Research on the Calibration Method of Kinematic Parameters of Six-Joint Industrial Robot Based on WOA-GA-PSO Algorithm

To address the issue of absolute positioning errors in the robotic machining process, a robot kinematic parameter calibration method based on a hybrid optimization of the Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), followed by Particle Swarm Optimization (PSO) (WOA-GA-PSO), is proposed. This method compensates for errors arising during the robot kinematic parameter calibration, thereby enhancing the robot's positioning accuracy and machining precision. Initially, the GA algorithm is employed to generate an initial population with higher diversity and fitness. Subsequently, the WOA algorithm is utilized to further optimize this population, leveraging its global search capability and the GA algorithm's performance optimization capabilities to produce an optimal PSO initial particle population. During the local search phase of the PSO iteration, the GA algorithm's optimization strategy is introduced to refine the local population. To enhance the reliability of the fitness function, an error calculation system and a fitness updating mechanism based on historical data are implemented during the fitness evaluation process. This ensures the fitness function accurately assesses the strengths and weaknesses of individual performances. Experimental results demonstrate that the kinematic parameter calibration method for six-joint industrial robots based on the WOA-GA-PSO algorithm significantly improves parameter prediction accuracy, which is crucial for precise robot motion control.