A CODECO Case Study and Initial Validation for Edge Orchestration of Autonomous Mobile Robots

2025-11-11

AI Summary

Kubernetes exhibits limited applicability in edge environments for autonomous mobile robots (AMRs) due to its reliance on stable network connectivity and homogeneous computing resources. Method: This paper proposes CODECO, a lightweight edge orchestration framework that replaces the centralized control plane with a decentralized communication architecture and resource-aware scheduling. Contribution/Results: Evaluated via KinD-based simulation, CODECO reduces average CPU utilization by 32% and transmission jitter by 41% compared to Kubernetes. Although it incurs a modest 12% memory overhead and increases deployment latency by 13%, it demonstrates superior robustness and deployability under resource constraints and high mobility—key characteristics of industrial AMR scenarios. CODECO thus establishes a novel paradigm for microservice orchestration tailored to heterogeneous, dynamic edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Evaluating Kubernetes limitations in mobile robotic edge environments
• Comparing CODECO orchestration with Kubernetes for AMR performance
• Analyzing resource usage and communication stability in container orchestration

Innovation

• CODECO orchestrates robots using Edge-Cloud continuum
• It reduces CPU usage and stabilizes communication patterns
• It trades memory overhead for enhanced security