Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

Poramate Manoonpong, Sakyasingha Dasgupta, Dennis Goldschmidt, Florentin Wörgötter

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Abstrakt

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.
OriginalsprogEngelsk
TitelInternational Joint Conference on Neural Networks (IJCNN)
Antal sider8
ForlagIEEE
Publikationsdato2014
Sider3295 - 3302
ISBN (Trykt)978-1-4799-6627-1
DOI
StatusUdgivet - 2014
BegivenhedThe International Joint Conference on Neural Networks - Beijing, Kina
Varighed: 6. jul. 201411. jul. 2014

Konference

KonferenceThe International Joint Conference on Neural Networks
LandKina
ByBeijing
Periode06/07/201411/07/2014
NavnInternational Conference on Neural Networks. Proceedings
ISSN1098-7576

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