Error recognition of robot kinematics parameters based on genetic algorithms

Abstract

With the development of modern industrial technologies and intelligent control technology, the requirement for the accuracy of the robot’s terminal attitude is getting higher and higher. Therefore, the technology of kinematics parameter identification, robot calibration becomes more and more important. In order to achieve the accurate calibration of the end position of the robot, it is necessary to identify and analyze its errors in order to improve its accuracy. In this paper, the error model of 6-DOF parallel manipulator is constructed based on vector matrix analysis, and the mapping relationship between attitude error and structural parameter error is obtained. In order to solve the problem that the attitude accuracy of the end of a 6-DOF parallel robot decreases, this paper creatively proposes a genetic algorithm to optimize the accuracy of the 6-DOF parallel robot. At the same time, this paper also improves the genetic algorithm by eliminating the similar individuals in the population of the algorithm through the application of the crowding mechanism, thus avoiding the premature convergence of the genetic algorithm. Finally, the proposed algorithm is compared with the least squares method. The simulation results show that the proposed genetic algorithm-based robot kinematics parameter error identification algorithm has obvious advantages.