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Nowadays wind energy is one of the most important and promising sources of environmentally clean renewable energy. Wind turbine blades are among the most expensive components. Depending on the size, their manufacturing costs range between 10 % and 20 % of total manufacturing costs. Moreover, the size of blades has increased in recent years, leading to greater efficiency and energy production, but presenting higher failure probability.

The study was aimed at developing a geoid model using Unmanned Aerial Vehicle (UAV) technology. To accomplish this, a UAV was deployed to capture imagery of the study area from a height of 150m, with a ground resolution of 4.19cm. A total of 3737 images were obtained, covering an area of 725.804 hectares. The existing ellipsoidal and orthometric heights were used to georeferenced the acquired images. For the analysis, 35 points were utilized, with 20 points designated as ground control points (GCPs) and the remaining 15 points as check points (CPs).

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene.

The aim of the work is to investigate the accuracy of the DEM of nearshore areas using UAV material. One of the important issues in hydrological flood modelling is the high accuracy of the DEM. In the case of a complex relief type, which is associated with meandering riverbeds, it is proposed to use UAV surveys to create a DEM.

The research concerns the methods of UAV group control in networks with duplex communication between nodes built on the "client-server" architecture. Such systems belong to self-organized networks with variable topology. It is important to study the allowable parameters of deviation from the task in the management of a group of UAVs and analysis of the network topology for the group flighting. The network was optimized according to the Ant Colony algorithm ACO.