Locomotion control of a hexapod by Turing patterns

Marco Pavone, Michael Stich, Bernhard Streibl

    Research output: Chapter in Book/Published conference outputChapter


    In this paper, the reflexive behavior of a biomorphic adaptive robot is analyzed. The motion generation of the robot is governed by a Reaction-Diffusion Cellular Neural Network (RD-CNN) that evolves towards a Turing pattern representing the action pattern of the robot. The initial conditions of this RD-CNN are given by the sensor input. The proposed approach is particularly valuable when the number of sensors is high, being able to perform data compression in real-time through analog parallel processing. An experiment using a small 6-legged robot realized in Lego MindStorms™ with three sensors is presented to validate the approach. A simulated 3×3 CNN is used to control this hexapod.

    Original languageEnglish
    Title of host publicationCISM International Centre for Mechanical Sciences, Courses and Lectures
    Number of pages7
    Publication statusPublished - 1 Jan 2008

    Publication series

    NameCISM International Centre for Mechanical Sciences, Courses and Lectures
    ISSN (Print)0254-1971
    ISSN (Electronic)2309-3706


    • Back Part
    • Central Pattern Genus
    • Command Neuron
    • Front Part
    • Turing Pattern


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