Integrated registration and utility of mobile AR Human-Machine collaborative assembly in rail transit

Abstract

In the Industry 5.0 and digital transformation stage, with human–machine collaboration at the core, an AR human–machine collaboration system can be used to enhance the perception ability of business personnel towards assembly interaction scenarios and objects, effectively improving the efficiency and quality of assembly. However, existing research on the effectiveness of AR human–machine collaborative assembly in more open and flexible rail transit scenarios is uncertain and limited. This article studies the integrated registration and utility of mobile AR human–machine collaborative assembly in rail transit. By proposing a mobile AR End-to-end Integrated Registration Network AR-EIRNet, which is based on AR assembly objects multiscale feure detection, pose inference optimization, and virtual-real fusion rendering, the virtual-real fusion registration application of rail transit AR human–machine collaborative assembly is realized, and then conduct in-depth analysis of AR-EIRNet performance based on RGB-synthesized enhanced images of rail transit, and the effectiveness of AR human–machine collaborative assembly is comprehensively and deeply evaluated via the virtual-real interaction behaviour of the ZD6 switch machine and a subjective questionnaire survey. The experimental results show that in open and complex rail transit scenarios, the registration accuracy of AR-EIRNet reaches 80%, and the assembly time and error count are reduced by 38% and 43% respectively, demonstrating AR-EIRNet has strong robustness and generalizability. AR handheld are more in line with operating habits, but the lack of immersion and interactive experience limits the significant improvement in complex assembly effects. AR glasses with multimodal perception can increase the cognitive load, but moderate cognitive pressure and multimodal interaction are beneficial for stimulating autonomous assembly motivation. The organic combination of two types of AR interactions can effectively meet the practical needs of adaptive, refined, and efficient AR human–machine collaborative digital assembly operations in open and complex rail transit scenarios.