Work

Immersive physical and agentic artificial intelligence of things in the multi-sensory extended reality-based cognitive robotic digital twin economy

Year: 2026

Type: article

Abstract: Research background: Our research is developed on the basis of literature indicating that neuromorphic image sensing algorithms and computer vision-based tactile sensing technologies can configure physical artificial intelligence (AI) industrial humanoid robotic and immersive 3D extended reality metaverse-based manufacturing machine workflow forecasting, embodied AI humanoid robotic manufacturing process and task performance, and industrial humanoid robotic manufacturing machine and computer vision-based production line operations in collaborative autonomous physical AI and deep neural network-based industrial humanoid robotic factories. The research gap diagnosis identifies a lack in the literature, with regard to how motion planning and digital signal processing algorithms can further real-time digital twin industrial metaverse-based task-specific agentic AI manufacturing process and context-aware high-volume production line coordination, autonomous end-to-end robotic coordination-intensive operational workflow execution, and flexible industrial multi-functional humanoid robotic manufacturing and full production scale operations. Purpose of the article: The objective of the paper is to clarify whether Internet of Things (IoT) sensing and actuation technologies can enable long-term enterprise agentic AI cyber-physical production-based automation performance, context-aware cross-functional end-to-end manufacturing and deep reinforcement learning-based workflow performance orchestration, and autonomous collaborative agentic AI and computer vision-based enterprise manufacturing operation modeling. The current paper improves the state of the art by indicating that digital twin-based industrial AI immersive and autonomous robotic machine tools can articulate large-scale real-time agentic AI and predictive analytics-based collaborative workflow augmentation assessment, computer vision-based physical AI resilient multi-agentic workflow and humanoid robotic factory operation management, and enterprise agentic AI performance coordination and multi-step manufacturing task completion. Methods: A quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, and the most relevant research published between 2025 and 2026 was identified and inspected. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was deployed for the search results and screening by use of the web-based Shiny app. Dimensions (for bibliometric mapping) and VOSviewer (for layout algorithms) were the harnessed data visualization tools. The machine learning-based study selection process and text mining systematic review management software and tools employed include: AMSTAR, BIBOT, CADIMA, Catchii, DistillerSR, Eppi-Reviewer, JBI SUMARI, Litstream, MMAT, Nested Knowledge, PICO Portal, Rayyan, and SWIFT-Active Screener. Findings & value added: The main value added derived from the systematic literature review is that sensor processing algorithms and immersive virtual reality technologies can reinforce enterprise AI process and multi-agent system operation automation, large-scale autonomous cloud and edge computing-based robotic enterprise business management process flexibility, and IoT industrial humanoid robotic enterprise business value and equipment performance forecasting. The benefits for theory and current state of the art are that visual recognition and motion control algorithms can assist cross-functional collaborative digital twin-based physical AI and cloud and edge computing-based factory floor production line simulation, enterprise cross-functional operation and predictive analytics-based resource planning automation, and predictive analytics-based collaborative immersive physical AI robotic and virtual 3D scene-based multi-agentic system performance management, for predictive maintenance-based large-scale flexible end-to-end sustained business value chains. Policy implications reveal that neuromorphic image sensing algorithms and computer vision-based tactile sensing technologies can support real-world physical AI industrial robotic and extended reality-based machine operation automation, enterprise business process functionality, management, and interoperability, and metaverse digital twin-based robotic enterprise immersive manufacturing process modeling.

Source: Oeconomia Copernicana

DOI: 10.24136/oc.4528