Research on Multi-User Collaboration Framework for XR based High-Risk Industries Safety Training = XR 기반 고위험 산업 안전 교육을 위한 다중 사용자 협업 프레임워크 연구

다국어 초록 (Multilingual Abstract)

Extended Reality (XR), as a developmental platform technology compatible with Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is increasingly widely used across various industries. However, existing XR systems often focus on individual technical training in some training applications, neglecting multi-user coordination and real-time collaboration, which are crucial for high-risk industries such as offshore maintenance, tunnel construction, and hazardous material handling. This study proposes a multi-user collaborative framework for safe training based on XR, aiming to build a logical architecture that supports cross-device interaction, real-time synchronization, and heterogeneous interaction among participants. The framework features controllable latency processing, real-time synchronization across multiple devices, modular device combinations, and various interaction methods, which can meet the diverse needs of various training content. Through comparative analysis with existing XR training architectures and industrial applications, the proposed framework shows great potential in scalability, user operability, and collaborative training efficiency, and provides theoretical insights and practical guidance for a more comprehensive XR training framework, further improving team collaboration, operational accuracy, and safety management in high-risk industrial environments.

국문 초록 (Abstract)

가상현실(VR), 증강현실(AR), 혼합현실(MR)을 포함하고 서로 호환되고 통합되는 개발 플랫폼 기술 체계 인 확장현실(XR)은 다양한 산업 분야에서 점점 더 널리 활용되고 있다. 그러나 기존 XR 시스템은 일부 교육 애플리케이션에서 개별 기술 교육에 집중하는 경향이 있어 해상 유지관리, 터널 건설, 유해물질 취급과 같은 고위험 산업에 필수적인 다중 사용자 협업 및 실시간 협업을 간과하는 경우가 많다. 본 연구는 XR 기반 안전 교육을 위한 다중 사용자 협업 프레임워크를 제안하며, 교차 기기 상호작용, 실시간 동기화, 그리고 참여자 간의 이기종 상호작용을 지원하는 논리적 아키텍처를 구축하는 것을 목표로 한다. 이 프레임워크는 제어 가능한 지연 시간 처리, 여러가지 기기 간 실시간 동기화, 모듈형 기기 조합, 그리고 다양한 상호작용 방식을 특징으로 하며, 다양한 교육 콘텐츠의 다양한 요구를 충족할 수 있다. 기존 XR 교육 아키텍처와 산업 응용 프로그램과의 비교 분석을 통해 제안된 프레임워크는 확장성, 사용자 조작성, 협업 교육 효율성 측면에서 큰 잠재력을 보여주며, 보다 포괄적인 XR 교육 프레임워크에 대한 이론적 통찰력과 실질적 지침을 제공하여 고위험 산업 환경에서 팀 협업, 운영 정확성, 안전 관리를 더욱 개선한다.

목차 (Table of Contents)

• List of Contents
• List of Contents i
• List of Tables iv
• List of Figures v
• List of Abbreviations. viii
• Acknowledgment ix
• 1. Introduction 1
• 1.1. Motivation and Background 1
• 1.1.1. Research Motivation 1
• 1.1.2. Research Background 3
• 1.1.3. Research Objectives 4
• 1.2. Overview of XR Technologies 5
• 1.2.1. Definition of VR, AR, MR, and XR 5
• 1.2.2. Technology Development Trends 7
• 1.2.3. Technology Market Environment 8
• 1.3. Challenges in High-Risk Industry Training 10
• 1.3.1. Characteristics of High-Risk Environments 10
• 1.3.2. Limitations of Traditional Training Models 10
• 1.3.3. Related Issues and Research Gaps 12
• 2. Multi-User Collaboration in Safety Training 13
• 2.1. Definition of Multi-User Collaboration 13
• 2.1.1. Information Asymmetry 13
• 2.1.2. Interaction Diversity 14
• 2.1.3. About Collaboration 15
• 2.2. Definition of Safety Training 16
• 2.2.1. Characteristics of Team-Based Training 17
• 2.2.2. Role-Based Division and Communication 18
• 2.3. Proposed Multi-User Cooperation Framework 19
• 2.3.1. Device Roles (VR, AR, MR) 19
• 2.3.2. Cross-Platform Collaboration 20
• 3. Proposed Research Framework 22
• 3.1. Related Literature and Existing Frameworks 22
• 3.1.1. XR Training Frameworks 23
• 3.1.2. Cognitive XR Frameworks 24
• 3.1.3. Metaverse-Based Training Approaches 25
• 3.1.4. Comparative Analysis 26
• 3.2. Communication and Synchronization Structures 29
• 3.2.1. Cross-Device Client–Server Structure 29
• 3.2.2. Centralized Synchronization and Data Flow 30
• 3.2.3. Controllable Delay Simulation Module 31
• 3.3. Heterogeneous Interaction 34
• 3.3.1. Role-Based Interaction Modalities 35
• 3.3.2. Information Exchange Across Devices 36
• 3.3.3. Design Implications 38
• 3.4. Utilizing and Defining “Delay” 39
• 3.4.1. Delay as a Training Parameter 39
• 3.4.2. Implementation of Delay Injection 40
• 3.4.3. Behavioral and System-Level Implications. 41
• 3.5. Related Experiments 42
• 3.5.1. AR–MR–PC Prototype 42
• 3.5.2. VR–AR–PC Marine Rescue Prototype 50
• 3.5.3. Multi-Device Interaction Experiments 55
• 4. Case Studies and Applications 59
• 4.1. Industrial XR Case Studies 59
• 4.1.1. Siemens XR Collaborative Training 59
• 4.1.2. BMW XR Design Collaboration 60
• 4.1.3. Osso VR Surgical Training 61
• 4.1.4. Operator XR – OP-2 / OP-2 Heavy 62
• 4.1.5. Comparative Summary and Synthesis 63
• 4.2. High-Risk Environment Applications 66
• 4.2.1. Industry Demands and Constraints 66
• 4.2.2. Feasibility of XR-Based Collaborative Training 66
• 4.2.3. Alignment with Digital Twin & AI Trends 67
• 5. Conclusion and Future Work 68
• 5.1. Conclusion 68
• 5.1.1. Summary of Findings 68
• 5.1.2. Implications for High-Risk Industry Training 70
• 5.2. Future Work 71
• 5.2.1. System Validation and Large-Scale Field Trials 71
• 5.2.2. AI-Assisted Scenario Generation and Assessment 71
• 5.2.3. Robust Cross-Device Calibration and Drift Compensation 72
• 5.2.4. Delay-Robust Interaction Design 72
• 5.2.5. Framework Indicators and Related Considerations 72
• References 73
• ABSTRACT 76
• 국문요약 78