In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.


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    Title :

    Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming


    Contributors:


    Publication date :

    2003-06-01


    Size :

    243457 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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