Multi-objective Optimization of Samples for Industrial Robot Error Compensation

A sample multi-objective optimization method is proposed for the error-similarity-based error compensation method. Firstly, qualitative analysis is performed to show the influence of the samples on error compensation, and the characteristics and mathematical model of the optimal samples are proposed based on the engineering application requirements of error compensation method. Then, a sample multi-objective optimization method based on NSGA-Ⅱ (non-dominated sorting genetic algorithm-Ⅱ) is proposed to solve the optimization problem of sample points. Finally, experiments and contrastive analysis are performed, and it is shown that the maximum positioning error of the robot is reduced from 1.4953 mm to 0.2752mm by using the optimal sample set, which is better than the other 2 random sample sets. The experimental results verify the feasibility and effectiveness of the proposed methods.