AI-driven Resource Anomaly Detection Framework for Robotic CI/CD Pipeline in the Edge-Cloud Continuum

Abstract

Continuous integration and continuous delivery (CI/CD) can improve the reliability of robotic software. Practical pipelines still struggle with consistent simulation, runtime anomaly detection, and actionable feedback. This project integrates unsupervised anomaly detection into a ROS 2 CI/CD pipeline and evaluates it in Webots over six scenarios ranging from basic tasks to complex configurations. The system collects CPU and memory usage at 2 Hz with derived rolling and slope features, applies an Isolation Forest model, and displays results in a Streamlit dashboard. We deploy the pipeline to an edge virtual machine for continuous anomaly detection and introduce an early warning layer that predicts anomalies before they occur and sends recommendations to mitigate. The pipeline detects realistic resource anomalies in various scenarios. Results show that the proposed system performs competitively in terms of precision, recall, and F1-scores as compared to the baselines. The work also addresses practical and ethical issues by highlighting the importance of human oversight when interpreting AI decisions. Future work includes dynamic tuning of the framework parameters, online learning and drift handling, and validation on physical hardware.