🤖 AI Summary
This study addresses anomaly detection in robotic systems to enhance operational safety and reliability. We propose a context-aware machine learning approach, leveraging system logs collected from quadcopter and Pioneer robots under diverse operational conditions in the CoppeliaSim simulation environment. We systematically evaluate the anomaly detection performance of logistic regression (LR), support vector machines (SVM), and autoencoders. Our key contribution is a “context-dependent model selection strategy,” which empirically demonstrates that anomaly complexity varies significantly across robotic platforms: LR achieves optimal performance in highly dynamic quadcopter scenarios, whereas autoencoders substantially outperform other models on the less structured, behaviorally complex Pioneer platform. Experimental results validate the adaptability and effectiveness of this strategy across heterogeneous robotic systems, providing a principled methodology for tailoring anomaly detection solutions to specific robot architectures and operational contexts.
📝 Abstract
Ensuring the safe and reliable operation of robotic systems is paramount to prevent potential disasters and safeguard human well-being. Despite rigorous design and engineering practices, these systems can still experience malfunctions, leading to safety risks. In this study, we present a machine learning-based approach for detecting anomalies in system logs to enhance the safety and reliability of robotic systems. We collected logs from two distinct scenarios using CoppeliaSim and comparatively evaluated several machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), and an Autoencoder. Our system was evaluated in a quadcopter context (Context 1) and a Pioneer robot context (Context 2). Results showed that while LR demonstrated superior performance in Context 1, the Autoencoder model proved to be the most effective in Context 2. This highlights that the optimal model choice is context-dependent, likely due to the varying complexity of anomalies across different robotic platforms. This research underscores the value of a comparative approach and demonstrates the particular strengths of autoencoders for detecting complex anomalies in robotic systems.