부산대학교 도서관

Geometric parameter errors have a direct impact on the positioning accuracy of industrial robots, making their reduction crucial for enhancing accuracy. Identifying the key geometric parameter errors with the greatest impact on robot accuracy significantly improves its performance. In this paper, we estimate the impact of geometric parameter errors in industrial robots on position accuracy using sensitivity analysis with the Random Forest (RF) method. Firstly, the kinematic error model of the industrial robot is constructed based on the MD-H convention. The principle of RF method is presented, and the geometric parameter errors are randomly sampled by the Latin hypercube sampling (LHS) method, the predictor delta importance (PDI) of each geometric parameter error is calculated. Then, the influence of each geometric parameter error on the position accuracy of the same pose is analyzed. Finally, a simulation experiment is performed with a 6-DOF industrial robot to validate the proposed method's correctness and effectiveness. The results indicate that the precision design of the vital geometric parameter errors could significantly enhance the position accuracy of the industrial robot.