General guiding model for mobile robots and its complexity reduced neuro-fuzzy approximation

The development of techniques for autonomous mobile robot navigation has been in focus for several decades . The main objectives of this paper are twofold. One is to extend the potential based guiding (PBG) model to a more general form that can be approximated by a common type neuro-fuzzy algorithm. The extended model eliminates the strongly alternating behavior of PBG. The second is to propose a computation complexity reduction method for the general form of the neuro-fuzzy technique. Same examples are given to show the effectiveness of the extended guiding model.

[1]  T. Fukuda,et al.  Adaptation, learning and evolution for intelligent systems , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[2]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[3]  T. Fukuda,et al.  Sensory network for fuzzy controller of a mobile robot , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[4]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[5]  László T. Kóczy,et al.  ARE FUZZY SYSTEMS UNIVERSAL APPROXIMATORS , 1999 .

[6]  Stuart C. Shapiro,et al.  Of Elephants and Men , 1995 .

[7]  Yeung Yam,et al.  Reduction of fuzzy rule base via singular value decomposition , 1999, IEEE Trans. Fuzzy Syst..

[8]  Toshio Fukuda.  Learning, adaptation and evolution for intelligent system , 1997, ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics.

[9]  László T. Kóczy,et al.  Size reduction by interpolation in fuzzy rule bases , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..