AI-Powered Interfaces for Extended Reality to support Remote Maintenance

TL;DR

This paper introduces AI-powered augmented reality interfaces for remote industrial maintenance, utilizing wearable targets and face orientation to enable hands-free, unbiased interactions supported by deep learning for motion adjustment.

Contribution

It presents novel AI-driven AR interaction interfaces that reduce user bias and improve remote maintenance efficiency in industrial settings.

Findings

The proposed interfaces outperform traditional methods in user bias reduction.

Deep learning models enable real-time motion adjustment for more accurate interactions.

Experimental results show increased maintenance accuracy and user satisfaction.

Abstract

High-end components that conduct complicated tasks automatically are a part of modern industrial systems. However, in order for these parts to function at the desired level, they need to be maintained by qualified experts. Solutions based on Augmented Reality (AR) have been established with the goal of raising production rates and quality while lowering maintenance costs. With the introduction of two unique interaction interfaces based on wearable targets and human face orientation, we are proposing hands-free advanced interactive solutions in this study with the goal of reducing the bias towards certain users. Using traditional devices in real time, a comparison investigation using alternative interaction interfaces is conducted. The suggested solutions are supported by various AI powered methods such as novel gravity-map based motion adjustment that is made possible by predictive deep models that reduce the bias of traditional hand- or finger-based interaction interfaces