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Exploiting inherent regularity in control of multilegged robot locomotion by evolving neural fields

Inden, B, Jin, Y, Haschke, R and Ritter, H (2011) Exploiting inherent regularity in control of multilegged robot locomotion by evolving neural fields

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Abstract

The control of multilegged robots is challenging because of the large number of sensors and actuators involved. However, the regularity inherent to gait control can be taken into account to design controllers for multilegged robots. In this paper, we show that NEATfields, a method designed for the evolution of large neural networks, can exploit this regularity to evolve significantly better gaits than those evolved by the standard NEAT method. We also show how evolved networks can control a robot with a ball-like morphology to move on a rough terrain. The success in evolving large neural networks suggests that the NEATfields method is a promising tool for studying complex behaviors in robotics and artificial life. © 2011 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Symplectic Elements
Date Deposited: 22 Jun 2012 18:35
Last Modified: 23 Sep 2013 19:27
URI: http://epubs.surrey.ac.uk/id/eprint/532807

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