Evolution of neural controllers for locomotion and obstacle-avoidance in a 6-legged robot - Archive ouverte HAL Access content directly
Journal Articles Connection Science Year : 1999

Evolution of neural controllers for locomotion and obstacle-avoidance in a 6-legged robot

Abstract

This article describes how the SGOCE paradigm has been used within the context of a "minimal simulation" strategy to evolve neural networks controlling locomotion and obstacle-avoidance in a 6-legged robot. A standard genetic algorithm has been used to evolve developmental programs according to which recurrent networks of leaky-integrators neurons were grown in a user-provided developmental substrate and were connected to the robot's sensors and actuators. Specific grammars have been used to limit the complexity of the developmental programs and of the corresponding neural controllers. Such controllers have been first evolved through simulation and then successfully downloaded on the real robot.
Fichier principal
Vignette du fichier
CS99_filliat.pdf (1.21 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01021228 , version 1 (09-07-2014)
hal-01021228 , version 2 (17-02-2023)

Identifiers

  • HAL Id : hal-01021228 , version 2

Cite

David Filliat, Jérôme Kodjabachian, Jean-Arcady Meyer. Evolution of neural controllers for locomotion and obstacle-avoidance in a 6-legged robot. Connection Science, 1999, 11, pp.223--240. ⟨hal-01021228v2⟩
98 View
72 Download

Share

Gmail Facebook Twitter LinkedIn More