A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications

Augmented Reality (AR) has experienced an increasing trend in applied research in the last few years. This emerging trend is focused in content-related challenges: mainly creation (Authoring), adaptation (Context-Awareness) and improvement (Interaction-Analysis) of augmented content. Research in these techniques has enabled Academia to recognise Augmented Reality capability for knowledge transfer, either from AR systems to users or between users. But to the best of author’s knowledge, there are no specific literature review in these areas, neither on their relations with AR knowledge transfer ability. Therefore, this paper aims to identify these relations through an analysis of state-of-the-art techniques in Authoring (A), Context-Awareness (CA) and Interaction-Analysis (IA) in the context of maintenance applications. In order to do so, a Systematic Literature Review (SLR) has been conducted on 74 application-relevant papers from 2012 to 2017. It comprised a thematic analysis to establish the relation between maintenance applications, research in A, CA and IA and AR knowledge transfer modes. Its results helped to classify AR maintenance-applications by technological readiness levels. They also revealed the potential of AR for users’ knowledge capture, and future research required for full knowledge management capabilities. Furthermore, the SLR method proposed could be extended to correlate AR systems and applications by their knowledge management capabilities in any AR application context

Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-to-Action

Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and are increasingly being employed in diverse fields. One such promising application is in operations and maintenance (O&M). O&M tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or under high-stress situations. By marrying the advantages of superimposing virtual objects onto the physical world, and generating human-like text using GPT, we can revolutionize O&M operations. This study introduces a system that combines AR, Optical Character Recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in O&M tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=15) is conducted to illustrate the findings and answer the research questions. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggests a reduction in cognitive load and an increase in trust when executing the same operations using the AR and AI system.Comment: 36 page