Year: 2026
Type: article
Study design: Systematic Review
Abstract: Cross Reality (CR) enables seamless transitions and collaboration between environments characterized by different degrees of virtuality, potentially offering new opportunities for the integrated use of Augmented Reality (AR) and Virtual Reality (VR) in industrial settings. From this perspective, CR may represent a valuable approach for supporting operational performance and worker experience in industrial workspaces. While AR and VR have demonstrated benefits when applied independently, their isolated adoption can be affected by technical, spatial, and interaction-related constraints. By combining their complementary strengths, CR could help address some of these limitations and support more flexible, continuous, and human-centered work experiences. Despite this potential, CR remains an emerging research field, and its industrial applications have not yet been thoroughly explored. To address this gap, this study is framed as a conceptual design investigation grounded in a Systematic Literature Review (SLR). The SLR identifies and analyzes existing CR studies and applications in industrial contexts. Its findings indicate that, despite growing research interest, concrete and mature industrial CR applications remain limited. The reviewed studies nevertheless indicate potential benefits for workflow efficiency, collaboration, workload reduction, and usability. Building on the evidence and research gaps identified through the SLR, a collaborative design session involving academic and industrial experts in AR and VR was conducted to develop prospective CR application concepts across seven industrial domains identified in the literature. Overall, the study provides both a systematic account of the current state of industrial CR research and a set of conceptual directions for future development and empirical validation.
Institution: Polytechnic University of Bari
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,490
Award: G984308157
OpenAlex ID: W7214444722