Autofocus Vision System Enhancement for UAVs via Autoencoder Generative Algorithm

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This chapter addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks including take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.

The purpose of this work is the analysis of the power supply system of unmanned aerial vehicle. One of the most important tasks of this system is to ensure the smallest possible losses in order to achieve greater endurance of the aircraft, which means – the ability to complete the mission. The presence of an inactive component of consumed power obviously reduces the efficiency of the entire unmanned aerial vehicle. Considering also that due to the influence of wind, speed changes and other factors, the power system spends a significant amount of time in the transition mode, the impact of this phenomenon becomes even more significant. Thus, the phenomenon of the occurrence of exchange power in the power supply system of an unmanned aerial vehicle is analyzed. One of the most common solutions for unmanned aerial vehicles is the use of a BLDC motor, which is a further development of DC motors and was created with the aim of improving their basic characteristics. This type of engine has gained its popularity due to numerous advantages: high reliability, efficiency, speed and others. The principles of control of the BLDС engine are given. The unmanned aerial vehicle power supply system, built on the basis of a buck converter and a bridge inverter, was analyzed. An equivalent circuit of the converter is built, taking into account the losses in the electric circuit. On its basis the relations for determining the value of exchange power in the power supply system are derived. With the help of these expressions, it is possible to determine the value of the exchange energy at an arbitrary time interval in the real power supply system of the aircraft. A model of the power supply system of an unmanned aerial vehicle with a BLDC engine was built in the Matlab Simulink software environment. A time diagram is obtained, on the basis of which it is possible to draw a conclusion about the content of exchange power in the converter circuit. To minimize this phenomenon, it is necessary to develop a compensation system or an intelligent control system.

In this paper, a quadcopter unmanned aerial vehicle (UAV) system based on neural-network enhanced dynamic inversion control is proposed for multiple real-world application scenarios. A sigma-pi neural network (SPNN) is used as the compensator to reduce the model error and improve the system performance in the presence of the uncertainties of UAV dynamics, payload, and environment. Besides, we present a technical framework for fast and robust implementation of multipurpose UAV systems and develop a testbed for the evaluation of UAV control system by using a high-precision optical motion capture system. Both simulation results and experiment results demonstrate that the SPNN can reduce the inversion errors related to UAV parameter uncertainties as well as tracking errors related to unknown disturbances and unmodeled dynamics. With the help of an online neural network (NN) learning mechanism, the entire system can achieve much higher accuracy in attitude and trajectory control than that achieved by conventional proportional-integral derivative based control systems under varying flight conditions.

The paper presents the analysis on stability of digital control system for unmanned vehicle with numerical analysis. The objective of this study is mainly emphasized on the fulfillment of the advanced control techniques according to the fundamental concepts of digital control system approaches for unmanned aerial vehicle system design. The targeted unmanned aerial vehicle system was designed based on the simple construction under the idea of fixed wing flight system approaches. The stability analyses on unmanned aerial vehicle are vital role to enhance the real world applications. The background concepts on digital control system for stability analysis on dynamic control system like unmanned aerial vehicle system. The appropriate controller design for dynamic control system of unmanned aerial vehicle is vital role to analyze the accurate stability condition for reality. The implementation of numerical analysis on compensator design has been developed by using MATLAB. The physical parameters in these analyses are based on the experimental outcomes from the recent research works in dynamical system implementations. The mathematical approaches are very helpful for numerical analysis on compensator design for the unmanned aerial vehicles schemes. The simulation results confirm that the high performance stability test on unmanned aerial vehicle system has been met with the theoretical works.

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This paper addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks such as take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

With the rapid development of intelligent information technology, human–computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human–computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human–computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human–computer interaction and has great development potential. The characteristics of human–computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human–computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious advantages.

The study focuses on the analysis and optimization of energy supply systems for unmanned aerial vehicles (UAVs), specifically FPV drones equipped with 10-inch propellers, to increase flight duration and operational efficiency under various civilian application scenarios. With the rapid expansion of drone usage in fields such as agriculture, environmental monitoring, logistics, and technical inspection, the issue of selecting the most efficient power source has become particularly relevant. The research aims to determine the optimal type of battery in terms of weight, energy density, endurance, and cost-effectiveness, ensuring reliable operation and sufficient energy reserve for different mission profiles. The article presents a comparative analysis between a lithium-polymer (LiPo) battery pack (6S, 10Ah) and three lithium-ion (Li-Ion) battery assemblies based on Molicel P42A cells with different configurations: 6S2P, 6S3P, and 6S4P. The analysis includes both theoretical calculations and practical considerations derived from known empirical data. Each configuration was evaluated based on three main criteria – flight time, maximum range, and overall economic viability. Additionally, the influence of payload mass (0–3 kg) on flight duration and efficiency was modeled for six key mission scenarios: observation and video surveillance, light and medium cargo delivery, heavy cargo delivery, one-way missions, and high-speed flight operations. Methodologically, the work takes into account motor efficiency (0.8–0.85), aerodynamic drag, and the voltage behavior of both LiPo and Li-Ion systems under load. The results demonstrate that Li-Ion batteries offer a higher specific energy density (up to 180 Wh/kg) and lower cost per watt-hour compared to LiPo alternatives, though their discharge characteristics limit maximum current output. Among the tested configurations, the Li-Ion 6S3P assembly showed the most balanced performance, providing a flight time of up to 45 minutes without payload and maintaining stable voltage throughout the discharge cycle. The LiPo 6S 10Ah pack remains advantageous for high-current, short-duration missions such as racing or rapid-response operations due to its superior instantaneous power output. The study concludes that no single battery configuration is universally optimal for all use cases. However, the Li-Ion 6S3P pack can be considered the most versatile and practical solution for multi-purpose civilian FPV platforms, combining reasonable cost, low weight, and extended endurance. The article also provides recommendations for selecting power systems depending on the mission profile and discusses the practical aspects of battery assembly, including the use of bi-metal copper and nickel-plated steel strips (0.15×10 mm) to ensure low resistance and reliable current transfer during high-load operation. The findings highlight the need for further experimental validation under real-world flight conditions, as well as future research focused on optimizing propeller geometry, assessing temperature impacts, and integrating hybrid power systems for small UAVs. The proposed analytical framework and results can serve as a methodological foundation for engineers, drone developers, and energy system designers seeking to enhance the performance and energy efficiency of modern FPV drones

A retrospective analysis of the use of unmanned aerial vehicles in the combat zone indicates a steady increase in the use of electronic warfare by the enemy, which reduces the effectiveness of reconnaissance flights of unmanned aerial vehicles, and in some cases leads to their loss. The article considers the prospects of creating and improving high-precision navigation systems, which is currently one of the priority areas for the development of unmanned aerial vehicles and relevant for ensuring high efficiency of unmanned aerial vehicles for both civil and military purposes. To develop and study methods and algorithms for localizing the position of unmanned aerial vehicles based on terrain images obtained by non-identical surveying means, taking into account the influence of electronic warfare on it, the article primarily considers approaches to comparing earth surface images as the main information component. The next step is to assess the correctness of the choice of informative parameters of the anchor points, where the mathematical model for their description and selection is the basis for the development of methods and algorithms for the formation of an autonomous navigation system for unmanned aerial vehicles. Taking into account the research conducted in the work, it was proposed to implement on board the drone intelligent information and calculation system of autonomous navigation, the algorithm of which is based on the logical basis of the SIFT descriptor. Thanks to the software implementation of the approaches proposed in the work and obtained on the basis of their experimental results, it is possible to develop and further improve methods of positioning unmanned aerial vehicles in space.

Analysis of a hydrogen fuel cell-PV power system for small UAV

The advances in microelectronics foster the Unmanned Aerial Vehicles (UAVs) to be used in many civil and academic applications that require higher levels of autonomy. Therefore, the navigation systems are considered one of the main subjects to study. This paper deals with the problem of estimating the pose of the UAV in the 3D world. In which, a vision-based navigation system using onboard monocular downward looking camera is proposed. The proposed system is based on a SIFT detector and FREAK descriptor which can keeps the performance of the feature matching and decrease the computational time. The system has been evaluated with real flight tests and the obtained results have been compared with the results from the DGPS.

This paper considers the problem of cascade control system for unmanned aerial vehicles (UAVs). The proposed control structure is a hierarchical scheme including an offline model predictive control (MPC) based on multi-parametric programming (MPP) technique and trajectory generator using genetic algorithm (GA). Simulation results are presented to show the effectiveness of the proposed strategy.