ABSTRACT. The aim of this systematic review is to synthesize and analyze multi-digital twin cooperation in smart industrial equipment and manufacturing environments. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout July 2024, with search terms including “Internet of Things reinforcement learning-based autonomous manufacturing operations in the industrial metaverse” + “context-aware augmented reality and Industry 4.0 cyber–physical production multi-robotic systems,” “digital twin artificial intelligence sensing and 3D immersive virtual reality technologies,” and “environment mapping and computer vision algorithms.” As research published in 2022 and 2023 was inspected, only 153 articles satisfied the eligibility criteria, and 18 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, JBI SUMARI, Litstream, MMAT, PICO Portal, and SRDR+.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: cyber–physical production; digital twin artificial intelligence; 3D immersive virtual reality; autonomous manufacturing operation; industrial metaverse

How to cite: Jandacka, J., Bîgu, A.-M., Hysa, E., Chitca, C., and Grigoroiu, M. (2024). “Context-Aware Augmented Reality and Industry 4.0 Cyber–Physical Production Multi-Robotic Systems, Digital Twin Artificial Intelligence Sensing and 3D Immersive Virtual Reality Technologies, and Environment Mapping and Computer Vision Algorithms for Internet of Things Reinforcement Learning-based Autonomous Manufacturing Operations in the Industrial Metaverse,” Journal of Self-Governance and Management Economics 12(4): 9–23. doi: 10.22381/jsme12420241.

Received 12 August 2024 • Received in revised form 19 December 2024
Accepted 22 December 2024 • Available online 29 December 2024