Optimizing Fourier Series Based Gaits for Modular Robots Using an Evolutionary Algorithm

2013 ◽  
Author(s):  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Genetic Algorithm ◽  
Fourier Series ◽  
Evolutionary Algorithm ◽  
Periodic Motion ◽  
Degrees Of Freedom ◽  
Series Representation ◽  
Robotic System ◽  
Experimental Results ◽  
New Method ◽  
Modular Robots

A new method of controlling and optimizing robotic gaits for a modular robotic system is presented in this paper. A robotic gait is implemented on a robotic system consisting of three Mobot modules for a total of twelve degrees of freedom using a Fourier series representation for the periodic motion of each joint. The gait implementation allows robotic modules to perform synchronized gaits with little or no communication with each other making it scalable to increasing numbers of modules. The coefficients of the Fourier series are optimized by a genetic algorithm to find gaits which move the robot cluster quickly and efficiently across flat terrain. Simulated and experimental results show that the optimized gaits can have over twice as much speed as randomly generated gaits.

scholarly journals Smart and Modular

2011 ◽  
Vol 133 (09) ◽  
pp. 48-51
Author(s):  
Harry H. Cheng ◽  
Graham Ryland ◽  
David Ko ◽  
Kevin Gucwa ◽  
Stephen Nestinger
Keyword(s):  
Degrees Of Freedom ◽  
Robotic System ◽  
Search And Rescue ◽  
Degree Of Freedom ◽  
Robotic Systems ◽  
Modular Robots ◽  
Modular Robot ◽  
Modular Systems ◽  
The Past ◽  
Three Degrees Of Freedom

This article discusses the advantages of a modular robot that can reassemble itself for different tasks. Modular robots are composed of multiple, linked modules. Although individual modules can move on their own, the greatest advantage of modular systems is their structural reconfigurability. Modules can be combined and assembled to form configurations for specific tasks and then reassembled to suit other tasks. Modular robotic systems are also very well suited for dynamic and unpredictable application areas such as search and rescue operations. Modular robots can be reconfigured to suit various situations. Quite a number of modular robotic system prototypes have been developed and studied in the past, each containing unique geometries and capabilities. In some systems, a module only has one degree of freedom. In order to exhibit practical functionality, multiple interconnected modules are required. Other modular robotic systems use more complicated modules with two or three degrees of freedom. However, in most of these systems, a single module is incapable of certain fundamental locomotive behaviors, such as turning.

Incremental Hyperplane Partitioning for Classification

2013 ◽  
Vol 4 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Tao Yang ◽  
Sheng-Uei Guan ◽  
Jinghao Song ◽  
Binge Zheng ◽  
Mengying Cao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Equations ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
New Method ◽  
Complex Pattern ◽  
Classification Problems ◽  
Encoding Scheme ◽  
Incremental Approach ◽  
Higher Dimensional

The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

Shape Modification of NURBS Curves Using Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1450-1454
Author(s):  
Yan Hong Li ◽  
Guo Wang Mu ◽  
Zeng Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Experimental Results ◽  
New Method ◽  
Shape Modification ◽  
Nurbs Curve ◽  
Nurbs Curves

In this paper, we propose a new method for shape modification of NURBS curves. For a given NURBS curve, we modify its one or more weights so that the curve passes through the point specified in advance. We convert this into an optimization problem and solve it by genetic algorithm. The experimental results show the feasibility and validity of our method.

Image Adaptive Denoising Method Based on Compressive Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 993-996
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng ◽  
Sun Li
Keyword(s):  
Genetic Algorithm ◽  
Image Quality ◽  
Compressive Sensing ◽  
Matching Pursuit ◽  
Experimental Results ◽  
New Method ◽  
Denoising Method ◽  
Edge Information ◽  
Fast Speed ◽  
Adaptive Denoising

We present a new adaptive denosing method using compressive sensing (CS) and genetic algorithm (GA). We use Regularized Orthogonal Matching Pursuit (ROMP) to remove the noise of image. ROMP algorithm has the advantage of correct performance, stability and fast speed. In order to obtain the optimal denoising effect, we determine the values of the parameters of ROMP by GA. Experimental results show that the proposed method can remove the noise of image effectively. Compared with other traditional methods, the new method retains the most abundant edge information and important details of the image. Therefore, our method has optimal image quality and a good performance on PSNR.

scholarly journals Navier-Stokes Three Dimensional Equations Solutions Volume Three

2018 ◽  
Vol 10 (4) ◽  
pp. 128
Author(s):  
Biruk Petros
Keyword(s):  
Fourier Series ◽  
Vector Fields ◽  
Stokes Equations ◽  
Stokes Problem ◽  
Series Representation ◽  
New Method ◽  
Navier Stokes ◽  
Sine Series ◽  
Navier Stokes Equation ◽  
Variable Function

Solution of Navier-Stokes equation is found by introducing new method for solving differential equations. This new method is writing periodic scalar function in any dimensions and any dimensional vector fields as the sum of sine and cosine series with proper coefficients. The method is extension of Fourier series representation for one variable function to multi-variable functions and vector fields.Before solving Navier-Stokes equations we introduce a new technique for writing periodic scalar functions or vector fields as the sum of cosine and sine series with proper coefficients. Fourier series representation is background for our new technique.Periodic nature of initial velocity for Navier-Stokes problem helps us write the vector field in the form of cosine and sine series sum which simplify the problem. 

Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

2014 ◽  
Vol 998-999 ◽  
pp. 1033-1036
Author(s):  
Jian Wang ◽  
Xiao Hu Duan ◽  
Yan Li ◽  
Peng Bai
Keyword(s):  
Genetic Algorithm ◽  
Effective Properties ◽  
High Accuracy ◽  
Experimental Results ◽  
New Method ◽  
Oil Analysis ◽  
Maintenance Costs ◽  
Vector Machines ◽  
Aero Engine ◽  
Genetic Algorithm Model

Diagnosis of engine fault is critical in reducing maintenance costs. A new method which incorporates hybrid relative vector machines and genetic algorithm (RVM-GA) was proposed to predict aero engine fault based on data of the spectrometric oil analysis. Experimental results show that it has a high accuracy and effective properties.

Cooperative Obstacle Avoidance of Multiple Robotic Fishes Based on Grids Method

2014 ◽  
Vol 536-537 ◽  
pp. 929-933
Author(s):  
Yu Lian Jiang
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Optimal Path ◽  
Experimental Results ◽  
New Method ◽  
Local Minima ◽  
New Approach ◽  
Serial Number

To resolve the problem of obstacle avoidance and path planning of multiple robotic fishes, this paper proposes a new approach which uses collaboration mechanism based on grids method. The proposed approach splits the workspace of those robotic fishes using grids method, identifies each grid with serial number, designs the cooperative mechanism to avoid collision among these fishes, and plans the path for each fish. This method was applied in the obstacle avoidance competition of multiple robotic fishes which happening in a field with obstacle in it. The experimental results show the new method is more effective, can get more optimal path, and avoid the local minima issue which arises frequently in the A-star algorithm and genetic algorithm. It significantly improves the ability of the multiple robotic fishes system on the aspect of path planning and coordination.

Immune Programming Applications in Image Segmentation

2011 ◽  
pp. 304-321
Author(s):  
Xiaojun Bi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Theoretical Analysis ◽  
Evolutionary Algorithm ◽  
Optimization Algorithms ◽  
Convergence Speed ◽  
Experimental Results ◽  
Intelligent Algorithms ◽  
Slow Convergence ◽  
Immune Programming

In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete the task of image segmentation. Traditional evolutionary algorithm represented by Genetic Algorithm is an efficient approach for image segmentation, but in the practical application, there are many problems such as the slow convergence speed of evolutionary algorithm and premature convergence, which have greatly constrained the application. The goal of introducing immunity into the existing intelligent algorithms is to utilize some characteristics and knowledge in the pending problems for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Theoretical analysis and experimental results show that immune programming outperforms the existing optimization algorithms in global convergence speed and is conducive to alleviating the degeneration phenomenon. Theoretical analysis and experimental results show that immune programming has better global optimization and outperforms the existing optimization algorithms in alleviating the degeneration phenomenon. It is a feasible and effective method of image segmentation.

An Effective Evolutionary Algorithm for Analyzing a Realiability of Geotechnical Engineering

2011 ◽  
Vol 105-107 ◽  
pp. 1528-1533
Author(s):  
Wei Zeng ◽  
Kai Wen ◽  
Bao Quan Zhao ◽  
Guang Cheng Zhang ◽  
San You Zeng
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Reliability Index ◽  
Geotechnical Engineering ◽  
Experimental Results ◽  
The Real ◽  
Real Coded Genetic Algorithm

The reliability index is not only nonlinear but also continuous, so we design the real coded genetic algorithm to improve the performance of the algorithm. The experimental results indicate that our method is 10 times faster than the binary-coded genetic algorithm, more accurate and stable than other methods.

scholarly journals An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Xian Zhang ◽  
Xiao-Yi Qian ◽  
Hui-Deng Peng ◽  
Jian-Hui Wang
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Convergence Rate ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Evolutionary Process ◽  
Experimental Results ◽  
Quantum Genetic Algorithm

For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. AndHεgate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved byMarkovchain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.

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