Aiming at the slow convergence speed of traditional particle swarm optimization (PSO) algorithm in solving the geometric error calibration problem of industrial robots

a two-stage dynamic particle swarm optimization algorithm (LDPSO-BT) is proposed. First

the error model of the industrial robot is established by the Denavit-Hartenberg method

the geometric error calibration problem is converted into the solution of high-dimensional nonlinear equations

and then the number of particle swarms and the number of particles are linearly reduced in the algorithm solution process. In the late iteration of the improved particle swarm algorithm

an improved search mode is used to improve the speed iteration formula of the traditional particle swarm

and then the end positioning accuracy of the two algorithms before and after the geometric error calibration of the industrial robot is compared by simulation experiments. The experimental results show that the number of particle swarms has an important influence on the iteration time. Reducing the number of particles of the particle swarm linearly can effectively reduce the geometric error calibration of industrial robots. At the same time

the improved speed iteration formula can be used in the later stage of the particle swarm algorithm to ensure the accuracy of convergence. Compared with the traditional particle swarm optimization algorithm

using the improved particle swarm algorithm to obtain the geometric error revision data of the industrial robot can not only effectively reduce the positioning error of the industrial robot

but also has a more efficient iteration efficiency.