2026-06-02T05:10:06Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68472022-09-15T00:05:16Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Basurto Hornillos, Nuño
author
Cambra Baseca, Carlos
author
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.
eng
Atribución 4.0 Internacional
Smart robotics
Component-based robot software
Performance monitoring
Anomaly detection
Machine learning
Unsupervised visualization
Clustering
Exploratory projection pursuit
A visual tool for monitoring and detecting anomalies in robot performance
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6847/1/Basurto-paa_2022.pdf
File
MD5
3d048295980f7e81dd7328daa7ecf11e
3268876
application/pdf
Basurto-paa_2022.pdf