RT info:eu-repo/semantics/article
T1 A visual tool for monitoring and detecting anomalies in robot performance
A1 Basurto Hornillos, Nuño
A1 Cambra Baseca, Carlos
A1 Herrero Cosío, Álvaro
K1 Smart robotics
K1 Component-based robot software
K1 Performance monitoring
K1 Anomaly detection
K1 Machine learning
K1 Unsupervised visualization
K1 Clustering
K1 Exploratory projection pursuit
K1 Informática
K1 Computer science
AB In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.
PB Springer
SN 1433-7541
YR 2022
FD 2022-05
LK http://hdl.handle.net/10259/6847
UL http://hdl.handle.net/10259/6847
LA eng
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-nov-2024