A mobile robot to navigate purposefully from a start location to a target location, needs three basic requirements: sensing, learning, and reasoning. In the existing system, the mobile robot navigates in a known environment on a predefined path. However, the pervasive presence of uncertainty in sensing and learning, makes the choice of a suitable tool of reasoning and decision-making that can deal with incomplete information, vital to ensure a robust control system. This problem can be overcome by the proposed navigation method using fuzzy support vector machine (FSVM). It proposes a fuzzy logic-based support vector machine (SVM) approach to secure a collision-free path avoiding multiple dynamic obstacles. The navigator consists of an FSVM-based collision avoidance. The decisions are taken at each step for the mobile robot to attain the goal position without collision. Fuzzy-SVM rule bases are built, which require simple evaluation data rather than thousands of input-output training data. The effectiveness of the proposed method is verified by a series of simulations and implemented with a microcontroller for navigation.Defence Science Journal, 2010, 60(4), pp.387-391, DOI:http://dx.doi.org/10.14429/dsj.60.496


    Access

    Download


    Export, share and cite



    Title :

    Fuzzy Support Vector Machine-based Multi-agent Optimal Path


    Contributors:

    Publication date :

    2010-07-09


    Remarks:

    doi:10.14429/dsj.60.496
    Defence Science Journal; Vol 60 No 4: Machine Learning and Pattern Recognition; 387-391 ; 0976-464X ; 0011-748X



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629




    Objective Speech Quality Evaluation Based on Fuzzy Multi-Class Support Vector Machine

    Jun, Z. / Deyun, Z. / Peng, F. | British Library Online Contents | 2006


    Fuzzy Support Vector Machine with a New Fuzzy Membership Function

    Tang, H. / Liao, Y. / Sun, F. | British Library Online Contents | 2009


    Optimal database combining with Multi Output Support Vector Machine for Eddy Current Testing

    Chelabi, Mohamed / Hacib, Tarik / Belli, Zoubida et al. | IEEE | 2014


    Multi-Component Gas Analyzing Based on Support Vector Machine

    Jipeng, L. / Junhua, L. | British Library Online Contents | 2005


    An Effective Fuzzy Support Vector Machine based Seizure Detection Model using EEG Signals

    Suguna, P. / Kirubagari, B. / Umamaheswari, R. | IEEE | 2020