Research on kinematic parameters optimization of robot arm based on random forest Bayesian optimization

As one of the essential logistics equipment in the production system, the robot arm is required to find the optimal configuration for its unknown loading and unloading trajectory and its kinematic parameters before the new product is put into production to find the optimal logistics parameter configuration scheme for the production. However, there are still some problems in optimizing the kinematic parameters of the robot arm, such as high optimization time cost and poor optimization effect. This paper is aimed at the working scenario of installing the fuel cell plate on the ABB IRB 1410 robot arm. The integrated normalized value of the motion stationarity, absolute positioning accuracy, and logistics efficiency of the robot arm is taken as the optimization objective. The Bayesian optimization based on the random forest probability surrogate model is adopted to optimize the critical kinematic parameters. The optimal experimental results are compared with Bayesian optimization based on the Gaussian process surrogate model under the same conditions. The experimental results show that, compared with Gaussian process Bayesian optimization, the comprehensive effect of random forest Bayesian optimization is improved by 15% in optimizing robot arm kinematic parameters.