Evolutionary Motion Synthesis for a Modular Robot Using Genetic Algorithm

2003 ◽  
Vol 15 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Eiichi Yoshida ◽  
◽  
Satoshi Murata ◽  
Akiya Kamimura ◽  
Kohji Tomita ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Synthesis Method ◽  
Three Dimensional ◽  
Modular Robot ◽  
Motion Synthesis ◽  
Natural Manner ◽  
Simple Description ◽  
Motion Sequence ◽  
Feasible Solutions

An evolutionary motion synthesis method using genetic algorithm (GA) is presented for self-reconfigurable modular robot M-TRAN designed to realize various robotic motions and three-dimensional structures. The proposed method is characterized by its capacity to derive feasible solutions for complex synthesis problem of M-TRAN through natural genetic representation. For this purpose, the behavior of the robot is described using a motion sequence including both the dynamic motions and configuration changes of the robot. It is a series of segments each of which can specify simultaneous motor actuations and selfreconfiguration by connection/disconnection, starting from a given initial configuration. This simple description can be straightforwardly encoded into genetic representation to which genetic operations can be applied in a natural manner. We adopt traveling distance achieved by the evolved motion as the fitness function of GA. To verify the effectiveness of the proposed method, we have conducted simulations of evolutionary motion synthesis for certain initial configurations. Consequently, we confirm various adaptive motions are acquired according to different initial configurations and fitness functions. We also verify the physical feasibility of the evolved motions through experiments using hardware module M-TRAN II.

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

2008 ◽  
pp. 2226-2247
Author(s):  
Alex Burns ◽  
Shital Shah ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Power Plant ◽  
Hybrid Method ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Power Plant Boiler ◽  
Best Parameter ◽  
Feasible Solutions ◽  
Plant Boiler

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

2011 ◽  
pp. 179-203
Author(s):  
Alex Burns ◽  
Shital Shah ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Power Plant ◽  
Hybrid Method ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Power Plant Boiler ◽  
Best Parameter ◽  
Feasible Solutions ◽  
Plant Boiler

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

Active Vibration Control of a Modular Robot Combining a Back-Propagation Neural Network with a Genetic Algorithm

2005 ◽  
Vol 11 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Yangmin Li ◽  
Yugang Liu ◽  
Xiaoping Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Vibration Control ◽  
Control Method ◽  
Active Vibration Control ◽  
Synthesis Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Modular Robot ◽  
Analysis Experiment

In this paper, a genetic algorithm based back-propagation neural network suboptimal controller is developed to control the vibration of a nine-degrees-of-freedom modular robot. A finite-element method is used to model the modules of the robot, and the entire system dynamic equation is established using the substructure synthesis method. Then the joint stiffness parameters are identified based on the experimental modal analysis experiment. After modeling the whole structure with the models of the robotic modules and the joint parameters, simulations of the vibration control for the modular robot in several configurations are carried out. It is shown that the control method presented in this paper is effective at suppressing the residual vibrations of the modular robot.

COUPLED GENETIC ALGORITHM/KOHONEN NEURAL NETWORK (GANN) FOR PROJECTION OF THREE-DIMENSIONAL PROTEIN STRUCTURES ONTO THE PLANE

2002 ◽  
Vol 01 (01) ◽  
pp. 45-52 ◽  
Author(s):  
KUTAY O. ALPER ◽  
CAREY K. BAGDASSARIAN
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Amino Acid ◽  
Fitness Function ◽  
Amino Acid Level ◽  
Protein Structures ◽  
Three Dimensional ◽  
Spatial Relations ◽  
Amino Acid Residues ◽  
Kohonen Neural Network

An algorithm is presented for projecting — at the amino acid level — the three-dimensional crystal structure of a protein molecule onto a planar surface. The scheme is topologically consistent: if two amino acid residues are closely juxtaposed in three-dimensional space, they remain so upon projection. Through such projections, a single resulting picture captures the spatial relations amongst a protein molecule's amino acids. Operationally, a genetic algorithm is used to "evolve" a parameter set which serves as input for a self-organizing Kohonen neural network responsible for the projection itself. A fitness function characterizing the quality of the projections is defined and maximized via the genetic algorithm. The workings of both the genetic algorithm and neural network are discussed in detail. In this work, we seek to optimize projections resulting from the inherently "frustrated" task of collapsing a space-filling collection of amino acid residues onto a simpler surface. Ultimately, the chosen application is a testing ground for establishing the success of our coupled genetic algorithm/Kohonen neural network scheme which can easily be adapted for other uses.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

Design and optimization of bump (compression surface) for diverterless supersonic inlet

2021 ◽  
pp. 095441002110029
Author(s):  
Irsalan Arif ◽  
Hassan Iftikhar ◽  
Ali Javed
Keyword(s):  
Fitness Function ◽  
Supersonic Jet ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Least Square ◽  
Pressure Recovery ◽  
Design Parameters ◽  
Inlet System ◽  
Design And Optimization ◽  
Supersonic Inlet

In this article design and optimization scheme of a three-dimensional bump surface for a supersonic aircraft is presented. A baseline bump and inlet duct with forward cowl lip is initially modeled in accordance with an existing bump configuration on a supersonic jet aircraft. Various design parameters for bump surface of diverterless supersonic inlet systems are identified, and design space is established using sensitivity analysis to identify the uncertainty associated with each design parameter by the one-factor-at-a-time approach. Subsequently, the designed configurations are selected by performing a three-level design of experiments using the Box–Behnken method and the numerical simulations. Surrogate modeling is carried out by the least square regression method to identify the fitness function, and optimization is performed using genetic algorithm based on pressure recovery as the objective function. The resultant optimized bump configuration demonstrates significant improvement in pressure recovery and flow characteristics as compared to baseline configuration at both supersonic and subsonic flow conditions and at design and off-design conditions. The proposed design and optimization methodology can be applied for optimizing the bump surface design of any diverterless supersonic inlet system for maximizing the intake performance.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

scholarly journals Hydrothermal Synthesis and Structural Investigation of a Crystalline Uranyl Borosilicate

Inorganics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 25
Author(s):  
Kristen A. Pace ◽  
Vladislav V. Klepov ◽  
Mark D. Smith ◽  
Travis Williams ◽  
Gregory Morrison ◽  
...  
Keyword(s):  
Synthesis Method ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Exploratory Research ◽  
The Novel ◽  
Orthorhombic Space Group ◽  
Crystalline Materials ◽  
Waste Remediation ◽  
And Storage ◽  
X Ray Absorption

The relevance of multidimensional and porous crystalline materials to nuclear waste remediation and storage applications has motivated exploratory research focused on materials discovery of compounds, such as actinide mixed-oxoanion phases, which exhibit rich structural chemistry. The novel phase K1.8Na1.2[(UO2)BSi4O12] has been synthesized using hydrothermal methods, representing the first example of a uranyl borosilicate. The three-dimensional structure crystallizes in the orthorhombic space group Cmce with lattice parameters a = 15.5471(19) Å, b = 14.3403(17) Å, c = 11.7315(15) Å, and V = 2615.5(6) Å3, and is composed of UO6 octahedra linked by [BSi4O12]5− chains to form a [(UO2)BSi4O12]3− framework. The synthesis method, structure, results of Raman, IR, and X-ray absorption spectroscopy, and thermal stability are discussed.

Sign in / Sign up

Export Citation Format

Share Document