Resumen de Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot

Héctor Quintián Pardo , Esteban Jove Pérez , José Luis Calvo-Rolle , Nuño Basurto Hornillos, Carlos Cambra Baseca , Álvaro Herrero Cosío , Emilio Santiago Corchado Rodríguez

• The detection of anomalies (affecting hardware or software) is an open challenge for cyber-physical systems in general and robots in particular. Physical anomalies related to the hardware components of such systems have been widely researched. However, scant attention has been devoted so far to study the anomalies affecting the software components. In order to bridge this gap, the present paper proposes the application of different classifiers to a robot performance dataset for the first time. The applied supervised models are targeted at detecting synthetically-induced software anomalies, having a detrimental impact on the performance of a collaborative robot. Obtained results demonstrate that the applied Machine Learning models can successfully address the target problem, with acceptable detection rates.