Homography-Based Navigation System for Unmanned Aerial Vehicles

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.

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 chapter, navigation systems for unmanned aerial vehicles (UAVs) are given. In this context, inertial navigation, air data system, surface radar, satellite radio navigation systems, Doppler and altimeter radars, magnetic sensors, vision-based systems, and simultaneous localization and mapping are described. Moreover, measurement faults for these systems, when they are used in UAVs, are investigated and details for the fault modeling are presented.

The use of unmanned aerial vehicles (UAVs) in various military and civil applications is the subject of much current attention. With recent developments in personal computer technology, and the availability at affordable cost of peripherals, and electronic and optical sensors, UAVs for long endurance missions, with flight autonomy beyond the visual range, have become an attractive challenge for study in universities and research institutes. This paper describes the development of a target-lock-on optical remote sensing system to be used as a payload in a university-class UAV. To accomplish autonomous way-point navigation for the conduct of optical sensing surveillance, a gimbaled-platform with servo control and an Attitude and Heading Reference System (AHRS) navigation system for UAV position and attitude measurements have been developed. The UAV also utilises a Global Position System (GPS) receiver, a pressure altimeter, gyroscopes and an electric compass. A novel mathematical model is proposed to calculate the optimal parameters for orientating the CCD camera line of sight with a ground target, designated in real time from a ground control station. Both ground and flight test results have demonstrated the feasibility of the navigation control scheme and the UAV’s ability to conduct ground target acquisition and image transmission.

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.

Technical solutions for the creation of fundamentally new universal multifunctional autonomous navigation system of radar operating principle are proposed. Currently, navigation systems of unmanned aerial vehicles (UAV) are based on global navigation satellite systems (GNSS) receivers, combined with a block of inertial sensors of spatial orientation. GNSS are based on the use of coordinated by motion and radiation signals of Earth artificial navigation satellites network. GNSS provide continuous and almost immediate determination of the position (location) and speed of the UAV almost over the globe. Such a system provides a fairly accurate determination of the UAV position and parameters of its movement subject to a good GNSS signal. At the same time, the GNSS is a radio-technical navigation system and, like any such system, is subject to the influence of external radio interference, both natural and artificial. One of the ways to solve the problem of performing UAV flights in conditions of natural and artificial interference, when information from external sources is not available to estimate the position of the UAV, is the use of path reckoning method. The most important component of the autonomous navigation system implementing the course-Doppler mode of path calculation is the Doppler velocity sensor and hover drift. The R&D of Doppler velocity sensor & hover drift for the UAV (small size and weight) is a complex scientific, technical and design task, the implementation of which became possible due to the use of modern technical (design, schematic, technological, etc.) solutions.

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.

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.

This article presents a comprehensive approach to the design and implementation of a modern control and navigation system for unmanned aerial vehicles (UAVs), specifically developed to ensure operational stability even under the challenging conditions of electronic warfare (EW). The study introduces a detailed structural and functional architecture that integrates classical control algorithms, proportional-integral-derivative (PID) controllers, advanced GPS modules with spoofing and jamming protection, as well as inertial navigation systems capable of compensating for GPS loss. Additionally, the system employs backup azimuth-based navigation, ensuring safe return even when all external navigation signals are compromised. A key feature of the presented system is its modular adaptability, supporting both military and civilian use cases, including long-range reconnaissance, environmental monitoring, logistics, and emergency rescue operations. The communication subsystem leverages LoRa and CRSF/ELRS protocols with frequency-hopping techniques to maintain secure, interference-resistant data channels. High-power video transmitters and telemetry overlays provide real-time situational awareness for the operator, supported by a user-friendly interface. The system was rigorously tested under near-combat conditions, demonstrating reliable signal integrity at distances up to 25 km, stable flight performance, and autonomous return-to-launch (RTL) behavior. Moreover, software-level enhancements, including modified ArduPilot firmware and pseudo-random frequency reassignment, significantly improved operational resilience. Future improvements include integrating artificial intelligence (AI) elements for enhanced autonomy, optimizing energy efficiency, and expanding visual navigation capabilities through advanced computer vision technologies. The results confirm that the developed UAV system is a scalable, robust, and effective solution for executing complex missions in dynamically changing and hostile environments.

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

The landing is one of the most dangerous maneuvers in the entirety of the flight phase of an Unmanned Aerial Vehicle (UAVs). Sudden changes in the environment cause issues regarding the stability of the drone, which poses a difficult challenge in landing the UAV precisely. To better the safety of any UAVs flying in urban areas, UAVs should be landed carefully, in a GPS-denied or network-disconnected environment, by using vision and inertial data. This paper presents UAV safe landing system which comprises of three sub-systems for detection of designated landing sites and autonomous pose correction, landing site inspection and landing flight control. This paper deals with vision-based target detection and pose correction system in-depth. The airborne vision system is utilized to recognize certain markers on the landing site. The information from the onboard visual sensors and Inertial Measurement Unit (IMU) is utilized to control and land UAV in a perfect landing trajectory, on a precise location. A series of experiments have been outlined to test and optimize the proposed method using Parrot AR.Drone 2.0.

Abstract. the presence of many types of navigation information measurement systems for unmanned aerial vehicles indicates that they have differences in accuracy, complexity of implementation, cost, reliability, and each of these methods is selected based on the complexity of the flight task, technical characteristics of the UAV used. The on-board control system must receive clear navigation information, but the systems used for navigation have their positive and negative sides, in the presence of natural or artificial obstacles, these systems lose their effectiveness and lead to uncontrolled UAV falls, which can pose a direct threat of soil pollution by gradually decomposing UAV components in the soil environment, which is why the development and improvement of new navigation systems is relevant.

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

With1 the rapid development of unmanned aerial vehicles, the probability of illegal flights and aerial collision increases. This paper proposes a UAV safety monitoring and management methodology considering flight plan management, real-time monitoring and alerting of potential accidents. Based on the method we design the architecture and develop a prototype system using internet of things technology. The system functions are validated and the performances are evaluated through real flight tests. Test results validate the applicability of the system.

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