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Enhanced SLAM for a Mobile Robot using Unscented Kalman Filter and Radial Basis Function Neural Network

Author Affiliations

  • 1Young Researchers Club, Qazvin Branch, Islamic Azad University, Qazvin, IRAN

Res. J. Recent Sci., Volume 2, Issue (2), Pages 69-75, February,2 (2013)

Abstract

This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) for a mobile robot to compensate for the Unscented Kalman Filter (UKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Radial Basis Function (RBF) and UKF which is a milestone for SLAM applications. A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. The proposed approach, based on a Hybrid filter, has some advantages in handling a robot with nonlinear motions because of the learning property of the RBF neural network. The simulation results show the effectiveness of the proposed algorithm comparing with an UKF based SLAM and also it shows that in larger environments has good efficiency.

References

  1. Kim J.M., Kim Y.T. and Kim S.S., An accurate localization for mobile robot using extended Kalman filter and sensor fusion, IEEE International Joint Conference on Neural Networks, 2928-2933 (2008)
  2. Kyung-Sik Choi, Suk-Gyu Lee.: Enhanced SLAM for a Mobile Robot using Extended Kalman Filter and Neural Networks, International Journal of Precision Engineering and Manufacturing, 112), 255-264 (2010)
  3. Zhu J., Zheng N., Yuan Z., Zhang Q. and Zhang X., Unscented SLAM with conditional iterations, 2009 IEEE Intelligent Vehicles Symposium, 134-139 (2009)
  4. Vafaeesefat A., Optimum Creep Feed Grinding Process Conditions for Rene 80 Supper Alloy Using Neural network, Int. J. Precis. Eng. Manuf., 10(3), 5-11 (2009)
  5. Houshangi N. and Azizi F., Accurate mobile robot position determination using unscented Kalman filter, 2005 Canadian Conference on Electrical and Computer Engineering, 846-851 (2005)
  6. Zhan R. and Wan J., Neural Network-Aided Adaptive Unscented Kalman Filter for Nonlinear State Estimation, IEEE Signal Processing Letters, 13(7), 445-448 (2006)
  7. Choi M.Y., Sakthivel R. and Chung W.K., Neural network aided extended Kalman filter for SLAM problem, IEEE International Conference on Robotics and Automation, 1686-1690 (2007)
  8. Hu Y.H. and Hwang J.N., Handbook of Neural Network Signal Processing. CRC Press, 3.1-3.23. (2001)
  9. Zu L., Wang H.K. and Yue F., Artificial neural networks for mobile robot acquiring heading angle. Proceedings of the Third Intemational Conference on Machine Laming and Cybemetics, 26-29 (2004)
  10. Julier S.J. and Uhlmann J.K., A New Extension of Kalman Filter to Nonlinear Systems, Proceedings of AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Contro, (1997)
  11. Scott F., Page.: Multiple-Opbject sensor Managment and optimization, PHD thesis, in the faculty of Engineering, Science and mathematic School of Electronics and Computer science (2009)
  12. Pathak Sunil, Turbocharging and Oil Techniques in Light Motor Vehicles, Research Journal of Recent Sciences, 1(1), 60-65 (2012)
  13. Farshid Hemmati, Influence of Internal Waves on Underwater Acoustic Propagation, Research Journal of Recent Sciences, 1(1), 73-76 (2012)
  14. Patil Pallavi and Ingle Vikal, Obtaining a high Accurate Fault Classification of Power Transformerbased on Dissolved Gas Analysis using ANFIS, Research Journal of Recent Sciences, 1(2), 97-99 (2012)
  15. Agbo G.A., Ibeh G.F. and Ekpe J.E., Estimation of Global Solar Radiation at Onitsha with Regression Analysis and Artificial Neural Network Models, Research Journal of Recent Sciences, 1(6), 27-31 (2012)
  16. Nagadeepa N., Enhanced Bluetooth Technology to Assist the High Way Vehicle Drivers, Research Journal of Recent Sciences, 1(8), 82-85 (2012)
  17. Bailey T., http://www-personal.acfr.usyd.edu.au/tbailey/ software/ index.html, June (2008)