In this paper, an alternative training approach to the EEM-based training method is presented and a fuzzy reactive navigation architecture is described. The new training method is 270 times faster in learning speed; and is only 4% of the learning cost of the EEM method. It also has very reliable convergence of learning; very high number of learned rules (98.8%); and high adaptability. Using the rule base learned from the new method, the proposed fuzzy reactive navigator fuses the obstacle avoidance behavior and goal seeking behavior to determine its control actions, where adaptability is achieved with the aid of an environment evaluator. A comparison of this navigator using the rule bases obtained from the new training method and the EEM method, shows that the new navigator guarantees a solution and its solution is more acceptable. © 1999 IEEE. ; published_or_final_version


    Access

    Download


    Export, share and cite



    Title :

    An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning


    Contributors:
    Yung, NHC (author) / Ye, C (author)

    Publication date :

    1999-01-01


    Remarks:

    29



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Vehicle-mounted intelligent combined navigator

    CHEN ZHIJIE / HU JIANJIAN | European Patent Office | 2021

    Free access

    Combined adjustable vehicle-mounted intelligent navigator

    SHI WEIHUA / YE JINPENG / LI LIANG | European Patent Office | 2023

    Free access

    Vehicle navigator

    European Patent Office | 2015

    Free access

    Intelligent vehicle-mounted navigator convenient to clean

    WANG HONGXIN / XIAO FEI / ZHANG PENG et al. | European Patent Office | 2022

    Free access

    Detachable vehicle-mounted intelligent navigator fixing device

    CHEN YIWEN | European Patent Office | 2021

    Free access