ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore deep reinforcement learning-based algorithms, context-aware augmented reality and metaverse decentralized governance systems, multi-sensory tracking and mobile edge computing technologies, and photorealistic synthetic images and data. Throughout February 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “autonomous manufacturing plants across immersive virtual spaces” + “ambient scene detection and remote assistance tools,” “synthetic media-based automated production and metaverse decentralized governance systems,” and “multi-sensory simulation and visual perception algorithms.” As research published in 2022 and 2023 was inspected, only 171 articles satisfied the eligibility criteria, and 17 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, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, and Rayyan.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: ambient scene detection; remote assistance; synthetic media-based automated production; metaverse; multi-sensory simulation; visual perception algorithms

How to cite: Saremi, M., Stamule, S., Radulica, M. I., Miklencicova, R., and Grupac, M. (2023). “Ambient Scene Detection and Remote Assistance Tools, Synthetic Media-based Automated Production and Metaverse Decentralized Governance Systems, and Multi-Sensory Simulation and Visual Perception Algorithms in Autonomous Manufacturing Plants across Immersive Virtual Spaces,” Journal of Self-Governance and Management Economics 11(2): 57–71. doi: 10.22381/jsme11220234.

Received 25 February 2023 • Received in revised form 24 June 2023
Accepted 27 June 2023 • Available online 30 June 2023