A CODECO Case Study and Initial Validation for Edge Orchestration of Autonomous Mobile Robots
H. Zhu, T. Samizadeh, R. C. Sofia
TL;DR
The paper tackles the challenge of orchestrating AMR workloads across edge and cloud resources where standard Kubernetes struggles with mobility, network variability, and constrained compute. It introduces CODECO, a telemetry-driven extension to Kubernetes that enables data–compute–network co-optimization, stateful migration, and secure overlay networking to support edge robotics. Through a KinD-based case study, it shows that CODECO reduces CPU usage and stabilizes inter-service communication at the expense of modest memory overhead and longer pod lifecycles, highlighting trade-offs relevant to dynamic AMR deployments. The work demonstrates CODECO's potential to enhance robustness and efficiency in edge robotic fleets and outlines a path toward real-device validation and scalability analyses.
Abstract
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.