A modular artificial neural net for controlling a six-legged walking system

Cruse H, Bartling C, Cymbalyuk G, Dean J, Dreifert M (1995)
Biol. Cybern. 72(5): 421-430.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Cruse, HolkUniBi; Bartling, Ch.; Cymbalyuk, G.; Dean, J.; Dreifert, M.
Abstract / Bemerkung
A system that controls the leg movement of an animal or a robot walking over irregular ground has to ensure stable support for the body and at the same time propel it forward. To do so, it has to react adaptively to unpredictable features of the environment. As part of our study of the underlying mechanisms, we present here a model for the control of the leg movement of a 6-legged walking system. The model is based on biological data obtained from the stick insect. It represents a combined treatment of realistic kinematics and biologically motivated, adaptive gait generation. The model extends a previous algorithmic model by substituting simple networks of artificial neurons for the algorithms previously used to control leg state and interleg coordination. Each system controlling an individual leg consists of three subnets. A hierarchically superior net contains two sensory and two 'premotor' units; it rhythmically suppresses the output of one or the other of the two subordinate nets. These are continuously active. They might be called the 'swing module' and the 'stance module' because they are responsible for controlling the swing (return stroke) and the stance (power stroke) movements, respectively. The swing module consists of three motor units and seven sensory units. It can produce appropriate return stroke movements for a broad range of initial and final positions, can cope with mechanical disturbances of the leg movement, and is able to react to an obstacle which hinders the normal performance of the swing movement. The complete model is able to walk at different speeds over irregular surfaces. The control system rapidly reestablishes a stable gait when the movement of the legs is disturbed.
Erscheinungsjahr
1995
Zeitschriftentitel
Biol. Cybern.
Band
72
Ausgabe
5
Seite(n)
421-430
ISSN
0340-1200
eISSN
1432-0770
Page URI
https://pub.uni-bielefeld.de/record/1865150

Zitieren

Cruse H, Bartling C, Cymbalyuk G, Dean J, Dreifert M. A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern. 1995;72(5):421-430.
Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., & Dreifert, M. (1995). A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern., 72(5), 421-430. https://doi.org/10.1007/s004220050144
Cruse, Holk, Bartling, Ch., Cymbalyuk, G., Dean, J., and Dreifert, M. 1995. “A modular artificial neural net for controlling a six-legged walking system”. Biol. Cybern. 72 (5): 421-430.
Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., and Dreifert, M. (1995). A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern. 72, 421-430.
Cruse, H., et al., 1995. A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern., 72(5), p 421-430.
H. Cruse, et al., “A modular artificial neural net for controlling a six-legged walking system”, Biol. Cybern., vol. 72, 1995, pp. 421-430.
Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., Dreifert, M.: A modular artificial neural net for controlling a six-legged walking system. Biol. Cybern. 72, 421-430 (1995).
Cruse, Holk, Bartling, Ch., Cymbalyuk, G., Dean, J., and Dreifert, M. “A modular artificial neural net for controlling a six-legged walking system”. Biol. Cybern. 72.5 (1995): 421-430.

16 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

The manifold structure of limb coordination in walking Drosophila.
DeAngelis BD, Zavatone-Veth JA, Clark DA., Elife 8(), 2019
PMID: 31250807
Walknet, a bio-inspired controller for hexapod walking.
Schilling M, Hoinville T, Schmitz J, Cruse H., Biol Cybern 107(4), 2013
PMID: 23824506
Dominance of local sensory signals over inter-segmental effects in a motor system: experiments.
Borgmann A, Toth TI, Gruhn M, Daun-Gruhn S, Büschges A., Biol Cybern 105(5-6), 2011
PMID: 22290138
Insect walking is based on a decentralized architecture revealing a simple and robust controller.
Cruse H, Dürr V, Schmitz J., Philos Trans A Math Phys Eng Sci 365(1850), 2007
PMID: 17148058
Control of swing movement: influences of differently shaped substrate.
Schumm M, Cruse H., J Comp Physiol A Neuroethol Sens Neural Behav Physiol 192(10), 2006
PMID: 16830135
Assessing sensory function in locomotor systems using neuro-mechanical simulations.
Pearson K, Ekeberg O, Büschges A., Trends Neurosci 29(11), 2006
PMID: 16956675
Experiments and models of sensorimotor interactions during locomotion.
Frigon A, Rossignol S., Biol Cybern 95(6), 2006
PMID: 17115216
Schema-based learning of adaptable and flexible prey-catching in anurans I. The basic architecture.
Corbacho F, Nishikawa KC, Weerasuriya A, Liaw JS, Arbib MA., Biol Cybern 93(6), 2005
PMID: 16292659
Behaviour-based modelling of hexapod locomotion: linking biology and technical application.
Durr V, Schmitz J, Cruse H., Arthropod structure & development. 33(3), 2004
PMID: IND43653723
Robots in invertebrate neuroscience.
Webb B., Nature 417(6886), 2002
PMID: 12015617
Load-regulating mechanisms in gait and posture: comparative aspects.
Duysens J, Clarac F, Cruse H., Physiol Rev 80(1), 2000
PMID: 10617766
Locomotor strategy for pedaling: muscle groups and biomechanical functions.
Raasch CC, Zajac FE., J Neurophysiol 82(2), 1999
PMID: 10444651
Oscillatory network controlling six-legged locomotion. Optimization of model parameters.
Cymbalyuk GS, Borisyuk RM, Müller-Wilm U, Cruse H., Neural Netw 11(7-8), 1998
PMID: 12662761

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