Optimizing CPGs method for the movement of the human's arm with two degrees of freedom

Nowadays with an increase in competition in science ve robotics sector, the harmony between human ve machines plays an important role in precision, comfortable ve usability, where that harmony affect the performance of automation, that commonly used in manufacture industry ve medical uses Etc. Therefore, with improvement in computer technology, manufacturers are looking toward computational simulation to reduce the testing time ve keep the cost low. The problem of controlling motion is an area in which neuroscience ve robotics can fruitfully interact. This study reviews researches carried out on human's arm motion by Central Pattern Generators (CPGs) using the Genetic Algorithms (GA) ve hybrid function. As well as, it shows that the CPGs are able to produce rhythmic patterns (RPs) similar to those that can be seen in nature without any input or sensory feedback. Moreover, the movement of the arm would be implemented as dart throwing ve can be used by developed algorithm software that uses the GA ve hybrid function. In addition, this study investigates three cases of the CPGs which are uncoupled, unidirectional, bidirectional two CPGs with two degrees of freedom (DOFs). For each cases, analysis ve comparison would be done by using different scenarios during the optimization. That optimization depends on objective functions, simple linear, ve nonlinear constraints. This thesis studies the sensitive of the parameters of CPGs. Finally, the real data is collected in order to show that the output of CPGs are able to produce RPs similar to real data. Keywords: Central Patterns Generators (Cpgs), Kinematics Equations, Optimizing Motion Generation, Genetic Algorithm (GA).