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A Simple Goal Seeking Navigation Method for a Mobile Robot Using Human Sense, Fuzzy Logic and Reinforcement Learning

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2008)

Abstract

This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method endows the robot the capabilities of obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a fuzzy reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with previous works are provided.

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References

  1. Maaref, H., Barret, C.: Sensor Based Navigation of an Autonomous Mobile Robot in an Indoor Environment. Control Engineering Practice 8, 757–768 (2000)

    Article  Google Scholar 

  2. Latombe, J.-C.: Robot Motion Planning. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  3. Koren, Y., Borenstein, J.: Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation. In: The 1991 IEEE International Conference on Robotics and Automation, Sacramento, California (1991)

    Google Scholar 

  4. Tan, G.-Z., He, H., Sloman, A.: Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots. Acta Automatica 33(3) (2007)

    Google Scholar 

  5. Liu, G., Li, T., Peng, Y., Hou, X.: The ant algorithm for solving robot path planning problem. In: The third IEEE International Conference on Information Technology and Applications, Sydney, vol. 2, pp. 25–27 (2005)

    Google Scholar 

  6. Brooks, R.A.: A robust Layered Control System for a mobile robot. IEEE Trans. On Robotics and Automation RA-2(1), 14–23 (1986)

    Google Scholar 

  7. Suwimonteerabuth, D., Chongstvatana, P.: Online robot learning by reward and punishment for a mobile robot. In: The 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Swizerland (October 2002)

    Google Scholar 

  8. Beom, H.B., Cho, H.S.: A Sensor Based Navigation for a Mobile Robot using Fuzzy Logic and Reinforcement Learning. IEEE Trans. On Systems, Man, and Cybernetics 25(3) (March 1995)

    Google Scholar 

  9. Ye, C., Nelson, H.C.Y.: A Fuzzy Controller with Supervised Learning Assisted Reinforcement Learning Algorithm for Obstacle Ovoidance. IEEE Trans. On Systems, Man, and Cybernetics 33(1) (February 2003)

    Google Scholar 

  10. Zavlangas, P.G., Tzafestas, S.G.: Motion control for Mobile Robot Obstacle Avoidance and Navigation: A Fuzzy Logic-Based Approach. Systems Analysis Modelling Simulation 43(12), 1625–1637 (2003)

    Article  Google Scholar 

  11. Dahmani, Y., Benyettou, A.: Fuzzy Reinforcement Rectilinear Trajectory Learning. Journal of Applied Science 4(3), 388–392 (2004)

    Article  Google Scholar 

  12. Kermiche, S., Saidi, M.L., Abbassi, H.A.: Gradient Descent Adjusting Takagi-Sugeno Controller for a Navigation of Robot Manipulator. Journal of Engineering and Applied Science 1(1), 24–29 (2006)

    Google Scholar 

  13. Glorennec, P.Y., Jouffe, L.: A Reinforcement Learning Method For Autonomous Robot. In: The Fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany (September 1996)

    Google Scholar 

  14. Gu, D., Hu, H.: Accuracy Based Fuzzy Q-Learning for Robot Behaviours. In: Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE-FUZZY 2004), Budapest, 25-29 July (2004)

    Google Scholar 

  15. Yager, R.R., Filev, D.P.: Essential of fuzzy modelling and control. John Wily & Sons inc (1994)

    Google Scholar 

  16. Boubertakh, H., Glorennec, P.Y.: Optimization of a Fuzzy PI Controller Using Reinforcement Learning. In: The 2006 IEEE Internationnal Conference on Information and Communication Technologies: From Theory to Applications, Damascus, vol. 1, pp. 1657–1662 (2006)

    Google Scholar 

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Ignac Lovrek Robert J. Howlett Lakhmi C. Jain

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© 2008 Springer-Verlag Berlin Heidelberg

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Boubertakh, H., Tadjine, M., Glorennec, PY. (2008). A Simple Goal Seeking Navigation Method for a Mobile Robot Using Human Sense, Fuzzy Logic and Reinforcement Learning. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_84

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  • DOI: https://doi.org/10.1007/978-3-540-85563-7_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85562-0

  • Online ISBN: 978-3-540-85563-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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