## 1 Introduction  
Major current global problems include high greenhouse gas emissions and the related issue of global warming [1]. This highlights the necessity of reducing emissions across all sectors of the economy. This challenge is particularly significant in the transportation sector, including intra-company transportation. Such transportation increasingly relies on Automated Guided Vehicles (AGVs). AGVs have been under development for many years and are particularly popular in warehouses and production facilities, where they have become an integral part of the production process. Their implementation not only replaces human labor in repetitive tasks that do not require significant attention but also mitigates many dangerous situations associated with human error. Research on AGVs is being conducted in numerous industrial and scientific centers. A critical factor for their effective utilization is the precision in mapping the planned travel route. Accordingly, much of the research on AGVs focuses on the appropriate selection of navigation systems. Many studies aim to optimize the control algorithms for both existing vehicles and prototype models designed for specific tasks. In many prototype AGV systems, microcontrollers and development boards, such as the Atmega 328 [2], STM 32 [3, 4], Atmega 128 [5], Arduino with Atmega 2560 microcontroller [6], and Arduino Uno [7], are used for control. Depending on their intended application, various sensors are employed to operate these vehicles. Existing AGVs, which navigate along physical tracks marked by lines (e.g., inductive loops or reflective line methods) detected by inductive sensors, reflective optocouplers, or cameras [8], are now being replaced by virtual lines created from images captured by cameras [9], lidars [10], laser sensors, and other technologies. The study in [8] explored the impact of image processing using a camera to control an AGV following a line. As is shown, vehicle control is affected by image resolution, image processing parameters, and frame rate. In [10], the authors used a single Light Detection and Ranging (LIDAR) sensor to enable proper navigation in an unfamiliar environment, avoiding obstacles even in low-light conditions. To create the virtual environment in which the vehicle operates, including for obstacle detection, neural networks are often used [11; 12; 13]. In [12], the authors conducted simulation analyses of AGV motion control using Physics-Informed Neural Networks (PINNs) in applying nonlinear Model Predictive Control (MPC) for AGV trajectory tracking. The results presented by the authors of this work confirmed that the analyzed model works effectively under dynamic conditions. The authors of [13] proposed a machine-learning-based approach for predictive control for autonomous vehicles. The study focused on dynamic adaptation of vision systems. The advantages and disadvantages of different AGV control methods are presented, for example, in [7]. In the case of line-of-sight methods based on the analysis of route line positions, interference with line recognition caused by various factors (changes in lighting, discontinuities, traces on the pavement identified as a traffic track, etc.) can be a significant problem. These issues are the subject of [14; 15], among others. The authors of [14] analyzed the problems of vision control of an AGV moving in a working area, which were associated with uneven illumination, obscuration of the line delineating the preset traffic path, or its damage, which results in incomplete or deformed images of paths, as well as additional artifacts that introduce errors in the analyzed path line. Many solutions combine multiple navigation techniques [16; 17; 18; 19; 20], which significantly improve vehicle positioning accuracy and may also allow the vehicle to move in different environments (such as open terrain [10] and indoor spaces). In [11], a combination of line detection using infrared sensors and a camera capturing the line's position was employed.