Anomaly Detection in Robot Applications: Comparison of Rule-Based and Machine Learning Methods

Abstract

The increasing use of robot applications in various industries requires close monitoring and management of the data generated by these systems. Therefore, it is essential to implement a monitoring system that identifies and reports abnormal situations in robots. The literature differentiates between rule-based and machine learning methods. Rule-based approaches rely on predefined rules to detect deviations from expected behavior. In contrast, machine learning algorithms acquire the ability to learn patterns on their own. This paper evaluates both approaches to determine whether machine learning algorithms can replace or enhance rule-based methods. The evaluation employs an actual production scenario that deals with three induced anomalies: Additional weight, drop of the manipulated object, and reduced speed.