Unmanned aerial vehicles (UAVs) are used nowadays in a wide range of applications, including monitoring, mapping, or surveying tasks, involving magnetic field mapping, mainly for geological and geophysical purposes. However, thanks to the integration of ultrasound-aided navigation used for indoor UAV flight planning and development in sensorics, the acquired magnetic field images can be further used, for example, to enhance indoor UAV navigation based on the physical quantities of the image or for the identification of risk areas in manufacturing or industrial halls, where workers can be exposed to high values of electromagnetic fields. The knowledge of the spatial distribution of magnetic fields can also provide valuable information from the perspective of the technical cleanliness. This paper presents results achieved with the original fluxgate magnetometer developed and specially modified for integration on the UAV. Since the magnetometer had a wider frequency range of measurement, up to 250 Hz, the DC (Direct Current) magnetic field and low frequency industrial components could be evaluated. From the obtained data, 3D magnetic field images using spline interpolation algorithms written in the Python programming language were created. The visualization of the measured magnetic field in the 3D plots offer an innovative view of the spatial distribution of the magnetic field in the area of interest.

This article deals with quadcopter modeling using a system for the measurement of unmanned aerial vehicle (UAV) parameters. UAVs are often equipped with various measurement devices and equipment for measurement, which significantly affects their weight. The currently available technical solutions and inventions do not allow corrections to be made to the on-board control electronics settings without the need to perform a test flight, or without the need to create complex and time-consuming mathematical models of the unmanned aerial vehicle; therefore, it is desirable to create a new method for modeling the characteristics of an UAV based on static laboratory measurements. The goal of this paper is to create a dynamic model of a quadcopter that will be adapted to a system for measuring UAV parameters, specifically the thrust of individual motors, which will be the next step to creating a new method for modeling UAV characteristics. This method can be used in the future for tuning flight control algorithms, based on static laboratory measurements.

In this article a new method is proposed for the preselection and objective evaluation of applicants for UAV training based on HMI models and mathematical analysis. With the rising number of UAV users there is a need for their preselection and qualification based on objective evaluation of their performance to assure the safety of air operations. A system consisting of a UAV RC controller, a display, and a software solution for the measurement of potential UAV pilot reaction was created. 59 participants were given the task to move the sticks of the RC controller to the random positions shown on the display as quickly and precisely as possible multiple times in a row. Based on the recorded stick movements, the performance of the participants was evaluated using HMI models and mathematical analysis. Four key aspects were identified for the preselection and continual evaluation of the UAV pilots: Transport delay, Time to 90% regulation, Overshoot, Deviation from the target in the stabilized state. For these parameters, limit values were proposed for the preselection of UAV pilot applicants. The created system with possible expansions can be used for effective UAV applicant preselection. The research supports the aim of better preselection and effective training of UAV pilots.