Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation - Sandi Baressi Šegota; Nikola Anđelić; Jelena Štifanić; Zlatan Car (Machines, 2024)
Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving <semantics><mover accent="true">R¯</mover>2=0.99924,σ=0.00007</semantics> and <semantics>M<mover accent="true">A¯</mover>PE=0.33589,σ=0.00955</semantics>, the model trained on the original, non-pruned, data achieves <semantics><mover accent="true">R¯</mover>2=0.98796,σ=0.00081</semantics> and <semantics>M<mover accent="true">A¯</mover>PE=0.46895,σ=0.05636</semantics>. These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.