## 1 Introduction
The camera was used as a sensor in the feedback loop of a proportional-integral-derivative (PID) controller. The goal of this solution was to reduce lateral oscillations during vehicle movement. In [20], the authors used an automated vehicle localization system based on ultra-wideband technology and visual guidance. There are also solutions in which infrared sensors are replaced by cameras. Cameras not only enable line identification but also recognition of markers that can be used in vehicle control [19; 21; 22]. The authors of [21] investigated a method for AGV navigation using a webcam and markers in the form of four directional signals and 10 letters of the alphabet. In [22], an omnidirectional camera image stream was used for AGV navigation. Route navigation was carried out using character recognition and implemented control. Among the unconventional methods of navigation can be included the system presented in [23] with AGV motion control, using cameras to recognize the hand gestures of the human operator. A significant focus is also placed on the safety of AGV movement [24] including that related to the detection and avoidance of obstacles and collisions with other vehicles [25; 26]. The authors of [25] present the results of a study of an autonomous navigation system that enables AGVs to detect and avoid obstacles based on the processing of data obtained with a front camera mounted on the vehicle. Some works are devoted to the analysis of fleet control of AGVs [27; 28]. In [27], simulation studies were conducted that included costs considering the number of AGVs, AGV speed, number of tasks, scheduling interval, and path length. The authors of [28] pointed out the important role of developing a control schedule for a set of AGVs, taking into account energy consumption and rational task allocation. The authors of [29] conducted research on the use of vision-based navigation in AGVs, which complements Global Positioning System (GPS)-based navigation. The developed vision-based navigation method was applied to a mine detection and neutralization platform [30], where vision-based navigation was used to complement other types of navigation systems, such as GPS and the inertial navigation system (INS). By using the camera, it was possible to control the vehicle in the event of a GPS signal loss. This approach resulted in satisfactory vehicle control performance under real-world conditions. An important factor in controlling an AGV is the variable speed of the vehicle, which depends on its load. Accordingly, various solutions have been proposed, including maintaining a constant vehicle speed by employing speed and inclination sensors [5]. Another solution is to measure the drive torque of the wheels and adjust the PID controller parameters based on the vehicle's current load. This adjustment is also important for optimizing energy consumption [18]. The application of Model Predictive Control (MPC) requires the development of a model of the controlled object; in this case, the AGV. This approach enables the determination of the AGV's path, which can account for dynamically emerging obstacles. This capability is particularly important in factories, where the increasing number of autonomous vehicles must coexist effectively. The rise in AGV traffic not only increases the risk of collisions but also reduces operational efficiency. In the study by [PERSON] and [PERSON], Path Planning of an AGV Based on Artificial Potential Field and Model Predictive Control [31], the authors highlighted the advantages and disadvantages of contemporary AGV control systems. They proposed an MPC algorithm based on an Artificial Potential Field (APF), enabling path planning, trajectory tracking, and improved system performance in real-time. An important consideration for future research would also be the energy consumption of such a control system.