This paper aims to design and implement a neuro-evolution for the purpose of an autonomous vehicle moving on the road, where its control is performed by an MLP-type feed-forward artificial neural network. The network perceives the environment and controls the angle of the vehicle's wheels as well as its speed in a 2D simulation environment. The vehicle itself consists of a four-wheeled chassis with rotatable front wheels with its own dynamics. The neural network is represented by a four-layer architecture and its parameters are optimized using a parallel genetic algorithm to minimize the chosen performance criterion for controlling the motion of the vehicle.


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    Title :

    Autonomus Systems Control Design Using Neuro-Evolution


    Contributors:


    Publication date :

    2022-09-11


    Size :

    1348202 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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