Positioning Accuracy Improvement of Laser Navigation Using Unscented Kalman Filter

  • Jungmin Kim
  • Kyunghoon Jung
  • Jaeyong Kim
  • Hajun Song
  • Sungshin Kim
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

This paper presents positioning improvement of a laser navigation system (LNS) using unscented Kalman filter (UKF) for an automatic guided vehicle (AGV). The existing AGVs mainly used a magnetic system or an inductive system as a guidance system. However, those have high cost and difficult maintenance according to change of environment, and can drive only the designated path which sensors are installed on. The laser guidance system is developed to solve those problems, but it has also problems which is slow response time and low accuracy. Therefore, we propose a sensor fusion method for the AGV. The sensors used in this paper are encoders, a gyro and the LNS, and they are fused by UKF. To analyze the performance of the proposed system, we designed a fork-type AGV for ourselves, and performed the experiment that was repeated 5 times under the same working conditions. In experimental results, we verified that the proposed method could improve positioning accuracy of the LNS effectively. In addition, it was appropriate to apply a real AGV system for autonomous driving.

Keywords

unscented Kalman filter laser navigation automatic guided vehicle 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gutsche, R., Laloni, C., Wahl, F.M.: A flexible transport system for industrial environments using global sensor and navigation concepts. Robotics and Autonomous Systems 14(2-3), 85–98 (1995)CrossRefGoogle Scholar
  2. 2.
    Hung, N., Im, J.S., Jeong, S.K., Kim, H.K., Kim, S.B.: Design of a sliding mode controller for an automatic guided vehicle and its implementation. International Journal of Control, Automation, and Systems 8(1), 81–90 (2010)CrossRefGoogle Scholar
  3. 3.
    Vis, I.F.A.: Survey of research in the design and control of automated guided vehicle systems. Eur. J. Oper. Res. 170(3), 677–709 (2006)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Xidias, E.K., Azariadis, P.N.: Mission design for a group of autonomous guided vehicles. Robotics and Autonomous Systems 59(1), 34–43 (2011)CrossRefGoogle Scholar
  5. 5.
    Jia, Z., Balasuriya, A., Challa, S.: Sensor fusion-based visual target tracking for autonomous vehicles with the out-of-sequence measurements solution. Robotics and Autonomous Systems 56(2), 157–176 (2008)CrossRefGoogle Scholar
  6. 6.
    Yang, Z.F., Tsai, W.H.: Viewing corridors as right parallelepipeds for vision-based vehicle localization. IEEE Trans. on Industrial Electronics 46(3), 653–661 (1999)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Schulze, L., Zhao, L.D.: Worldwide Development and Application of Automatic Guided Vehicle Systems. Service Operation and Informatics 2, 164–176 (2007)CrossRefGoogle Scholar
  9. 9.
    Wakaumi, H., Nakamura, K., Matsumura, T.: Development of an automated wheelchair guided by a magnetic ferrite marker lane. Journal of Rehabilitation Research and Development 29(1), 27–34 (1992)CrossRefGoogle Scholar
  10. 10.
    Chan, C.Y.: A system review of magnetic sensing system for ground vehicle control and guidance. California PATH Research Report, UCB–ITS–PRR–2002–20 (2002)Google Scholar
  11. 11.
    Julier, S.J., Durrant-Whyte, H.F.: Navigation and parameter estimation of high speed road vehicles. In: Robotics and Automation Conference, pp. 101–105 (1995)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II. SPIE (1997)Google Scholar
  14. 14.
    Ko, S.I., Choi, J.S., Kim, B.H.: Indoor mobile localization system and stabilization of localization performance using pre-filtering. International Journal of Control, Automation, and Systems 6(2), 204–213 (2008)MathSciNetGoogle Scholar
  15. 15.
    Kim, K.J., Park, C.G.: Non-Symmetric Unscented Transformation with Application to In-Flight Alignment. International Journal of Control, Automation, and Systems 8(4), 776–781 (2010)CrossRefGoogle Scholar
  16. 16.
    Choi, K.S., Lee, S.G.: An Enhanced CSLAM for Multi-robot based on Unscented Kalman Filter. International Journal of Control, Automation, and Systems 10(1), 102–108 (2012)CrossRefGoogle Scholar
  17. 17.
    Laneurit, J., Chapuis, R., Chausse, F.: Accurate vehicle positioning on a numerical map. International Journal of Control, Automation, and Systems 3(1), 15–31 (2005)Google Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jungmin Kim
    • 1
  • Kyunghoon Jung
    • 1
  • Jaeyong Kim
    • 1
  • Hajun Song
    • 1
  • Sungshin Kim
    • 1
  1. 1.Dept. of Electrical EngineeringPusan National UniversityGeumjeongKorea

Personalised recommendations