Navigating in an unknown environment is a task commonly accomplished by most animals. Nevertheless, it is not justified to infer that this capacity needs complex reasoning involving abstract geometrical computations. In this paper, the authors' aim is to show that such behavior, including switching between goals, can be simulated by simple artificial neural networks (NN) where no complex computation is performed. The authors present a real development and simulations about a Khepera robot and a simulated system named Prometheus. The authors use a novel neural architecture named PerAc (Perception-Action) which is a systematic way to decompose the control of an autonomous robot in perception and action flows. The authors show that action simplifies the interpretation of perception: each action is a choice and conditions entirely the future of the robot.


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    Title :

    Navigating with an animal brain: a neural network for landmark identification and navigation


    Contributors:
    Gaussier, P. (author) / Zrehen, S. (author)


    Publication date :

    1994-01-01


    Size :

    626478 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Navigating with an Animal Brain: a Neural Network for Landmark Identification and Navigation

    Gaussier, P. / Zrehen, S. / IEEE; Industrial Electronics Society | British Library Conference Proceedings | 1994



    Unidentified landmark navigation

    Bellantoni, J.F. | Engineering Index Backfile | 1967


    Unidentified landmark navigation.

    BELLANTONI, J. F. | AIAA | 1967


    Unidentified landmark navigation.

    Bellantoni, J. F. | NTRS | 1967