Central pattern generator based reflexive control of quadruped walking robots using a recurrent neural network

2014 ◽  
Vol 62 (10) ◽  
pp. 1497-1516 ◽  
Author(s):  
Duc Trong Tran ◽  
Ig Mo Koo ◽  
Yoon Haeng Lee ◽  
Hyungpil Moon ◽  
Sangdeok Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Central Pattern Generator ◽  
Recurrent Neural Network ◽  
Pattern Generator ◽  
Walking Robots

scholarly journals Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking

2021 ◽  
Vol 15 ◽  
Author(s):  
Dai Owaki ◽  
Shun-ya Horikiri ◽  
Jun Nishii ◽  
Akio Ishiguro
Keyword(s):  
Central Pattern Generator ◽  
Environmental Changes ◽  
Sensory Information ◽  
Motor Command ◽  
Pattern Generator ◽  
Bipedal Walking ◽  
Walking Robots ◽  
Two Dimensional ◽  
Limb Coordination ◽  
Systematic Methodology

Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for designing a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended motor command, in designing localised controllers in the CPG-based bipedal walking model. To this end, we developed a Tegotae function that quantifies the Tegotae concept. This function allowed incorporating decentralised controllers into the proposed bipedal walking model systematically. We designed a two-dimensional bipedal walking model using Tegotae functions and subsequently implemented it in simulations to validate the proposed design scheme. We found that our model can walk on both flat and uneven terrains and confirmed that the application of the Tegotae functions in all joint controllers results in excellent adaptability to environmental changes.

scholarly journals Central pattern generator and feedforward neural network-based self-adaptive gait control for a crab-like robot locomoting on complex terrain under two reflex mechanisms

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772344 ◽  
Author(s):  
Gang Wang ◽  
Xi Chen ◽  
Shi-Kai Han
Keyword(s):  
Neural Network ◽  
Central Pattern Generator ◽  
Complex Terrain ◽  
Pattern Generator ◽  
Feedforward Neural Network ◽  
Control Technique ◽  
Gait Control ◽  
Generator System ◽  
Adaptive Gait ◽  
Self Adaptive

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.

scholarly journals Modeling and Simulation of Hexapod Kinematics with Central Pattern Generator

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>

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

2013 ◽  
Vol 23 (08) ◽  
pp. 1350142 ◽  
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.

Walking quality guaranteed central pattern generator control method

2013 ◽  
Vol 228 (3) ◽  
pp. 569-579
Author(s):  
Jiaqi Zhang ◽  
Xiaolei Han ◽  
Xueying Han
Keyword(s):  
Central Pattern Generator ◽  
Control Method ◽  
Central Pattern Generators ◽  
Pattern Generator ◽  
Walking Robots ◽  
High Quality ◽  
Traditional Methods ◽  
Walking Gait ◽  
Pattern Generators ◽  
Periodic Inputs

Creating effective locomotion for a legged robot is a challenging task. Central pattern generators have been widely used to control robot locomotion. However, one significant disadvantage of the central pattern generator method is its inability to design high-quality walks because it only produces sine or quasi-sine signals for motor control as compared to most cases in which the expected control signals are more advanced. Control accuracy is therefore diminished when traditional methods are replaced by central pattern generators resulting in unaesthetically pleasing walking robots. In this paper, we present a set of solutions, based on testings of Sony’s four-legged robotic dog (AIBO), which produces the same walking quality as traditional methods. First, we designed a method based on both evolution and learning to optimize the walking gait. Second, a central pattern generator model was put forth to enabled AIBO to learn from arbitrary periodic inputs, which resulted in the replication of the optimized gait to ensure high-quality walking. Lastly, an accelerator sensor feedback was introduced so that AIBO could detect uphill and downhill terrains and change its gait according to the surrounding environment. Simulations were performed to verify this method.

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