Edge computing processes data near its source, reducing latency and enhancing security compared to traditional cloud computing while providing its benefits. This paper explores edge computing for migrating an existing safety-critical robotics use case from an onboard dedicated hardware solution. We propose an edge robotics architecture based on Linux, Docker containers, Kubernetes, and a local wireless area network based on the TTWiFi protocol. Inspired by previous work on real-time cloud, we complement the architecture with a resource management and orchestration layer to help Linux manage, and Kubernetes orchestrate the system-wide shared resources (e.g., caches, memory bandwidth, and network). Our architecture aims to ensure the fault-tolerant and predictable execution of robotic applications (e.g., path planning) on the edge while upper-bounding the end-to-end latency and ensuring the best possible quality of service without jeopardizing safety and security.

翻译：相较于传统云计算，边缘计算在数据源附近处理数据，在降低延迟、增强安全性的同时保留了云计算的优势。本文探讨如何利用边缘计算将现有安全关键型机器人用例从机载专用硬件方案迁移至边缘环境。我们提出一种基于Linux、Docker容器、Kubernetes及采用TTWiFi协议的本地无线局域网的边缘机器人架构。受实时云研究的启发，我们通过资源管理与编排层增强该架构，协助Linux管理系统级共享资源（如缓存、内存带宽及网络），并由Kubernetes进行统一编排。该架构旨在保障机器人应用（如路径规划）在边缘环境中的容错与可预测执行，同时严格限定端到端延迟上限，在不影响安全性的前提下实现最优服务质量。