Modeling Gait Using CPG (Central Pattern Generator) and Neural Network

Although quite a few central pattern generator controllers have been developed to regulate the locomotion of terrestrial bionic robots, few studies have been conducted on the central pattern generator control technique for amphibious robots crawling on complex terrains. The present article proposes a central pattern generator and feedforward neural network-based self-adaptive gait control method for a crab-like robot locomoting on complex terrain under two reflex mechanisms. In detail, two nonlinear ordinary differential equations are presented at first to model a Hopf oscillator with limit cycle effects. Having Hopf oscillators as the basic units, a central pattern generator system is proposed for the waveform-gait control of the crab-like robot. A tri-layer feedforward neural network is then constructed to establish the one-to-one mapping between the central pattern generator rhythmic signals and the joint angles. Based on the central pattern generator system and feedforward neural network, two reflex mechanisms are put forward to realize self-adaptive gait control on complex terrains. Finally, experiments with the crab-like robot are performed to verify the waveform-gait generation and transition performances and the self-adaptive locomotion capability on uneven ground.

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Neural network model of an amphibian ventilatory central pattern generator

Journal of Computational Neuroscience ◽

10.1007/s10827-019-00718-4 ◽

2019 ◽

Vol 46 (3) ◽

pp. 299-320 ◽

Cited By ~ 1

Author(s):

Ginette Horcholle-Bossavit ◽

Brigitte Quenet

Keyword(s):

Neural Network ◽

Central Pattern Generator ◽

Network Model ◽

Neural Network Model ◽

Pattern Generator

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Rapid Homeostatic Plasticity of Intrinsic Excitability in a Central Pattern Generator Network Stabilizes Functional Neural Network Output

Journal of Neuroscience ◽

10.1523/jneurosci.1945-12.2012 ◽

2012 ◽

Vol 32 (28) ◽

pp. 9649-9658 ◽

Cited By ~ 35

Author(s):

J. L. Ransdell ◽

S. S. Nair ◽

D. J. Schulz

Keyword(s):

Neural Network ◽

Central Pattern Generator ◽

Pattern Generator ◽

Homeostatic Plasticity ◽

Intrinsic Excitability ◽

Network Output ◽

Functional Neural Network

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Modeling and Simulation of Hexapod Kinematics with Central Pattern Generator

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v5i2.pp72-86 ◽

2016 ◽

Vol 5 (2) ◽

pp. 72

Author(s):

Aju M.T.

Keyword(s):

Neural Network ◽

Central Pattern Generator ◽

Body Length ◽

Network Models ◽

Pattern Generator ◽

Neural Network Models ◽

Hybrid Controller ◽

Learning Procedure ◽

The Central Nervous System ◽

Reflex System

<span>The revealed secrets of nature always led humans to their aspiring achievements. The fastest animal on land is Cheetah and similar robot has developed by engineers so far to attain a record speed of 20mph among legged robots. But in nature there are some insects those are far ahead of cheetah in speed with a unit of body length per second. Insects are small in their body size with legs usually countable from 4 to 12 or more. With more legs they can have more stability and can adapt to different terrain faster while walking. Six legged robot (hexapod) is generally expect to attain higher speed in terms of body length per second, since the nature has proof for it. Bio-inspired Central Pattern Generator (CPG) is in use for so far in robotic world to mimic the locomotion patterns of insects and other animals. Currently the hybrid controller of CPG and reflex is going on and this paper suggests a new architecture for the system. Neural Network modeled CPG acts as the motor neuron for each joint of the leg. In each instant a neural network models the gait of the robot by learning procedure from the reflex system. This is like the Central Nervous System (CNS) selecting gait of an animal according to the terrain that travels. CNS takes sensory feedback from eyes, force on each leg and body balance from cochlea to adapt the gait for current terrain. This paper in first place tries to simulate the gait patterns for a hexapod.</span>

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NONLINEAR DYNAMICS OF AN OSCILLATORY NEURAL NETWORK ACTING AS A MOTOR CENTRAL PATTERN GENERATOR

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127413501423 ◽

2013 ◽

Vol 23 (08) ◽

pp. 1350142 ◽

Cited By ~ 1

Author(s):

J. HURTADO-LÓPEZ ◽

D. F. RAMÍREZ-MORENO

Keyword(s):

Neural Network ◽

Nonlinear Dynamics ◽

Central Pattern Generator ◽

Bifurcation Analysis ◽

Central Pattern Generators ◽

Pattern Generator ◽

Reduced Model ◽

Quadruped Locomotion ◽

Oscillatory Neural Network ◽

Pattern Generators

In this work, we present a bifurcation analysis of a network of symmetrically coupled units modeling central pattern generators for quadruped locomotion. Here, we show a reduced model and characterize its dynamics and the dependence of the model behavior when one of the parameters is varied.

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