SOLVING THE FAILING TRACK MARKER PROBLEM IN AUTOMATED GUIDED VEHICLE SYSTEMS – A CASE STUDY
Tomasz Lewowski
tomasz.lewowski@ratelware.comWrocław University of Science and Technology, Faculty of Computer Science and Management, Department of Applied Informatics, Wrocław, Poland; Octant sp. z o.o., www.octant.pl (Poland)
http://orcid.org/0000-0003-4897-1263
Abstract
This paper is a case study of the development of a localization and positioning subsystem of an Automated Guided Vehicle-based transportation system. The described system uses primarily RFID markers for localization. In some deployments, those markers occasionally fail, mostly due to being crushed by cargo platforms operated by a human or due to internal defects. Those failures are not common enough to warrant switching from marker-based localization to a more sophisticated technique, but they require additional effort from maintenance staff. In this case study, we present our solution to this problem – a self-tuning algorithm that is able to detect marker failures and, in most cases, keep the system operational. The paper briefly discusses business circumstances under which such a solution is reasonable and then describes in detail the entire technical process, including data acquisition, verification, algorithm development and finally, the result of deploying the system in production.
Keywords:
Industrial control, Unmanned vehicles, Fault tolerance, MaintenanceReferences
Bandyopadhyay S.: Intelligent Vehicles and Materials Transportation in the Manufacturing Sector: Emerging Research and Opportunities. IGI Global 2017.
DOI: https://doi.org/10.4018/978-1-5225-3064-0
Google Scholar
Campion G., Bastin G., D’Andréa-Novel B.: Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. Proceedings IEEE International Conference on Robotics and Automation (IEEE) 1993, [http://doi.org/10.1109/ROBOT.1993.292023].
DOI: https://doi.org/10.1109/ROBOT.1993.292023
Google Scholar
Clausing D.: Taguchi methods to improve the development process. IEEE International Conference on Communications – Spanning the Universe 2, 1988 826–832, [http://doi.org/10.1109/ICC.1988.13674].
DOI: https://doi.org/10.1109/ICC.1988.13674
Google Scholar
Deming W. E.: Sample Design in Business Research. Wiley-Interscience, 1990.
Google Scholar
Dreyfus S.: An Appraisal of Some Shortest-Path Algorithms. Operations Research 17(3), 1969, [http://doi.org/10.1287/opre.17.3.395].
DOI: https://doi.org/10.1287/opre.17.3.395
Google Scholar
Ismail A. H., Ramli H. R., Ahmad M. H., Marhaban M. H.: Vision-based system for line following mobile robot. IEEE Symposium on Industrial Electronics & Applications 2009, 642–645, [http://doi.org/10.1109/ISIEA.2009.5356366].
DOI: https://doi.org/10.1109/ISIEA.2009.5356366
Google Scholar
Lee J-W., Choi S-U., Lee C-H., Lee Y-J., Lee K-S: A study for AGV steering control and identification using vision system. IEEE International Symposium on Industrial Electronics Proceedings 3, 2001, 1575–1578 (Cat. No. 01TH8570), [http://doi.org/10.1109/ISIE.2001.931941].
DOI: https://doi.org/10.1109/ISIE.2001.931941
Google Scholar
Leitner S. H., Mahnke W.: OPC UA–service-oriented architecture for industrial applications. ABB Corporate Research Center 48, 2006, 61–66.
Google Scholar
Li L., Schultze L.: Comparison and Evaluation of SLAM Algorithms for AGV Navigation. F.-J. Villmer, E. Padoano (Eds.): Department of Production Engineering and Management. Production Engineering and Management. Lemgo 2018.
Google Scholar
Pang Y., De La Cruz A. L., Lodewijks G.: Bipolar magnetic positioning system for automated guided vehicles. IEEE Intelligent Vehicles Symposium 2008, 883–888, [http://doi.org/10.1109/IVS.2008.4621228].
DOI: https://doi.org/10.1109/IVS.2008.4621228
Google Scholar
Park J., Kim J. Y., Kim B., Kim S.: Global Map Generation using LiDAR and Stereo Camera for Initial Positioning of Mobile Robot. International Conference on Information and Communication Technology Robotics (ICT-ROBOT), 2018, 1–4, [http://doi.org/10.1109/ICT-ROBOT.2018.8549897].
DOI: https://doi.org/10.1109/ICT-ROBOT.2018.8549897
Google Scholar
Park J., Lee J., Park Y., Kim S. W.: AGV parking system based on tracking landmark. 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 2009, 340–343 [http://doi.org/10.1109/ECTICON.2009.5137022].
DOI: https://doi.org/10.1109/ECTICON.2009.5137022
Google Scholar
Quan S., Chen J.: AGV Localization Based on Odometry and LiDAR. 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing 2019, 483–486, [http://doi.org/10.1109/WCMEIM48965.2019.00102].
DOI: https://doi.org/10.1109/WCMEIM48965.2019.00102
Google Scholar
Ray K. A., Gupta M., Behera L., Jamshidi M.: Sonar based Autonomous Automatic Guided Vehicle (AGV) navigation. IEEE International Conference on Systems and System Engineering 2008, 1–6,[http://doi.org/10.1109/SYSOSE.2008.4724179].
DOI: https://doi.org/10.1109/SYSOSE.2008.4724179
Google Scholar
Wiklund U., Andersson U., Hyyppä K.: AGV navigation by angle measurements. Automated guided vehicle systems: Proceedings of the 6th International Conference 1988, 199–212.
Google Scholar
Wang D., Low C. B.: Modeling Skidding and Slipping in Wheeled Mobile Robots: Control Design Perspective. IEEE/RSJ International Conference on Intelligent Robots and Systems 2006, 1867–1872,[http://doi.org/10.1109/IROS.2006.282309].
DOI: https://doi.org/10.1109/IROS.2006.282309
Google Scholar
Authors
Tomasz Lewowskitomasz.lewowski@ratelware.com
Wrocław University of Science and Technology, Faculty of Computer Science and Management, Department of Applied Informatics, Wrocław, Poland; Octant sp. z o.o., www.octant.pl Poland
http://orcid.org/0000-0003-4897-1263
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