Abstract
Autonomous robots are one of the critical components in modern manufacturing systems. For this reason, the uninterrupted operation of robots in manufacturing is important for the sustainability of autonomy. Detecting possible fault symptoms that may cause failures within a work environment will help to eliminate interrupted operations. When supervised learning methods are considered, obtaining and storing labeled, historical training data in a manufacturing environment with faults is a challenging task. In addition, sensors in mobile devices such as robots are exposed to different noisy external conditions in production environments affecting data labels and fault mapping. Furthermore, relying on a single sensor data for fault detection often causes false alarms for equipment monitoring. Our study takes requirements into consideration and proposes a new unsupervised machine-learning algorithm to detect possible operational faults encountered by autonomous mobile robots. The method suggests using an ensemble of multi-sensor information fusion at the decision level by voting to enhance decision reliability. The proposed technique relies on dissimilarity-based sensor data segmentation with an adaptive threshold control. It has been tested experimentally on an autonomous mobile robot. The experimental results show that the proposed method is effective for detecting operational anomalies. Furthermore, the proposed voting mechanism is also capable of eliminating false positives in case of a single source of information is utilized.
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The datasets generated and analysed during the current study are not publicly available at this moment.
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Acknowledgements
This research is supported in part by the Scientific and Technical Research Council of Turkey (TUBITAK) under 2232 International Fellowship for Outstanding Researchers Program with the grant number 118C252.
Funding
Funding was provided by Scientific and Technical Research Council of Turkey (TUBITAK) under 2232 International Fellowship for Outstanding Researchers Program with the grant number 118C252. Metin Yılmaz, one of the authors, is a 100/2000 Council of Higher Education (COHE) PhD Scholarship student.
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Conceptualization, A.Y. and E.C; methodology, M.K. and M.Y.; software, M.K. and M.Y.; investigation, E.C., M.K. and M.Y.; resources, E.C.; writing-original draft preparation, M.K. and M.Y.; writing-review and editing, A.Y. and E.C.; supervision, A.Y.; project administration, E.C. All authors have read and agreed to the published version of the manuscript.
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Kasap, M., Yılmaz, M., Çinar, E. et al. Unsupervised dissimilarity-based fault detection method for autonomous mobile robots. Auton Robot 47, 1503–1518 (2023). https://doi.org/10.1007/s10514-023-10144-2
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DOI: https://doi.org/10.1007/s10514-023-10144-2