A Tracking Algorithm for Autonomous Navigation of AGVs: Federated Information Filter

  • Kim, Yong-Shik (School of Mechanical Engineering, Pusan National University) ;
  • Hong, Keum-Shik (Dept. of Mechanical and Intelligent Systems Engineering, Pusan National University)
  • Published : 2004.09.01


In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.



  1. Adam, A., Rivlin, E., and Rotstein, H. (1999), ;'Fusion of fixation and odometry for vehicle navigation', IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 29, No.6, pp. 593-603
  2. Bar-Shalom, Y. and Li, X. (1995), Multitarget -Multisensor Tracking: Principles and Techniques, YBS, Storrs, CT
  3. Bar-Shalom, Y., Li, X., and Kirubarajan, T. (2001), Estimation with Applications to Tracking and Navigation, John Wiley & Sons, INC, New York
  4. Carelli, R. and Freire, E. O. (2003), 'Corridor navigation and wall-following stable control for sonar-based mobile robots', Robotics and Autonomous Systems, Vol. 45, No. 3-4, pp. 235-247
  5. Carlson, N. A. and Berarducci, M. P. (1994), 'Federated Kalman filter simulation results', Journal of The Institute of Navigation, Vol. 41, No.3, pp. 297-321
  6. Chang, K. C., Zhi, T., and Saha, R. K. (2002), 'Performance evaluation of track fusion with information matrix filter', IEEE Trans. on Aerospace and Electronic Systems, Vol. 38, No.2, pp. 455-466
  7. Kim, D. W., Park, Y. C., and Chung, H. Y. (2001), 'Design of an absolute location and position measuring system for a mobile robot', KSME InternationalJournal, Vol. 15, No. 10, pp. 1369-1379
  8. Lim, J. H. and Kang, C. U. (2002), 'Grid-based localization of a mobile robot using sonar sensors', KSME Int. Journal, Vol. 16, No.3, pp. 302-309
  9. Madhavan, R. and Durrant-Whyte, H. F. (2004), 'Natural landmark-based autonomous vehicle navigation', Robotics and Autonomous Systems, Vol. 46, pp. 79-95
  10. Mutambara, A. G. O. (1998), Decentralized Estimation and Control for Multisensor Systems, CRC Press, Boca Raton
  11. Nebot, E. M. and Durrant-Whyte, H. (1999), 'A high integrity navigation architecture for outdoor autonomous vehicles', Robotics and Autonomous Systems, Vol. 26, No. 2-3, pp. 81-97
  12. Paik, B. S. and Oh, J. H. (2000), 'Gain fusion algorithm for decentralized parallel Kalman filters', IEE Proc.- Control Theory and Applications, Vol. 147, No.1, pp.97-103
  13. Park, T. J., Ahn, J. W., and Han, C. S. (2002), 'A path generation algorithm of an automatic guided vehicle using sensor scanning method', KSME International Journal, Vol. 16, No.2, pp. 137-146

Cited by

  1. Robust stabilization of a wheeled vehicle: Hybrid feedback control design and experimental validation vol.24, pp.2, 2010,