Dissertation for the degree of Doctor of Philosophy in speciality 152 - Metrology and Information and Measurement Technology, Branch of Knowledge 15 - Automation and Instrumentation, Lviv Polytechnic National University, Ministry of Education and Science of Ukraine, Lviv, 2025.
The dissertation is devoted to the research, development and implementation of modern software and hardware systems used in drones, with a special emphasis on metrological support and optimization of their characteristics. A significant part of the dissertation is devoted to ensuring the accuracy and reliability of measurements when operating drones using advanced calibration methods and test platforms, which is due to the ability of drones to work autonomously with a high degree of accuracy in various conditions.
Section 1 Amphibious robot design for surface and underwater operations. The first section presents the design and development of an unmanned system capable of performing amphibious operations, following the example of water striders. The section explores hardware integration such as Nvidia Jetson for real-time control and Robot Operating System (ROS) for system modularity and software integration. The aim is to ensure a smooth transition between surface and subsea operations, with particular emphasis on the metrological challenges associated with designing a system that operates in two different physical environments. In addition, this section discusses the technical and environmental factors that affect the accuracy of measurements and their significance in the design process.
Section 2: Creation of a test platform for measuring hydrodynamic characteristics. The second section is devoted to the development of a test platform for measuring the hydrodynamic characteristics of a drone in real water conditions. Key parameters such as thrust, energy consumption and reaction rate are analyzed to optimize the operation of the power plant. The section discusses the metrological support necessary to ensure accurate measurements, taking into account factors such as sensor calibration, environmental impact and repeatability of measurements. By evaluating drone performance under controlled conditions, this section provides insight into the relationship between energy consumption and propulsion plant efficiency, laying the groundwork for further improvement.
Section 3: Increasing stability with filtering algorithms. The third section examines the control problems that arise in an underwater environment, with an emphasis on optimizing the stability and position of the drone using advanced filtering algorithms. The integration of complementary and Kalman filters helps improve control of the drone's position, allowing it to maintain stability and recover quickly from disturbances. This section highlights the importance of real-time data processing to improve drone performance, especially in dynamic environments where precise control is critical. The filtering methods discussed here provide metrological support, minimizing errors and increasing the reliability of data from sensors.
Section 4: Data-based hydrodynamic modeling. The fourth section presents a data-based hydrodynamic model developed using real-time data from force sensors mounted on a drone. This model allows the drone to adapt to different underwater conditions, predicting hydrodynamic forces and adjusting its behavior accordingly. The use of machine learning methods, such as linear regression, helps to optimize the movements of the drone and increases its operational efficiency. This section demonstrates how important metrological support is to verify the accuracy of the collected data and ensure that the model accurately reflects the real conditions. The results of this section indicate a significant improvement in drone performance, especially in terms of accuracy and energy efficiency.
