2026-06-02T05:10:43Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/68472022-09-15T00:05:16Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Basurto Hornillos, Nuño Cambra Baseca, Carlos Herrero Cosío, Álvaro 2022-09-14T08:23:19Z 2022-09-14T08:23:19Z 2022-05 1433-7541 http://hdl.handle.net/10259/6847 10.1007/s10044-021-01053-0 1433-755X 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. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. application/pdf eng Springer Pattern Analysis and Applications. 2022, V. 25, n. 2, p. 271-283 https://doi.org/10.1007/s10044-021-01053-0 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Smart robotics Component-based robot software Performance monitoring Anomaly detection Machine learning Unsupervised visualization Clustering Exploratory projection pursuit Informática Computer science A visual tool for monitoring and detecting anomalies in robot performance info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Pattern Analysis and Applications 25 2 271 283