Biologically inspired neural controllers for motor control in a quadruped robot

This paper presents biologically inspired neural controllers for generating motor patterns in a quadruped robot. Sets of artificial neural networks are presented which provide 1) pattern generation and gait control, allowing continuous passage from walking to trotting to galloping, 2) control of sitting and lying down behaviors, and 3) control of scratching. The neural controllers consist of sets of oscillators composed of leaky-integrator neurons, which control pairs of flexor-extensor muscles attached to each joint. The networks receive sensory feedback proportional to the contraction of simulated muscles and to joint flexion. Similarly to what is observed in cats, locomotion can be initiated by either applying tonic (i.e. non-oscillating) input to the locomotion network or by sensory feedback from extending the legs. The networks are implemented in a quadruped robot. It is shown that computation can be carried out in real time and that the networks can generate the above mentioned motor behaviors.

[1]  F. Delcomyn.  Neural basis of rhythmic behavior in animals. , 1980, Science.

[2]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[3]  J. Smith,et al.  Neuromuscular patterns of stereotypic hindlimb behaviors in the first two postnatal months. III. Scratching and the paw-shake response in kittens. , 1988, Brain research.

[4]  J. Smith,et al.  Neuromuscular patterns of stereotypic hindlimb behaviors in the first two postnatal months. II. Stepping in spinal kittens. , 1988, Brain research.

[5]  Peter A. Getting.  Reconstruction of small neural networks , 1989 .

[6]  A. Roberts,et al.  Mutual Re‐excitation with Post‐Inhibitory Rebound: A Simulation Study on the Mechanisms for Locomotor Rhythm Generation in the Spinal Cord of Xenopus Embryos , 1990, The European journal of neuroscience.

[7]  N. S. Bradley,et al.  Animal models offer the opportunity to acquire a new perspective on motor development. , 1990, Physical therapy.

[8]  T. Williams.  Phase coupling by synaptic spread in chains of coupled neuronal oscillators. , 1992, Science.

[9]  Patrik Larsson,et al.  A Distributed Neural Network Architecture for Hexapod Robot Locomotion , 1992, Neural Computation.

[10]  S. Grillner,et al.  Neural networks that co-ordinate locomotion and body orientation in lamprey , 1995, Trends in Neurosciences.

[11]  Thomas Kindermann,et al.  Walking: A Complex Behavior Controlled by Simple Networks , 1995, Adapt. Behav..

[12]  P. Katz.  Neurons, Networks, and Motor Behavior , 1996, Neuron.

[13]  Stephen Grossberg,et al.  Neural control of interlimb oscillations , 1997, Biological Cybernetics.

[14]  S Grossberg,et al.  Neural control of interlimb oscillations. II. Biped and quadruped gaits and bifurcations. , 1997, Biological cybernetics.

[15]  Hiroaki Kitano,et al.  Development of an Autonomous Quadruped Robot for Robot Entertainment , 1998, Auton. Robots.

[16]  David P. Bashor,et al.  A large-scale model of some spinal reflex circuits , 1998, Biological Cybernetics.

[17]  Auke Jan Ijspeert,et al.  Synthetic Approaches to Neurobiology: Review and Case Study in the Control of Anguiliform Locomotion , 1999, ECAL.

[18]  Hiroshi Kimura,et al.  Biologically-inspired adaptive dynamic walking of the quadruped on irregular terrain , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[19]  John Hallam,et al.  Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology , 1999, Adapt. Behav..