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Monitoring and Controlling Speed for an Autonomous Mobile Platform Based on the Hall Sensor

  • Adam Ziebinski
  • Markus Bregulla
  • Marcin Fojcik
  • Sebastian Kłak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

Cyber Physical Systems are often used in the automotive industry as embedded systems for constructing Advanced Driver Assistance Systems. Further development of current applications and the creation of new applications for vehicle and mobile platforms that are based on sensor fusion are essential for the future. While ADAS are used to actively participate in the controlling a vehicle, they can also be used to control mobile platforms in industry. In the article, the results of tests of different rates of data acquisition from Hall sensors to measure speed for mobile platform are presented. The purpose of the research was to determine the optimal platform parameter to indicate the refresh frequency in such a way that the measurements obtained from a Hall sensor will be reliable and will require less of the available computing power. Additionally, the results from investigations of the precise movement for a specified distance using a Hall sensor for a mobile platform are presented.

Keywords

ADAS CPS Data acquisition Sensor fusion Hall sensor 

Notes

Acknowledgements

This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (grant agreement no: 612207) and research work financed from funds for science in years 2016-2017 allocated to an international co-financed project (grant agreement no: 3491/7.PR/15/2016/2).

References

  1. 1.
    Cyber-Physical Systems (CPS) (NSF17529) | NSF - National Science Foundation. https://www.nsf.gov/pubs/2017/nsf17529/nsf17529.htm
  2. 2.
    Poovendran, R.: Cyber-physical systems: close encounters between two parallel worlds [point of view]. Proc. IEEE 98, 1363–1366 (2010)CrossRefGoogle Scholar
  3. 3.
    Cupek, R., Huczala, L.: Passive PROFIET I/O OPC DA server. Presented at the IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2009 (2009)Google Scholar
  4. 4.
    Maka, A., Cupek, R., Rosner, J.: OPC UA object oriented model for public transportation system. Presented at the 2011 Fifth UKSim European Symposium on Computer Modeling and Simulation (EMS) (2011)Google Scholar
  5. 5.
    Baheti, R., Gill, H.: Cyber-physical systems. Impact Control Technol. 12, 161–166 (2011)Google Scholar
  6. 6.
    Flak, J., Gaj, P., Tokarz, K., Wideł, S., Ziębiński, A.: Remote monitoring of geological activity of inclined regions – the concept. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2009. CCIS, pp. 292–301. Springer, Berlin (2009). doi: 10.1007/978-3-642-02671-3_34CrossRefGoogle Scholar
  7. 7.
    Wang, Y., Vuran, M.C., Goddard, S.: Cyber-physical systems in industrial process control. ACM SIGBED Rev. 5, 1–2 (2008)CrossRefGoogle Scholar
  8. 8.
    Thompson, C., White, J., Dougherty, B., Schmidt, D.C.: Optimizing mobile application performance with model-driven engineering. In: Lee, S., Narasimhan, P. (eds.) SEUS 2009. LNCS, vol. 5860, pp. 36–46. Springer Berlin Heidelberg, Berlin (2009). doi: 10.1007/978-3-642-10265-3_4CrossRefGoogle Scholar
  9. 9.
    Fleming, W.J.: Overview of automotive sensors. IEEE Sens. J. 1, 296–308 (2001)CrossRefGoogle Scholar
  10. 10.
    Ziebinski, A., Cupek, R., Grzechca, D., Chruszczyk, L.: Review of advanced driver assistance systems (ADAS). Presented at the 13th International Conference on Computer Methods Science Engineering (2017)Google Scholar
  11. 11.
    el Popovic, R., Randjelovic, Z., Manic, D.: Integrated Hall-effect magnetic sensors. Sens. Actuators A Phys. 91, 46–50 (2001)CrossRefGoogle Scholar
  12. 12.
    Proca, A.B., Keyhani, A.: Identification of variable frequency induction motor models from operating data. IEEE Trans. Energy Convers. 17, 24–31 (2002)CrossRefGoogle Scholar
  13. 13.
    Budzan, S., Kasprzyk, J.: Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications. Opt. Lasers Eng. 77, 230–240 (2016)CrossRefGoogle Scholar
  14. 14.
    Grzechca, D., Wrobel, T., Bielecki, P.: Indoor location and identification of objects with video surveillance system and WiFi module (2014)Google Scholar
  15. 15.
    Kobylecki, M., Kania, D., Simos, T.E., Kalogiratou, Z., Monovasilis, T.: Double-tick realization of binary control program. Presented at the AIP Conference Proceedings (2016)Google Scholar
  16. 16.
    Ziębiński, A., Świerc, S.: The VHDL implementation of reconfigurable MIPS processor. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AINSC, pp. 663–669. Springer, Berlin (2009). doi: 10.1007/978-3-642-00563-3_69CrossRefGoogle Scholar
  17. 17.
    Behere, S., Törngren, M.: A functional architecture for autonomous driving. Presented at the Proceedings of the First International Workshop on Automotive Software Architecture (2015)Google Scholar
  18. 18.
    Czyba, R., Niezabitowski, M., Sikora, S.: Construction of laboratory stand and regulation in ABS car system. Presented at the 2013 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE May 2013Google Scholar
  19. 19.
    Rodriguez, F., Emadi, A.: A novel digital control technique for brushless DC motor drives. IEEE Trans. Ind. Electron. 54, 2365–2373 (2007)CrossRefGoogle Scholar
  20. 20.
    Shao, J., Nolan, D., Hopkins, T.: A novel direct back EMF detection for sensorless brushless DC (BLDC) motor drives (2002)Google Scholar
  21. 21.
    Samoylenko, N., Han, Q., Jatskevich, J.: Dynamic performance of brushless DC motors with unbalanced hall sensors. IEEE Trans. Energy Convers. 23, 752–763 (2008)CrossRefGoogle Scholar
  22. 22.
    Pan, C., Chen, L., Chen, L., Jiang, H., Li, Z., Wang, S.: Research on motor rotational speed measurement in regenerative braking system of electric vehicle. Mech. Syst. Signal Process. 66–67, 829–839 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adam Ziebinski
    • 1
  • Markus Bregulla
    • 2
  • Marcin Fojcik
    • 3
  • Sebastian Kłak
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  2. 2.Technische Hochschule IngolstadtIngolstadtGermany
  3. 3.Western Norway University of Applied SciencesFørdeNorway

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