Robot kinematics parameter identification method based on hybrid genetic simulated annealing algorithm

Abstract

The invention discloses a robot kinematics parameter identification method based on a hybrid genetic simulated annealing algorithm. According to the method, a kinematics model of a robot and an errormodel of the robot are constructed, and performance advantages of a genetic algorithm and a simulated annealing algorithm are utilized and mixed together to be used; accurate identification of model errors is achieved through the global search capacity of the genetic algorithm and the local search capacity of the simulated annealing algorithm, and therefore various parameters of the robot models can be corrected, and the absolute positioning precision of the tail end of the robot is improved; and an optimal solution obtained by the genetic algorithm is directly input into the simulated annealing algorithm, and therefore the global optimization capacity is achieved, a local optimal solution can be omitted, and error model parameters are accurately identified.

Description

technical field [0001] The invention relates to the technical field of robot kinematics calibration, in particular to a method for identifying robot kinematics parameters based on a hybrid genetic simulated annealing algorithm. Background technique [0002] With the application and development of robot offline programming technology, robot calibration technology, as one of the key technologies for the practical application of offline programming technology, has received more and more attention and research from scientific researchers. According to the robot calibration process, selecting an appropriate kinematic model and calibration measurement method is the premise of robot calibration. On this basis, processing the calibration data to realize error parameter identification and correction is the purpose of robot calibration. [0003] There are many ways to model robot kinematics. Different kinematic models have a great influence on the accuracy of the robot end. The more p...

AI Technical Summary

Problems solved by technology

The most commonly used method for kinematic parameter identification is the least squares method. This method does not need to consider the disturbance information, but the number of equations needs to be greater than the number of identification parameters, which requires more measurement data points, a large amount of calculation, and the need to design Reasonable trajectory has certain limitations; Mei Gaoming et al. use genetic algorithm to identify kinematic parameters, but genetic algorithm is easy to fall into local optimum; although simulated annealing algorithm can jump out of local optimal solution for global optimization, but its The requirements for the initial solution are high. If the initial solution is not good, it will take a long time to identify the accurate parameters.

Images