova, K., Rowland, Z., Stehel, V., Gajanova, L., Horák, J., Grupac, M., Caha, Z., Blazek, R., Kovalova, E., & Nagy, M.
Affiliations
  • Curtin University (AU)
  • Institute of Technology and Business (CZ)
  • University of Žilina (SK)
ORCID
Venue
Equilibrium. Quarterly Journal of Economics and Economic Policy
Publisher
Instytut Badan Gospodarczych / Institute of Economic Research
Published
2024-09-27
Volume
19
Issue
3
Pages
719-748
ISSN
  • 1689-765X (ISSN-L)
  • 2353-3293
DOI
10.24136/eq.3131
OpenAlex
W4402917435
Cited by
38
References
1
Citations by year
2026: 14 · 2025: 24
FWCI
7.79
Type
journal article
Language
en
Open access
Licence
Locations
Equilibrium Quarterly Journal of Economics and Economic Policy (publishedVersion, cc-by)
Funders
  • Agentúra na Podporu Výskumu a Vývoja (VEGA 1/0494/24)
  • Vedecká Grantová Agentúra MŠVVaŠ SR a SAV
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
Keywords
Internet of Robotic Things, digital twin, industrial metaverse, extended reality, generative AI, cyber-physical production systems, smart factory, robot trajectory planning, collision-free motion planning, edge and fog computing, deep reinforcement learning, spatial computing, computer vision, sensor fusion

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