Command Fusion Based Fuzzy Controller Design for Moving Obstacle Avoidance of Mobile Robot

  • Hyunjin Chang
  • Taeseok Jin
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)


In this paper, we proposed a fuzzy inference model for navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using sensor fusion, based on situational command using an ultrasonic sensor. Instead of using “physical sensor fusion” method which generates the trajectory of a robot based upon the environment model and sensory data, “command fusion” method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process.


Fuzzy Control Navigation Mobile robot Obstacle Avoidance 



This research was supported by Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2012 (Grants No. 00045079), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0021054).


  1. 1.
    Er M, Tan TP, Loh SY (2004) Control of a mobile robot using generalized dynamic fuzzy neural networks. Microprocess Microsyst 28:491–498CrossRefGoogle Scholar
  2. 2.
    Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3(1):28–44MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Nair D, Aggarwal JK (1998) Moving obstacle detection from a navigation robot. IEEE Trans Robot Autom 14(3):404–416Google Scholar
  4. 4.
    Bentalba S, ElHajjaji A, Tachid A (1997) Fuzzy control of a mobile robot: a new approach. In: IEEE international conference on control applications, pp 69–72Google Scholar
  5. 5.
    Furuhashi T, Nakaoka K, Morikawa K, Maeda H, Uchikawa Y (1995) A study on knowledge finding using fuzzy classifier system. J Jpn Soc Fuzzy Theory Syst 7(4):839–848Google Scholar
  6. 6.
    Itani H, Furuhashi T (2002) A study on teaching information understanding by autonomous mobile robot. Trans SICE 38(11):966–973Google Scholar
  7. 7.
    Beom HR, Cho HS (1995) A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning. IEEE Trans Syst Man Cybern 25(3):464–477Google Scholar
  8. 8.
    Ohya A, Kosaka A, Kak A (1998) Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. IEEE Trans Robot Autom 14(6):969–978CrossRefGoogle Scholar
  9. 9.
    Tunstel E (2000) Fuzzy-behavior synthesis, coordination, and evolution in an adaptive behavior hierarchy. In: Saffiotti A, Driankov D (eds) Fuzzy logic techniques for autonomous 470 Tunstel, de Oliveira, and Berman vehicle navigation, studies in fuzziness and soft computing (chapter 9). Springer-Verlag, HeidelbergGoogle Scholar
  10. 10.
    Mehrjerdi H, Saad M, Ghommam J (2011) Hierarchical fuzzy cooperative control and path following for a team of mobile robots. IEEE/ASME Trans Mechatron 16(5):907–917CrossRefGoogle Scholar
  11. 11.
    Wang DS, Zhang YS, Si WJ (2011) Behavior-based hierarchical fuzzy control for mobile robot navigation in dynamic environment. In: 2011 Chinese control and decision conference (CCDC), pp 2419–2424Google Scholar
  12. 12.
    Jouffe L (1998) Fuzzy inference system learning by reinforcement method. IEEE Trans Syst Man Cybern Part C 28(3):338–355Google Scholar
  13. 13.
    Leng G, McGinnity TM, Prasad G (2005) An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst 150:211–243MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Nishina T, Hagiwara M (1997) Fuzzy inference neural network. Neurocomputing 14:223–239CrossRefGoogle Scholar
  15. 15.
    Takahama T, Sakai S, Ogura H, Nakamura M (1996) Learning fuzzy rules for bang–bang control by reinforcement learning method. J Jpn Soc Fuzzy Theory Syst 8(1):115–122Google Scholar
  16. 16.
    Tunstel E (1999) Fuzzy behavior modulation with threshold activation for autonomous vehicle navigation. In: 18th international conference of the North American fuzzy information processing society, New York, pp 776–780Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Mechatronics EngineeringDongSeo UniversityBusanKorea

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