## 1 Introduction
The authors proposed an approach using a laser rangefinder to determine the distances of all obstacles surrounded within a given range of angles. The AGV can follow the specified object according to the rational intensity of the reflection and the range of the object detected from the rangefinder. Systems that use probabilistic methods are also used in the navigation of AGVs. In these systems, based on the estimation of errors in subsequent items, corrections are made in the calculation algorithm [30; 31; 32; 33; 34]. Other papers [35; 36] have presented the simplest solution based on odometry. The implemented program calculates and introduces corrections to the computational algorithm based on the measurements of the end position error in two runs achieved in opposite directions. Another paper [37] presented the implementation and experimental validation of a trajectory tracking and damage detection algorithm for AGV sensors and actuators based on many positioning modules, including laser scanners. The algorithm used an extended Kalman filter (EKF). In [38], the authors proposed a method for solving the problem of AGV vehicle location within the known problem of simultaneous location and map construction (SLAM). The studies covered SLAM location based on EKF and the detection of landmarks, odometry and inertial measurement unit (IMU) measurements, as well as laser scanners. In [39], research was carried out on the issue of AGV navigation in an industrial environment with frequently changing hall layouts. The autonomy of an AGV is very helpful in such cases. The vehicle carries out a set of tasks, among which we can distinguish planning, perception, route planning and route tracking as the tasks that an industrial vehicle must perform. The extended Kalman filter uses information from the odometry and laser navigation system to estimate the position of the vehicle. Another paper [40] presented a solution in which the robot used laser rangefinders to study the environment and learn the placement of objects in this environment. This process took place in several stages. In the first stage, the robot learned the initial alignment, while the second stage involved the detection of changes in the arrangement of objects. The final step in the process was to return each object to its previous position, which involved object tracking, map updates and path planning. SLAM was implemented using EKF. Another paper [41] described the use of the Kalman filter to analyze data from laser rangefinders. The filtered data made it possible to determine the position of the vehicle in relation to the reference line and to calibrate the navigation system. Laser rangefinders were also used in conjunction with vision systems. Examples of such applications are presented in [42]. The aim of this article is to present the developed methodology for the automatic correction of the AGV vehicle course. The developed methodology uses laser rangefinders to drive the vehicle on specific sections of the route, and to automatically correct the current position of the vehicle within a specific section of the route. The proposed methodology for the automatic course correction using a laser rangefinder to drive an AGV vehicle is a solution that simplifies and, at the same time, reduces the costs of the navigation system. The organization of the article is as follows. Section 2 characterizes the research object, as well as the laser rangefinders that were used. The main method of navigation used--odometry--is also described. Section 3 presents the proposed methodology for automatic course correction, the results of the experimental research and their analysis. Section 4 contains a summary and final conclusions.