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Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot
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SYSNO ASEP 0537806 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot Author(s) Věchet, Stanislav (UT-L) RID, ORCID
Krejsa, Jiří (UT-L) RID, ORCID
Chen, K.S. (TW)Number of authors 3 Source Title ENGINEERING MECHANICS 2020. - Brno : Brno University of Technology Institute of Solid Mechanics, Mechatronics and Biomechanics, 2020 / Fuis V. - ISSN 1805-8248 - ISBN 978-80-214-5896-3 Pages s. 508-511 Number of pages 4 s. Publication form Print - P Action International Conference Engineering Mechanics 2020 /26./ Event date 24.11.2020 - 25.11.2020 VEvent location Brno Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords anomalies detection ; system diagnostic ; robot operating system Subject RIV JC - Computer Hardware ; Software OECD category Robotics and automatic control Institutional support UT-L - RVO:61388998 UT WOS 000667956100119 DOI 10.21495/5896-3-508 Annotation Autonomous mobile robots are complex mechatronic machines which consists of numerous hardware and software modules working asynchronously to achieve desired behaviour. As there are many frameworks which helps to overcome the flat learning curve the problem of internal diagnostics arises. While users and developers are able to focus only on solving the high level problem algorithm or methods the internal states of the system is hidden. This helps to separate the users from solving hardware issues, which is helping until everything works properly. We present an algorithm which is able to detect anomalies in time based behaviour of the robot to improve the users confidence that the internal robot framework works correctly and as desired. The algorithm is based on probabilistic patterns detection based on Bayesian probabilistic theory. Workplace Institute of Thermomechanics Contact Marie Kajprová, kajprova@it.cas.cz, Tel.: 266 053 154 ; Jana Lahovská, jaja@it.cas.cz, Tel.: 266 053 823 Year of Publishing 2021
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