A Aptitude Measuring-Control System Of Robot

The repair and maintenance work of the turbine blades is the international problem for all the hydro power plant. A lot of manpower and resources is costed every year, especially in China, because the rivers contain plentiful sand, the problem is more serious. In order to lessen labor intensity and increase the welding and grinding precision and efficiency of the robot, introducing and exploring a repair robot used for welding and grinding on the turbine blades is very useful for those plants. Due to the measurement-control system is the core of robot, in order to improve whole capability and automation level of the robot, hardware and software structure of the system is designed.The theme is an important part of province project for tackling key problem concerning the development of minitype welding and repairing instrument suiting for working in the turbine pit, the theme is worked in the welding and grinding robot. With D-H method the kinetic coordinate and equations are built which is the base of controlling robot.Due to the robot depends on demonstration to realize the required track, we must get the inverse kinematic solutions for the special purpose robot repairing hydraulic turbines with redundant degree of freedom, but the traditional approach is complicated. A kind of multilayer forward neural network based on improved genetic algorithm is applied to build the inverse kinematics model of manipulator, aim at the model, each operator and coding plan of the improved algorithm are discussed in detail, because the first joint of the robot is not rotational, for to unify to the fitness function, the relation model between every joint of the robot and corresponding electromotor rotational angle which is regarded as output sample neural network is build. The software in which the neural network is realized is designed in the paper, and with two-link planar manipulator, the feasible and effective of using GA to learn the weights of NN is studied, simulations show that the proposed method improves considerably the inverse kinematic solutions for robot manipulator and guarantees a rapid global convergence.