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.
Highlights
Kinetic parameters are the main factor influencing the control of fast and highly precise movements of industrial robots [1]
Fu et al [5] used the particle swarm optimization (PSO) algorithm with least squares to identify the kinetic parameters of a seven-DOF collaborative robot in Xinsong, but PSO can fall into the local optimum owing to poor population diversity in the late stage of processing that reduces the accuracy of identification of the parameters
Simulation experiments showed that the proposed algorithm is more accurate and faster than the common particle swarm and Beetle Antennae Search in identifying the dynamics parameters of robots
Summary
Kinetic parameters are the main factor influencing the control of fast and highly precise movements of industrial robots [1]. Gautier et al used a two degree-of-freedom (DOF) robot as the object of study and applied the extended Kalman filter and the least-squares method to identify its parameters. Memar used the SCHUNK Powerball LWA 4P as an experimental object, constructed a dynamics model for it, and implemented the least-squares method to identify the dynamic parameters of the industrial robot [3]. Fu et al [5] used the particle swarm optimization (PSO) algorithm with least squares to identify the kinetic parameters of a seven-DOF collaborative robot in Xinsong, but PSO can fall into the local optimum owing to poor population diversity in the late stage of processing that reduces the accuracy of identification of the parameters. Ding et al [6] identified the dynamics of the robot by using the genetic algorithm, but the process of coding of the algorithm is cumbersome
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