云边协同架构让远程机器人操作更实时、可靠且可扩展。
CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
- 采用云-边-机器入协同架构，实现确定性连接与智能计算。
- 结合5G和数字孪生技术，实现实时控制与系统可扩展性提升。
- 适合工业自动化、远程运维等对延迟敏感的场景研究者。
遥感机器人是工业信息物理系统（ICPS）的核心基础，支持跨领域的远程操作。然而，传统云端遥感机器人存在延迟高、可靠性差、可扩展性不足等问题，限制了关键应用中的实时性能。云边遥感（CFTel）基于云边自动化（CFA）范式，构建分布式云-边缘-机器人计算架构，实现确定性连接、确定性互联智能与确定性网络计算。本文综述了近期在CFTel方面的进展，强调其在实现低延迟、可扩展、自主化及人工智能驱动遥感机器人方面的潜力。分析了支撑技术，包括5G超可靠低延迟通信（URLLC）、边缘智能、具身智能与数字孪生。研究表明，CFTel能显著提升实时控制能力、系统可扩展性与自主性，并支持服务化解决方案。同时探讨了延迟约束、网络安全风险、互操作性问题及标准化挑战。本研究为未来遥感机器人领域的研究人员、利益相关方及产业实践者提供基础参考。
原文摘要 · Abstract (English)
Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, enabling deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. This paper synthesizes recent advancements in CFTel, aiming to highlight its role in facilitating scalable, low-latency, autonomous, and AI-driven telerobotics. We analyze architectural frameworks and technologies that enable them, including 5G Ultra-Reliable Low-Latency Communication, Edge Intelligence, Embodied AI, and Digital Twins. The study demonstrates that CFTel has the potential to enhance real-time control, scalability, and autonomy while supporting service-oriented solutions. We also discuss practical challenges, including latency constraints, cybersecurity risks, interoperability issues, and standardization efforts. This work serves as a foundational reference for researchers, stakeholders, and industry practitioners in future telerobotics research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。