Data-driven Energy Evaluation and Optimization Method for Industrial Robots

Abstract

With the rapid development and wide application of industrial robots (IRs), it inevitably brings huge energy consumption (EC), which has become an important part of manufacturing EC. Therefore, the EC optimization of IRs has become the key to the green transformation and upgrading of the manufacturing industry, and it is of great significance for the realization of "carbon neutrality" and "carbon peaking". Therefore, this paper focuses on the energy evaluation and optimization of IR, and realizes the power and EC evaluation and motion parameter optimization of its trajectories based on data-driven method. First, the convolutional neural network (CNN) and Transformer models are combined to build the energy model of IR, and then accurate modeling of its power and EC is realized based on the deep learning algorithms. Based on the above energy model, the exhaustive method and genetic algorithm (GA) are used to find the optimal motion parameters and obtain the optimal trajectory with the least EC. Finally, the experimental results show that the proposed method can achieve more than 98% and 99% of the power and EC modeling of IR, respectively. The optimization of the trajectory motion parameters of IR is realized through exhaustive method and GA, and the maximum optimization potential on the test dataset can reach 52.77%, which verifies the effectiveness and accuracy of the proposed method.