Robust Design of Parameter Tolerance to Industrial Robot

doi:10.3901/JME.2023.11.147

Abstract

Abstract: To reduce the sensitivity of positional accuracy reliability to kinematic and dynamic uncertain parameters of industrial robots, a robust optimization design method for kinematic and dynamic uncertain parameter tolerance is established based on the signal-to-noise ratio (SNR) of positional error. Aiming at the time-consuming problem of solving the dynamic equations to industrial robots, an efficient radial basis function neural network surrogate model for solving the dynamic equations of industrial robots is established, which effectively improved the efficiency of positional error simulation of industrial robots. The robust optimization design model is solved by genetic algorithm. Besides, the kinematic and dynamic uncertain parameter tolerance of industrial robots is optimized by taking the mean value of positional error and the SNR and the processing cost as the objective functions. Finally, the optimization results of the above three optimization objective function are treated as standard deviation of uncertain parameters, the positional accuracy reliability and positional accuracy reliability sensitivity of industrial robots are comparative analyzed. The effectiveness of the proposed robust design method of the kinematic and dynamic parameters tolerances for industrial robots is demonstrated.

Key words: industrial robot, reliability analysis, robust design, positional error, reliability sensitivity

CLC Number:

TP242

WU Jinhui, ZHANG Dequan, HAN Xu. Robust Design of Parameter Tolerance to Industrial Robot[J]. Journal of Mechanical Engineering, 2023, 59(11): 147-158.

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