Navigation System For Unmanned Aerial Vehicles Research Articles

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

An Improved Aquila Optimizer with Local Escaping Operator and Its Application in UAV Path Planning

Background: With the development of intelligent technology, Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end. Objective: In order to obtain a better flight path, this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator, named LEOAO, to deal with the UAV path planning problem in three-dimensional environments. Methods: UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance, flight height, turning angle and climbing/diving angle. In this paper, the LEOAO is introduced to find the optimal path by minimizing the cost function, and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm. Results: To test the performance of LEOAO, two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer, whale optimization algorithm, including the original Aquila optimizer. Conclusion: The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally, the improved Aquila optimizer obtains a better path.

ТАКСОНОМІЧНИЙ МЕТОД ВИБОРУ СПОСОБУ ВЕКТОРИЗАЦІЇ ПОТОЧНОГО ЗОБРАЖЕННЯ ДЛЯ ОПТИКО-ЕЛЕКТРОННОГО МОДУЛЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТА

In the article, a taxonomic method for selecting the optimal vectorization approach for processing current images in the optical-electronic module of unmanned aerial vehicles (UAVs) was developed. The primary goal of this method is to optimize UAV navigation systems, which is particularly relevant due to the need to improve data processing efficiency in conditions of limited access to satellite navigation systems. This is critically important for ensuring high autonomy and accuracy of UAVs during the performance of various tasks. The need for such a method arose from the specific challenges faced by UAVs when operating in complex and unpredictable conditions. Analysis has shown that the use of vector images can significantly reduce the time required for information processing. This is possible due to the smaller volume of data that needs to be processed compared to traditional raster images. Reducing information processing time is critically important for the operational capability of UAVs to respond to dynamic changes in the operational environment, especially in critical situations. The taxonomic approach applied in the study allows for systematic classification and evaluation of different vectorization methods in terms of their processing speed, accuracy of results, and data retention efficiency. This enables the identification of the most suitable method for the specific operational conditions in which the UAV is used, thereby optimizing the overall system performance. Furthermore, the results of this research are significant not only in the scientific field but also in practical applications. By improving autonomous navigation systems for UAVs, the study directly contributes to enhancing their effectiveness in complex and unpredictable conditions. This is particularly relevant for missions that require a high level of precision and reliability, such as those performed by military UAVs and emergency response. Enhanced navigation capabilities allow these UAVs to perform complex maneuvers and make real-time decisions based on processed vectorized images, thereby reducing the risk of errors and increasing mission success rates. An improved vectorization method could lead to significant enhancements in UAV navigation systems, making them more resistant to failures and capable of operating in less structured environments without heavy reliance on satellite signals. This enhancement is vitally important for both military and civilian UAV applications, where operational flexibility and reliability are extremely important.

Machine learning and UAV path following identification algorithm based on navigation spoofing

With advances in navigation technology, unmanned aerial vehicles (UAVs) have become widely available to all sections of society. Given the potential hazards of UAVs, as seen from their use in the Russia–Ukraine war and security incidents at London and Baghdad airports, counter-UAV technology is receiving unprecedented attention. This paper describes a method for taking control of a UAV indirectly by navigation spoofing and luring it to a designated area from which it can be neutralized. Our contributions are threefold: first, we analyze the limitations of the traditional state estimation and control model for UAV navigation based on the integration of the Global Navigation Satellite System and Inertial Navigation System. Second, we propose a particle hypothesis and planning model that is insensitive to the particular UAV navigation system, and decompose the UAV navigation spoofing process into two steps: identification and planning. Finally, to overcome the poor heading angle prediction accuracy of unary polynomial regression, we propose a support vector regression algorithm that improves the prediction accuracy from 1.5° to 1.01° under navigation spoofing. Experimental results using the proposed navigation spoofing method prove that machine learning offers significant advantages in modeless identification.

Retraction: Al Said, N, Gorbachev, Y, Avdeenko, A. An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development. Software: Practice and Experience 2021; 51: 1509–1517. https://doi.org/10.1002/spe.2964

The above article, published online on July 2021 in Wiley Online Library https://onlinelibrary.wiley.com/doi/10.1002/spe.2964 has been retracted by agreement between the journal's Editor‐in‐Chief and John Wiley & Sons Ltd.The retraction has been agreed following concerns regarding manipulation of the peer review and publishing process. Concerns were originally raised by a third party (1). Further investigation by the publisher has found manipulation of the peer review process. The retraction has been agreed because the peer review of the article was compromised. The authors disagree with this retraction. Abalkina, A. (2022). Publication and collaboration anomalies in academic papers originating from a paper mill: evidence from a Russia‐based paper mill https://arxiv.org/abs/2112.13322

Phased Array Radio Navigation System on UAVs: Real-Time Implementation of In-flight Calibration⋆

Unmanned aerial vehicles (UAV) require an alternative positioning system to the global navigation satellite systems (GNSS) for safety-critical operations since GNSS is prone to unintentional or malicious electromagnetic interference. As a possible redundant positioning solution independent of GNSS, the phased array radio system (PARS) has proved its potential as a local navigation system for UAVs. Previous work suggested an automatic calibration algorithm to estimate the precise PARS ground antenna orientation to improve PARS positioning accuracy. This algorithm was then integrated with PARS- and barometer-aided inertial navigation system (INS) based on multiplicative extended Kalman filter (MEKF) to enable in-flight calibration when GNSS is available. This paper presents the implementation of the previously suggested aided-INS with in-flight calibration mode for real-time operation.

System Engineering-Based Conceptual Design of Indoor Navigation System of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been become important day by day because of advantages in our life use cases; specially, they can play important role in the industry 4.0 concept such as automation in production plants. System engineering approach is planning the design and production phases. Every system, subsystem, component, or mission of the systems are investigated before project starts and each requirement is set. The investigation of compatibility between systems and products minimizes the risks during the production phases. This paper proposes the usage of system engineering approach to the conceptual design of an UAV navigation system which aims to be used in indoor environments. In order to provide autonomous indoor flight, system requirements are set by using system engineering approach. Then step by step, the basic functions that UAV needs to complete operation are specified. The required products are listed as a tree and the relation between products and functions are investigated. Thus, the conceptual design of the UAV indoor navigation system is demonstrated by using of the functional tree, the functions and product relation matrix, the product tree, and the use cases. Moreover, the system operational requirements are investigated, and the requirement operation phases, modes and use cases are also determined. Thanks to system engineering, all these complex systems are put together systematically, and the conceptual design is achieved.

AN EXPERIMENTAL DETERMINATION AND NUMERICAL ANALYSIS OF A LOITER MUNITION UNMANNED AERIAL VEHICLE SYSTEM

This paper experimentally determinates the calculation of an Unmanned Aerial Vehicle (UAV) and analyzes the design criteria within four different scenarios. The UAV to be designed is a loiter munition UAV system. A mobile or equipment such as a parachute and/or airbag. The UAV may be operable day and night conditions, capable of 2-3 hours of flight hours, and will be launched from a catapult. The UAV navigation system is compact and easily controlled by a personal Global Positioning System (GPS). The mentioned UAV will have a vast operational capability, especially for defense and border security activities since it is equipped with advanced avionics and a small physical footprint for covert operations. As a consequence of this research, it can be claimed that the UAV's mid-wing, twin-tail, and relatively light body will have three axis stability and offer numerous benefits, particularly in terms of operational cost.

A Visual Navigation System for UAV under Diverse Illumination Conditions

ABSTRACT The high-precision navigation and positioning ability of UAV (Unmanned Aerial Vehicles) is the key factor to reflect its degree of automation. Visual navigation based on image matching has become one of the important research fields for UAV to realize autonomous navigation, because of its low cost, strong anti-jamming ability, and good location result. However, the visual quality of images captured by UAV will be seriously affected by some factors, such as weak illumination conditions or insufficient performance of its sensors. Resolving a series of degradation of low-light images can improve the visual quality and enhance the performance of UAV visual navigation. In this paper, we propose a novel fully convolutional network based on the Retinex theory to solve the degradations of low-light images captured by UAV, which can improve the visual quality of the images and visual matching performance effectively. At the same time, a visual navigation system is designed based on the proposed network. Extensive experiments demonstrate that our method outperforms the existing methods by a large margin both quantitatively and qualitatively, and effectively improves the performance of the image matching algorithms. The visual navigation system can successfully realize the self-localization of UAV under different illumination conditions. Moreover, we also prove that our method is also effective in other practical tasks (e.g. autonomous driving).

Indoor Unmanned Aerial Vehicle Navigation System Using LED Panels and QR Codes

In this study, we propose an unmanned aerial vehicle (UAV) navigation system using LED panels and QR codes as markers in an indoor environment. An LED panel can display various patterns; hence, we use it as a command presentation device for UAVs, and a QR code can embed various pieces of information, which is used as a sign to estimate the location of the UAV on the way of the flight path. In this paper, we present a navigation method from departure to destination positions in which an obstacle lies between them. In addition, we investigate the effectiveness of our proposed method using an actual UAV.

Retracted: An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development

AbstractThe popularity of unmanned aerial vehicles (UAVs) kept growing over the past few decades. UAVs are widely used in various fields for monitoring, mapping, aerial photography, rescue operations, and so forth. However, the UAV navigation may be difficult in areas where global positioning systems is not available. This article presents an analytical review of the promising methods of creating a navigation system based on a pattern recognition algorithm. Linear algebra and mathematical morphology to describe the operation of pattern recognition applications in UAV's navigation systems, classification of images through profound research of intelligent navigation systems, analysis and digital image processing to identify options for the implementation of artificial intelligent systems of visual navigation were used to identify existing pattern recognition applications in the process of constructive systematization. It was shown that the UAV navigation requires objects in the image to be recognized and the distance to a possible obstacle to be estimated using deep learning method for image segmentation and depth map estimation. An obligatory initial point in the image recognition algorithm is the image preprocessing procedures based on machine vision techniques. The results of this study may be useful in developing efficient UAV applications for military and civil purposes.

Simulation of platform-free inertial navigation system of unmanned aerial vehicles based on neural network algorithms

The object of research is the process of controlling the trajectory of unmanned aerial vehicles (UAVs) in autonomous flight mode based on neural network algorithms. The study is based on the application of numerical-analytical approach to the selection of modern technical solutions for the construction of standard models of platformless inertial navigation systems (BINS) for micro and small UAVs, followed by support for assumptions. The results of simulation in the Matlab environment allowed to simulate the operation of the UAV control system based on MEMS technology (using microelectromechanical systems) and Arduino microcomputers. It was also possible to experimentally determine the nature of the influence of the structure of the selected neural network on the process of formation of navigation data during the disappearance of the GPS signal. Thus, to evaluate the effectiveness of the proposed solutions for the construction of BINS, a comparative analysis of the application of two algorithms ELM (Extreme Learning Machine)-Kalman and WANN (Wavelet Artificial Neural Network)-RNN (Recurrent Neural Network)-Madgwick in the form of two experiments. The purpose of the experiments was to determine: the study of the influence of the number of neurons of the latent level of the neural network on the accuracy of approximation of navigation data; determining the speed of the process of adaptive learning of neural network algorithms BINS UAV. The results of the experiments showed that the application of the algorithm based on ELM-Kalman provides better accuracy of learning the BINS neural network compared to the WANN-RNN-Madgwick algorithm. However, it should be noted that the accuracy of learning improved with the number of neurons in the structure of the latent level <500, which iincreases computational complexity and increases the learning process time. This can complicate the practical implementation using micro- and small UAV equipment. In addition, thanks to the simulation, the result of the study of the application of the proposed neural network algorithms to replace the input data instead of GPS signals to the input BINS, allowed to estimate the positioning error during the disappearance of GPS signals. Also, the application of the WANN-RNN-Madgwick algorithm allows to approximate and extrapolate the input signals of navigation parameters in a dynamic environment, while the process of adaptive learning in real time.

UAV attitude calculation algorithm based on acceleration correction model

In order to improve the accuracy of attitude of unmanned aerial vehicle (UAV) navigation system in dynamic environment, an attitude calculation algorithm based on acceleration correction model is proposed. First, the acceleration correction model is established to calculate the estimated non-gravitational acceleration and external non-gravitational acceleration to modify the output value of the accelerometer, which reduces the influence of non-gravitational acceleration on the attitude calculation in dynamic environment. Then, the attitude calculation model based on Kalman filter is built, attitude angle calculated by corrected acceleration and magnetometer as measurement of filtering model, and the attitude calculation algorithm based on the acceleration correction model is designed. The experimental results show that the algorithm can reduce the interference of non-gravitational acceleration to attitude calculation, which avoids attitude angle divergence of UAV navigation system in dynamic environment, and improves the accuracy and anti-interference ability of UAV navigation system in dynamic environment.

UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to its small size, swift mobility and low cost. Therefore, the study of guidance, navigation and control (GNC) system of UAV has becoming a popular research direction. Path planning plays an important role in the GNC system. In this paper, a multi-layer path planning algorithm based on reinforcement learning (RL) technique is proposed. Compared to the classic Q-learning, the proposed multi-layer algorithm has a distinct advantage that it collects both global and local information which greatly improves overall performance. The proposed RL algorithm has two layers, the higher layer deals with the local information, which could be considered as a short-term strategy. The lower layer deals with the global information, which could be considered as a long-term strategy. Both the higher layer and lower layer are working in harmony to plan a collision-free path. B-spline curve approach is applied for on-line path smoothing. Simulation results in different scenarios prove the effectiveness of multi-layer Q-learning algorithm.

Application and development trend of unmanned aerial vehicle navigation technology

UAV (Unmanned Aerial Vehicle) equipment is novel because of its remote automatic control characteristics, so navigation technology is particularly critical. It is necessary to study and apply UAV technology. This paper first analyzes the UAV navigation technology, then the different types of UAV navigation system, and finally looks forward to the development direction of UAV navigation system, hoping that through this study, we can have a general understanding of the application and development trend of UAV navigation technology, and hope that the relevant research can be further enriched and improved to guide practical application.

Навигационно-пилотажная система беспилотного летательного аппарата для мониторинга лесных пожаров

Forest fires are a common natural disaster all over the world. A great number of fires occur annually in the forest ecosystems of the European North of Russia for natural reasons and as a result of anthropogenic impacts. One of the urgent problems of forestry is the organization of effective control of forest fires. Herewith, it is important to quickly detect the source of fire, as well as to monitor the development of the fire, and to coordinate the actions of the staff of the ground forest protection services. Unmanned aerial vehicles (UAV) of different classes are increasingly used for these purposes. For forestry experience and, especially for forest fire monitoring, the most practical are helicopter-type UAV, which do not require special training of staff. Such devices can operate in the mode of “fire tower” and in the mode of monitoring the edge of the fire with an option to reset information pennants at certain points. The use of UAV is an effective means of monitoring the fire situation in addition to existing methods and technologies, and especially in cases of impossibility of using highresolution satellite images for operative tasks. For the effective operation of UAV in the area of active forest fires and, thus, strong smoke, navigation and piloting systems are necessary to perform safe flights outside the action of the ground control panel located at the starting point. The aim of this work is to develop a navigation and piloting system for UAV, which can be used in the area of limitation of ground control point. The use of such systems will allow monitoring of the fire situation in real time, which is especially important in the organization of protection and suppression of forest fires in large areas of nature reserves, where the priority is to preserve the biological diversity of natural ecosystems and unique landscapes. This article may be of interest to forestry specialists, developers of UAV and equipment, fire protection and the Ministry of Emergency Situations staff, as well as to engineering students to gain experience with UAV.

Optimizing Strap-down Inertial Navigation Systems Characteristics and External Correction Sensors for Autonomous Navigation of Different UAV Classes

Currently, unmanned aerial vehicles (UAVs) can be used in topographic works, condition monitoring and diagnostics of extended engineering structures, delivering goods to hard-to-reach places, etc. To provide the widespread UAVs applications and raise the number of tasks to be solved through their using, it is necessary to increase their autonomy degree in terms of navigation support, in particular. Unmanned aerial vehicles (UAV) control systems for autonomous navigation use the strap-down inertial navigation systems (SINS) based on various types of gyroscopes. SINS based on the laser gyroscopes, which have a large mass, have the best accuracy. UAVs with a payload mass that is commensurable with the mass of navigation equipment require optimization of SINS characteristics. An optimization method has been developed to enable obtaining a Pareto set for the mass and accuracy of SINS based on laser gyroscopes. A comprehensive assessment of the characteristics of SINS and UAV carrier with different payload mass has been performed. Various SINS correction methods are considered when satellite navigation is unavailable.For overland flights, the correlation-extreme navigation systems (CENS) and SLAM methods (for simultaneous localisation and mapping) can be used. CENS require a reference lay-of-the-land description and a sufficient density of landmarks. In navigation based on SLAM algorithms, there is no need in the reference lay-of-the-land description, and the initial data can be obtained through the optical sensors under appropriate condition of the atmospheric path.Regardless of the condition of the atmospheric path, type of the underlying surface and its information available in detail, the UAV coordinates can be determined by Doppler dead reckoning using a Doppler system (DISS). At low and medium altitudes SINS correction is possible, only heading sensor data are needed to calculate the path angle.In combining with DISS and 3D Flash Ladar sensors (for implementing SLAM algorithms), it is more optimal to use low-accuracy SINS based on fibre-optic gyroscopes rather than laser gyro-based systems.The results obtained can be used when developing navigation systems for medium, light and heavy-medium UAVs.

Error Overboundings of KF-Based IMU/GNSS Integrated System Against IMU Faults.

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper studies the error overboundings of the state estimation of the extended Kalman filter (EKF) in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault condition, which can be used to assure the integrity of the UAV navigation system. The error overboundings of the error-state inertial navigation equations based EKF (error-state EKF) are obtained according to the IMU faults propagation derivation, which can be expressed as a sum of the terms related to the EKF innovation, the estimated bias, and the remaining position error. It presents the same expression with the error overbounding of the full-state inertial navigation equations based EKF (full-state EKF). Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation efficiency. This study will be extended to the integrity monitoring of multisensor systems.

Observable modes and absolute navigation capability for landmark-based IMU/Vision Navigation System of UAV

This paper focuses mainly on the analysis of observable modes and absolute navigation capability of the landmark-based IMU/Vision Navigation System (IMU/VNS) for Unmanned Aerial Vehicle (UAV). Firstly, the mathematical model of the IMU/VNS is established. Secondly, the observability matrix of the landmark-based IMU/VNS is obtained based on Lie derivative. And the observable modes with different number of landmarks are derived by solving the null space of the observability matrix. Finally, by deriving the navigation parameters of UAV from the observable modes, analysis is made on the absolute navigation capability of the landmark-based IMU/VNS when the absolute positions of the landmarks are available. And simulation results verify the correctness of all the above analysis methods.

Multi-layered optimal navigation system for quadrotor UAV

Purpose This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV). Design/methodology/approach The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS). Findings All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system. Practical implications The proposed controllers are easily implementable on-board and are computationally efficient. Originality/value The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.