针对移动机器人边缘调度，提出更高效的CODECO方案。
A CODECO Case Study and Initial Validation for Edge Orchestration of Autonomous Mobile Robots
- 用CODECO替代Kubernetes，优化移动机器人的资源调度。
- CPU消耗降低，通信更稳定，内存多用10-15%。
- 适合资源受限的移动机器人系统部署。
自主移动机器人（AMRs）越来越多地在边缘-云连续体中采用容器化微服务。尽管Kubernetes是此类系统的事实标准编排器，但其对稳定网络、同质资源和充足计算能力的假设，在移动且资源受限的机器人环境中并不完全成立。本文描述了一个智能制造中AMR的案例研究，并在受控的KinD环境中对CODECO编排与标准Kubernetes进行了初步对比。评估指标包括Pod部署与删除时间、CPU和内存使用率、以及跨Pod数据传输速率。结果表明，CODECO在降低CPU消耗、实现更稳定的通信模式方面表现更好，代价是带来适度的内存开销（10-15%）以及由于安全叠加层初始化导致的轻微延迟增加。
原文摘要 · Abstract (English)
Autonomous Mobile Robots (AMRs) increasingly adopt containerized micro-services across the Edge-Cloud continuum. While Kubernetes is the de-facto orchestrator for such systems, its assumptions of stable networks, homogeneous resources, and ample compute capacity do not fully hold in mobile, resource-constrained robotic environments. This paper describes a case study on smart-manufacturing AMRs and performs an initial comparison between CODECO orchestration and standard Kubernetes using a controlled KinD environment. Metrics include pod deployment and deletion times, CPU and memory usage, and inter-pod data rates. The observed results indicate that CODECO offers reduced CPU consumption and more stable communication patterns, at the cost of modest memory overhead (10-15%) and slightly increased pod lifecycle latency due to secure overlay initialization.
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