Calibration Techniques Research Of Industrial Robot

Robot calibration is one of the key techniques of robot off-line programming. Calibration is defined as using advanced measurement equipments and model -based parameter identification approaches to identify the accurate robot parameters and increase the absolute accuracy.Combining its real geometrical structure, a polishing robot's error model is established and a single point method is used to identify the geometrical parameter errors. Introducing a new error compensation thought in which different kinds errors can be compensated with different ways. Establishing the ideal and actual kinematics model, and kinematics equation and inverse kinematics one are obtained.In order to compensate the joint errors, this paper uses genetic neural network to get the joint errors. To overcome the premature of the genetic algorithm, the paper introduces Solis&Wets algorithm that has dynamic parameter encoding character to complement the genetic algorithm. The robot joint errors are achieved through genetic neural network, and robot pose accuracy is increased through compensating the joints.Further, this paper studies the geometry parameters calibration method. Based on pose matching principle, the paper uses nonlinear optimization procedure and iterative linear equation to calibrate the geometrical parameters and compare their calibration results. Considering that in some case some of the robot parameters that theoretically are necessary could possibly be indistinguishable to others, because they produce almost the same manipulator pose error in the considered working subspace. A new non-redundant error parameter set is obtained and its calibration results are compared and analysised with the complete set.Considering the shortcomings of the geometrical parameter calibration method this paper introduces a new calibration method based on neural network.