scholarly journals Performance Comparisons of Bio-Micro Genetic Algorithms on Robot Locomotion

2020 ◽  
Vol 10 (11) ◽  
pp. 3863
Author(s):  
Francisco A. Chávez-Estrada ◽  
Jacobo Sandoval-Gutiérrez ◽  
Juan C. Herrera-Lozada ◽  
Mauricio Olguín-Carbajal ◽  
Daniel L. Martínez-Vázquez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Embedded System ◽  
Computing Time ◽  
Central Pattern Generators ◽  
Quadruped Robot ◽  
Robot Locomotion ◽  
Real Performance ◽  
Pattern Generators ◽  
The Individual ◽  
Computational Resources

This paper presents a comparison of four algorithms and identifies the better one in terms of convergence to the best performance for the locomotion of a quadruped robot designed. Three algorithms found in the literature review: a standard Genetic Algorithm (GA), a micro-Genetic Algorithm ( μ GA), and a micro-Artificial Immune System ( μ AIS); the fourth algorithm is a novel micro-segmented Genetic Algorithm ( μ sGA). This research shows how the computing time affects the performance in different algorithms of the gait on the robot physically; this contribution complements other studies that are limited to simulation. The μ sGA algorithm uses less computing time since the individual is segmented into specific bytes. In contrast, the use of a computer and the high demand in computational resources for the GA are avoided. The results show that the performance of μ sGA is better than the other three algorithms (GA, μ GA and μ AIS). The quadruped robot prototype guarantees the same conditions for each test. The structure of the platform was developed by 3D printing. This structure was used to accommodate the mechanisms, sensors and servomechanisms as actuators. It also has an internal battery and a multicore Embedded System (mES) to process and control the robot locomotion. The computing time was reduced using an mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. For example, in the experiment of a one-second gait cycle, GA uses 700% of computing time, μ GA (76%), μ AIS (32%) and μ sGA (13%). This research solves the problem of quadruped robot’s locomotion and gives a feasible solution (Central Pattern Generators, (CPGs)) with real performance parameters using a μ sGA bio-micro algorithm and a mES architecture.

scholarly journals Performance Comparisons of Bio-micro Genetic Algorithms on Robot Locomotion

2020 ◽  
Author(s):  
Francisco A. Chávez-Estrada ◽  
Jacobo Sandoval-Gutierrez ◽  
Juan C. Herrera-Lozada ◽  
Mauricio Olguín-Carbajal ◽  
Daniel L. Martínez-Vázquez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Embedded System ◽  
Processing Time ◽  
Quadruped Robot ◽  
Robot Locomotion ◽  
Real Performance ◽  
The Individual ◽  
Computational Resources ◽  
And Control ◽  
Better Than

This paper presents a novel micro-segmented genetic algorithm (μsGA) to identify the best solution for the locomotion of a quadruped robot designed on a rectangular ABS plastic platform. We compare our algorithm with three similar algorithms found in the specialized literature: a standard genetic algorithm (GA), a micro-genetic algorithm (μGA), and a micro artificial immune system (μAIS). The quadruped robot prototype guarantees the same conditions for each test. The platform was developed using 3D printing for the structure and can accommodate the mechanisms, sensors, servomechanisms as actuators. It also has an internal battery and a multicore embedded system (mES) to process and control the robot locomotion. This research proposes a μsGA that segments the individual into specific bytes. μGA techniques are applied to each segment to reduce the processing time; the same benefits as the GA are obtained, while the use of a computer and the high computational resources characteristic of the GA are avoided. This is the reason why some research in robot locomotion is limited to simulation. The results show that the performance of μsGA is better than the three other algorithms (GA, μGA and AIS). The processing time was reduced using a mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. This research solves the problem of continuous locomotion of a quadruped robot, and gives a feasible solution with real performance parameters using a μsGA bio-micro algorithm and a mES architecture.

The programmable CPG model based on Matsuoka oscillator and its application to robot locomotion

2020 ◽  
Vol 11 (03) ◽  
pp. 2050018
Author(s):  
Qiang Lu ◽  
Zhaochen Zhang ◽  
Chao Yue
Keyword(s):  
Significant Contribution ◽  
Central Pattern Generators ◽  
Robot Locomotion ◽  
Model Based ◽  
Chaotic Signals ◽  
Periodic Input ◽  
Periodic Signals ◽  
Pattern Generators ◽  
Sine Signal ◽  
Control Locomotion

The central pattern generator (CPG) is an important functional unit in the spinal cord which can produce rhythmic signals to control locomotion. Recently, there has been a growing interest in programmable central pattern generators (PCPG). In this paper, a new PCPG oscillator and a generic PCPG model based on the Matsuoka oscillator are presented. The perturbation method is used to determine the convergence of the generic PCPG model. The sine signal, the mix signal and chaotic signals are provided as inputs to the model, and the simulation results show that the generic PCPG can learn arbitrary periodic signals. In this paper, the generic PCPGs are allocated at each joint of the compass-like and the three-link robots and their outputs are chosen as joint position commands. The simulations show that the generic PCPG can be used to control robot locomotion effectively. The contributions of this paper are as follows: (1) A new PCPG oscillator based on the Matsuoka oscillator is presented as a beneficial enhancement to the PCPG oscillators. (2) A generic PCPG model is built comprising three PCPG oscillators. It can learn any periodic input signal. These findings are a significant contribution to generic PCPG research.

scholarly journals Dynamical State Forcing on Central Pattern Generators for Efficient Robot Locomotion Control

2020 ◽  
pp. 799-810
Author(s):  
Thirawat Chuthong ◽  
Binggwong Leung ◽  
Kawee Tiraborisute ◽  
Potiwat Ngamkajornwiwat ◽  
Poramate Manoonpong ◽  
...  
Keyword(s):  
Central Pattern Generators ◽  
Locomotion Control ◽  
Robot Locomotion ◽  
Dynamical State ◽  
Pattern Generators

Head Motion Stabilization During Quadruped Robot Locomotion

2011 ◽  
Vol 2 (1) ◽  
pp. 39-62 ◽  
Author(s):  
Miguel Oliveira ◽  
Cristina P. Santos ◽  
Lino Costa ◽  
Ana Rocha ◽  
Manuel Ferreira
Keyword(s):  
Global Optimization ◽  
Head Movement ◽  
Nonlinear Dynamical Systems ◽  
Fitness Function ◽  
Central Pattern Generators ◽  
Quadruped Robot ◽  
Biologically Inspired ◽  
Nonlinear Dynamical ◽  
Pattern Generators ◽  
The One

In this work, the authors propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows for explicitly specified parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. The overall idea is to generate head movement opposed to the one induced by locomotion, such that the head remains stabilized. Thus, in order to achieve this desired head movement, it is necessary to appropriately tune the CPG parameters. Three different global optimization algorithms search for this best set of parameters. In order to evaluate the resulting head movement, a fitness function based on the Euclidean norm is investigated. Moreover, a constraint-handling technique based on tournament selection was implemented.

scholarly journals Three-dimensional walking of a simulated muscle-driven quadruped robot with neuromorphic two-level central pattern generators

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988528
Author(s):  
Yasushi Habu ◽  
Keiichiro Uta ◽  
Yasuhiro Fukuoka
Keyword(s):  
Three Dimensional ◽  
Central Pattern Generators ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Neural Systems ◽  
Rhythm Generation ◽  
Pattern Generators ◽  
Cat Model ◽  
Muscle Models ◽  
Leg Mechanisms

We aim to design a neuromorphic controller for the locomotion of a quadruped robot with muscle-driven leg mechanisms. To this end, we use a simulated cat model; each leg of the model is equipped with three joints driven by six muscle models incorporating two-joint muscles. For each leg, we use a two-level central pattern generator consisting of a rhythm generation part to produce basic rhythms and a pattern formation part to synergistically activate a different set of muscles in each of the four sequential phases (swing, touchdown, stance, and liftoff). Conventionally, it was difficult for a quadruped model with such realistic neural systems and muscle-driven leg mechanisms to walk even on flat terrain, but because of our improved neural and mechanical components, our quadruped model succeeds in reproducing motoneuron activations and leg trajectories similar to those in cats and achieves stable three-dimensional locomotion at a variety of speeds. Moreover, the quadruped is capable of walking upslope and over irregular terrains and adapting to perturbations, even without adjusting the parameters.

scholarly journals Optimizing central pattern generators (CPG) controller for one legged hopping robot by using genetic algorithm (GA)

2018 ◽  
Vol 7 (2.14) ◽  
pp. 160 ◽  
Author(s):  
Arman Hadi Azahar ◽  
Chong Shin Horng ◽  
Anuar Mohamed Kassim ◽  
Amar Faiz Zainal Abidin ◽  
Mohamad Haniff Harun ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Stochastic Optimization ◽  
Central Pattern Generators ◽  
Optimization Method ◽  
Pattern Generator ◽  
Pi Controller ◽  
Optimum Parameters ◽  
Hopping Robot ◽  
Pattern Generators ◽  
The One

This paper presents the optimization process of Central Pattern Generator (CPG) controller for one legged hopping robot by using Genetic Algorithm (GA). To control the one legged hopping robot, a CPG controller is designed and integrated with a conventional Proportional-Integral (PI) controller. Conventionally, the CPG parameters are tuned manually. But by using this method, the parameters produced are not exactly the optimum parameters for the CPG. Therefore, a computational stochastic optimization method; GA is designed to optimize the CPG controller parameters. The GA is designed based on minimizing the error produced towards achieving the reference height. The re-sponse of the one legged hopping robot is compared and the results of the error towards reference height are analyzed.  

Parallel Spine Design and CPG Motion Test of Quadruped Robot

2019 ◽  
Vol 34 (05) ◽  
pp. 2059013
Author(s):  
Binrui Wang ◽  
Yixuan Liu ◽  
Zhongwen Li ◽  
Dijian Chen ◽  
Ruizi Ma ◽  
...  
Keyword(s):  
Lateral Displacement ◽  
Central Pattern Generators ◽  
Quadruped Robot ◽  
Model Based ◽  
Biological Phenomena ◽  
Parallel Kinematic Mechanism ◽  
Physical Prototype ◽  
Spine Model ◽  
Pattern Generators ◽  
The Stability

The spine of mammals aids in the stability of locomotion. Central Pattern Generators (CPGs) located in spinal cord can rapidly provide a rhythmic output signal during loss of sensory feedback on the basis of a simulated quadruped agent. In this paper, active spine of quadruped robot is shown to be extremely effective in motion. An active spine model based on the Parallel Kinematic Mechanism (PKM) system and biological phenomena is described. The general principles involved in constructing a neural network coupled with limbs and spine to solve specific problems are discussed. A CPG mathematical model based on Hopf nonlinear oscillators produces rhythmic signal during locomotion is described, where many parameters to be solved must be formulated in terms of desired stability, often subject to vertical stability analysis. Our simulations demonstrate that active spine with setting reasonable CPG parameters can reduce unnecessary lateral displacement during trot gait, improving the stability of quadruped robot. In addition, we demonstrate that physical prototype mechanism provides a framework which shows correctness of simulation, and stability can thus be easily embodied within locomotion.

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