2024-03-28T16:17:12Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68472022-09-15T00:05:16Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
A visual tool for monitoring and detecting anomalies in robot performance
Basurto Hornillos, Nuño
Cambra Baseca, Carlos
Herrero Cosío, Álvaro
Smart robotics
Component-based robot software
Performance monitoring
Anomaly detection
Machine learning
Unsupervised visualization
Clustering
Exploratory projection pursuit
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.
2022-09-14
2022-09-14
2022-05
info:eu-repo/semantics/article
1433-7541
http://hdl.handle.net/10259/6847
10.1007/s10044-021-01053-0
1433-755X
eng
Pattern Analysis and Applications. 2022, V. 25, n. 2, p. 271-283
https://doi.org/10.1007/s10044-021-01053-0
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
Springer