Abstract
This paper is represented to research of method to improve the performance of magnetic guide sensor using Kalman filter. The magnetic guide sensor is calculating the center position of the AGV (Automatic Guided Vehicle) by to measure the magnetic information of a magnetic substance that is magnetic tape, magnet spot. The existing magnetic guide sensor is the device that calculates the center position of the AGV using the magnetic force of the measured data that is one pole to measure each axis at the histogram algorithm. But, the existing method is unfit for requiring the high accuracy such as industrial setting because of interference between sensors and the effect by disturbance. Therefore, in this paper proposed method that increases the position accuracy of the magnetic guide sensor using Kalman filter. To verify the proposed method, we use the AGV to install magnetic guide sensor. And it compare the positioning accuracy of the propose method and the commercialized magnetic guide sensor. As a result, the proposed method was found 24.78% to improve the positioning accuracy of the proposed method than that of the commercialized magnetic guide sensor.
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References
Evers, J.J.M., Koppers, S.A.J.: Automated guided vehicle traffic control at a container terminal. Transportation Research Part A: Policy and Practice 30, 21–34 (1996)
Setlur, P., Wagner, J.R., Dawson, D.M., Braganza, D.: A trajectory tracking steer-by-wire control system for ground vehicles. IEEE Trans. on Vehicular Technology 55(1), 76–85 (2006)
Schulze, L., Zhao, L.D.: Worldwide Development and Application of Automatic Guided Vehicle Systems. Service Operation and Informatics 2, 164–176 (2007)
Caruso, M.J., Bratland, T., Smith, C.H., Schneider, R.: A New Perspective on Magnetic Field Sensing. Sensor Magazine 15(12), 34–46 (1998); California PATH Research Report, UCB-ITS-PRR-2003-8 (March 2003)
Ryoo, Y.J., Kim, E.S., Lim, Y.C.: Intelligent Positioning System for Magnetic Sensor Based Autonomous Vehicle. In: SCIS & ISIS (2004)
Tsakonas, E.E., Sidiropoulos, N.D., Swami, A.: Optimal Particle Filters for Tracking a Time-Varying Harmonic or Chirp Signal. IEEE Transactions on Signal Processing 56, 4598–4610 (2008)
Carrasco, R., Cipriano, A.: Fuzzy logic based nonlinear kalman filter applied to mobile robots modelling. In: IEEE International Conference on Fuzzy Systems, vol. 3, pp. 25–29 (2004)
Chatterjee, A., Matsuno, F.: A neuro-fuzzy assisted extended kalman filter based approach for simultaneous localization and mapping (slam) problems. IEEE Transactions on Fuzzy Systems 15, 984–997 (2007)
Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F.: A new approach for filtering nonlinear systems. In: Proceedings of the American Control Conference, pp. 1628–1632 (1995)
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Jung, E., Kim, J., Cho, H., Lee, J., Kim, S. (2013). Improvement of Position Accuracy of Magnetic Guide Sensor Using Kalman Filter. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_78
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DOI: https://doi.org/10.1007/978-3-642-33926-4_78
Publisher Name: Springer, Berlin, Heidelberg
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