The Kinematic Calibration of an Industrial Robot With an Improved Beetle Swarm Optimization Algorithm

Abstract

The article proposes a kinematic calibration method based on an improved beetle swarm optimization algorithm for an industrial robot to finish drilling and riveting. Specifically, a preference random substitution method to improve the update strategy was presented, which comprises a new boundary-processing strategy that avoids stagnation states and local optimization traps by adopting a dynamic parameter adjustment strategy to increase the speed and stability of searches. The effectiveness of the proposed method was verified by simulations and calibration experiments involving a robot drilling and riveting system with an industrial robot KUKA KR500L340-2. In addition, comparisons were conducted with linear least-squares, particle swarm optimization, and beetle swarm optimization algorithms. According to the results of calibration simulations and experiments, the fitness function value of the proposed algorithm can decrease rapidly in the first 20 iterations, while the mean value of the end-effector position error is reduced from 2.95mm to 0.20mm by using the proposed algorithm. Compared with other three algorithms, the positioning accuracy is improved by more than 60%.