Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot

0537806 - ÚT 2021 RIV CZ eng C - Conference Paper (international conference)
Věchet, Stanislav - Krejsa, Jiří - Chen, K.S.
Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot.
ENGINEERING MECHANICS 2020. Brno: Brno University of Technology Institute of Solid Mechanics, Mechatronics and Biomechanics, 2020 - (Fuis, V.), s. 508-511. ISBN 978-80-214-5896-3. ISSN 1805-8248.
[International Conference Engineering Mechanics 2020 /26./. Brno (CZ), 24.11.2020-25.11.2020]
Grant - others:AV ČR(CZ) MOST-20-06
Program: Bilaterální spolupráce
Institutional support: RVO:61388998
Keywords : anomalies detection * system diagnostic * robot operating system
OECD category: Robotics and automatic control

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.
Permanent Link: http://hdl.handle.net/11104/0316605