ABSTRACT. The aim of this systematic review is to synthesize and analyze robotic navigation and computer vision artificial intelligence systems, semantic 3D mapping and predictive maintenance algorithms, multiple autonomous mobile robots, and context modeling and digital twin simulation tools. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout January 2023, with search terms including “digital twin factories and interactive industrial metaverse” + “robotic navigation and computer vision artificial intelligence systems,” “image processing computational and spatio-temporal fusion algorithms,” and “deep generative modeling and 3D simulation tools.” As research published in 2022 and 2023 was inspected, only 167 articles satisfied the eligibility criteria, and 19 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: Abstrackr, AXIS, Catchii, R package and Shiny app citationchaser, JBI SUMARI, and Litstream.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: robotic navigation; computer vision artificial intelligence; spatio-temporal fusion; deep generative modeling; digital twin factory; interactive industrial metaverse

How to cite: Bonciu, C. I., Ćurčić, N., Burcea, Ș. G., Cug, J., Tulyakova, I., and Dengov, V. V. (2023). “Robotic Navigation and Computer Vision Artificial Intelligence Systems, Image Processing Computational and Spatio-Temporal Fusion Algorithms, and Deep Generative Modeling and 3D Simulation Tools in Digital Twin Factories and Interactive Industrial Metaverse,” Journal of Self-Governance and Management Economics 11(2): 42–56. doi: 10.22381/jsme11220233.

Received 28 January 2023 • Received in revised form 21 June 2023
Accepted 23 June 2023 • Available online 30 June 2023