Locomotion control of a hexapod by Turing patterns

Marco Pavone; Michael Stich; Bernhard Streibl

doi:10.1007/978-3-211-78775-5_16

Locomotion control of a hexapod by Turing patterns

Marco Pavone, Michael Stich, Bernhard Streibl

Research output: Chapter in Book/Published conference output › Chapter

Abstract

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 language	English
Title of host publication	CISM International Centre for Mechanical Sciences, Courses and Lectures
Publisher	Springer
Pages	213-219
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-211-78775-5_16
Publication status	Published - 1 Jan 2008

Publication series

Name	CISM International Centre for Mechanical Sciences, Courses and Lectures
Volume	500
ISSN (Print)	0254-1971
ISSN (Electronic)	2309-3706

Keywords

Back Part
Central Pattern Genus
Command Neuron
Front Part
Turing Pattern

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10.1007/978-3-211-78775-5_16

Cite this

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AU - Stich, Michael

AU - Streibl, Bernhard

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KW - Command Neuron

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ER -

Locomotion control of a hexapod by Turing patterns

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