Abstract

Industrial Augmented Reality (IAR) is one of the key technologies pointed out by the Industry 4.0 paradigm as a tool for improving industrial processes and for maximizing worker efficiency. Training and assistance are two of the most popular IAR-enabled applications, since they may significantly facilitate, support, and optimize production and assembly tasks in industrial environments. This article presents an IAR collaborative application developed jointly by Navantia, one of the biggest European shipbuilders, and the University of A Coruña (Spain). The analysis, design, and implementation of such an IAR application are described thoroughly so as to enable future developers to create similar IAR applications. The IAR application is based on the Microsoft HoloLens smart glasses and is able to assist and to guide shipyard operators during their training and in assembly tasks. The proposed IAR application embeds a novel collaborative protocol that allows operators to visualize and interact in a synchronized way with the same virtual content. Thus, all operators that share an IAR experience see each virtual object positioned at the same physical spot and in the same state. The collaborative application is first evaluated and optimized in terms of packet communications delay and anchor transmission latency, and then, its validation in a shipyard workshop by Navantia’s operators is presented. The performance results show fast response times for regular packets (less than 5 ms), low interference rates in the 5 GHz band, and an anchor transmission latency of up to 30 s. Regarding the validation tests, they allow for obtaining useful insights and feedback from the industrial operators, as well as clear guidelines that will help future developers to face the challenges that will arise when creating the next generation of IAR applications.

Highlights

  • The Fourth Industrial Revolution, called the Industry 4.0 paradigm, represents the digital transformation of factories in terms of people, processes, services, and systems to increase competitiveness and offer a customer-centered value

  • A similar work with a smartphone-based application that uses Vuforia and Unity software was described in [29]. Such an application was aimed at training and assistance when handling industrial equipment (e.g., PLCs), and its usability was evaluated through a questionnaire

  • It must be noted that such a latency is mostly determined by the size of the anchor generated by the Microsoft HoloLens framework, which depends on the complexity of the shapes of the real-world environment and on the amount of detail with which the Augmented Reality (AR) devices have been able to map their surroundings

Read more

Summary

Introduction

The Fourth Industrial Revolution, called the Industry 4.0 paradigm, represents the digital transformation of factories in terms of people, processes, services, and systems to increase competitiveness and offer a customer-centered value. IAR can help to improve industrial processes, to maximize worker efficiency and mobility, and to incorporate new learning and training procedures such as blended teaching and digital near-the-job trainings [15]. Such trainings can reduce costs and time, increase safety, and adapt to different learning paces and experience. A novel collaborative IAR framework is proposed in order to enable creating IAR experiences The performance of such a framework is evaluated in terms of packet communications delay, communication interference, and anchor transmission latency.

IAR Training Systems
IAR Assistance Systems
Developing IAR Training and Assistance Systems
Shipbuilding IAR Systems
Analysis of the State-of-the-Art
Analysis and Design of the Proposed System
Main Goals of the System
Design Requirements
Communications Architecture
Hardware and Software
Collaborative Framework
Master’s Discovery Process
Anchor Synchronization
AR User Event Synchronization
Design
Implementation
Experimental Setup
Regular Packet Communication Delay
Interference Influence
Anchor Transmission Latency
Validation Tests
Operator Feedback
Key Findings
Next Challenges
Findings
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.