A New Method for Identifying Kinetic Parameters of Industrial Robots

Abstract

Identifying the kinetic parameters of an industrial robot is the basis for designing a controller for it. To solve the problems of the poor accuracy and easy premature convergence of common bionic algorithms for identifying the dynamic parameters of such robots, this study proposed simulated annealing with similar exponential changes based on the beetle swarm optimization (SEDSABSO) algorithm. Expressions for the dynamics of the industrial robot were first obtained through the SymPyBotics toolkit in Python, and the required trajectories of excitation were then designed to identify its dynamic parameters. Following this, the search pattern of the global optimal solution for the beetle swarm optimization algorithm was improved in the context of solving for these parameters. The global convergence of the algorithm was improved by improving the iterative form of the number N of skinks in it by considering random perturbations and the simulated annealing algorithm, whereas its accuracy of convergence was improved through the class exponential change model. The improved beetle swarm optimization algorithm was used to identify the kinetic parameters of the Zhichang Kawasaki RS010N industrial robot. The results of experiments showed that the proposed algorithm was fast and highly accurate in identifying the kinetic parameters of the industrial robot.

Keywords

• industrial robot
• kinetic parameter identification
• beetle swarm optimization algorithm
• stochastic perturbation