Reliable artificial intelligence in the situational management of guided robotic platforms of industrial enterprises.
DOI:
https://doi.org/10.34121/1028-9763-2024-1-34–54Keywords:
guided robotic platform, situational control, reliable artificial intelligence, artificial neural network, forecasting, rechargeable battery, керована робототехнічна платформа, ситуаційне управління, надійний штучний інтелект, штучна нейронна мережа, прогнозування, акумуляторна батареяAbstract
The incorporation of guided robotic platforms (GRPs) has become an essential component of Industry 4.0. A significant amount of data obtained from sensors can be used for intelligent control. Analyzing and selecting informative data, as well as removing redundant data for subsequent use, is a crucial task. The paper analyses and compares different industrial enterprise management types that utilize GRPs. A comparative analysis of procedures for optimizing the decision choice in general and situational management has been carried out. It has been proved that using traditional management for industrial enterprises that use GRPs is ineffective. The advantages and disadvantages of traditional and situational management of industrial enterprises are shown. Taking into account the shortcomings of each of them, the authors have developed an improved method of situational management that utilizes reliable artificial intelligence (AI). For this purpose, the stages of the modified general procedure for optimizing the choice of AI solutions have been described. A mathematical description of the process of evaluating alternatives has been made, which can be adapted to the use of AI models and their training. A model of the procedure for optimizing the choice of solutions based on situational control that considers the GRP’s battery charge has been developed. The model has been tested on a route map built at an industrial enterprise for the GRP Formica 1 manufactured by AIUT, Gliwice, Poland. The frame of the GRP Formica 1 data acquisition structure has been described. Data mining has been applied to the collected sensor data. The predictive model has been built based on an artificial neural network with deep learning and takes into account the residual charge of the battery, the mass transported by the GRP, and the presence of obstacles on its way. The mean absolute percentage error for the short-term forecast of the battery discharge of the GRP has decreased to 8 %. This proves the high efficiency of the developed method of situational control.References
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