Position Of Unmanned Aerial Vehicle Research Articles

A Visual Navigation Algorithm for UAV Based on Visual-Geography Optimization

The estimation of Unmanned Aerial Vehicle (UAV) poses using visual information is essential in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a UAV visual navigation algorithm based on visual-geography Bundle Adjustment (BA) to address the challenge of missing geolocation information in monocular visual navigation. This algorithm presents an effective approach to UAV navigation and positioning. Initially, Visual Odometry (VO) was employed for tracking the UAV’s motion and extracting keyframes. Subsequently, a geolocation method based on heterogeneous image matching was utilized to calculate the geographic pose of the UAV. Additionally, we introduce a tightly coupled information fusion method based on visual-geography optimization, which provides a geographic initializer and enables real-time estimation of the UAV’s geographical pose. Finally, the algorithm dynamically adjusts the weight of geographic information to improve optimization accuracy. The proposed method is extensively evaluated in both simulated and real-world environments, and the results demonstrate that our proposed approach can accurately and in real-time estimate the geographic pose of the UAV in a GNSS-denied environment. Specifically, our proposed approach achieves a root-mean-square error (RMSE) and mean positioning accuracy of less than 13 m.

TROG: a fast and robust scene-matching algorithm for geo-referenced images

ABSTRACT In a GNSS-denied/challenged environment, the scene-matching navigation system (SMNS) is a vital autonomous technology for unmanned aerial vehicles (UAV). This paper proposes a novel template-matching framework for multimodal images since UAV navigation requires precision and real-time performance while utilizing onboard computers with low computational power. Specifically, first, the local descriptor is extracted to form a pixel-wise feature representation of an image. Then, the Fast Fourier Transform is applied to measure the similarity based on the feature representation. In the feature extraction part, a gradient-like pixel-level feature descriptor is designed, which is reconstructed by weighting it according to the gradient-like angles, named the three-dimensional reconstruction oriented gradient-like (TROG) descriptor. An optimized similarity measurement template is introduced in the matching part, which improves the traditional feature-based similarity measurement algorithm defined using Fast Fourier Transform (FFT) in the frequency domain. This strategy eliminates redundant computations during the matching process. To verify the effectiveness of the proposed algorithm, satellite imagery data from Google Earth are used as a reference images, and sensed images (including optical, IR, SAR, and Hyperspectral) are captured by UAV and satellites for image matching to testify to the registration accuracy, robustness, and computational efficiency. The experiment demonstrates that TROG is accurate, robust, and attains high real-time performance, making it applicable to UAV navigation and positioning. Additionally, field-flight experiments evaluate scene-matching navigation under satellite-denied conditions and low computational power conditions for UAVs, demonstrating that our scene-matching navigation system can achieve precise positioning with a positioning error of less than 1.637 m, which is comparable to satellite/inertial navigation systems. The experimental results from outdoor flight experiments highlight the value of our proposed algorithm in engineering applications under satellite denial conditions.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

A novel adaptive super-twisting trajectory tracking control with back propagation algorithm for a quadrotor UAV

This paper presents a new method for controlling a quadrotor unmanned aerial vehicle (UAV) with neural network adaptive adjustment combined with a super-twisting algorithm, which utilizes back-propagation algorithm in neural networks to design an adaptive method that can adjust the coefficients of the sliding mode surface as well as the control gain adjustment adaptive problem in the super-twisting to improve the stability and accuracy of the position and attitude control of the quadrotor UAV under uncertainty and external disturbances. Specifically, the adaptive neural network learns to dynamically adjust the sliding surface parameters and control gain, effectively inhibiting the sensitivity to parameter uncertainty and external disturbances, while the super-twisting sliding mode control ensures that the sliding trajectory converges in finite time and reduces the chattering phenomenon. In addition, the quadrotor UAV system is divided into a fully-actuated subsystem and an under-actuated subsystem, each of which contains two control inputs and the appropriate control algorithms are designed respectively, and the stability of the algorithm is demonstrated by means of a Lyapunov function in finite time. The proposed control method for quadrotor UAVs is validated through numerical simulations conducted in the Matlab/Simulink environment.

Research on optimal deep learning‐based formation flight positioning of UAV

SummaryThe article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights.

Interactive technology of high voltage tower acceptance UAV Based on AR and YOLOv7

To address many shortcomings of the current unmanned aerial vehicle (UAV) inspection mode of the transmission line high-voltage towers and improve the inspection efficiency, the UAV tower foreign matter detection technology based on Augmented Reality (AR) and You Only Look Once: Unified, Real-Time Object Detection (YOLOv7) has been developed. Firstly, the system architecture, including UAV, ground station, and AR glasses is established. Then, the AR scene is built by using the Unity engine, and the interactive control mode between AR glasses and UAV is designed. Subsequently, the mapping mode of head action and UAV command is devised to synchronize the UAV action and head action. Finally, the tower is detected for foreign objects by using YOLOv7 vision detection technology. The experimental results validate the efficacy of the built AR scene, demonstrating seamless functionality in completing the video streaming and attitude data transmission functions. The use of AR glasses can achieve interactive control of the UAV attitude. In addition, the YOLOv7 visual detection algorithm can accurately detect the attachment of foreign objects to the high-voltage tower.

Precision Landing of Unmanned Aerial Vehicle under Wind Disturbance Using Derivative Sliding Mode Nonlinear Disturbance Observer-Based Control Method

Unmanned aerial vehicles (UAVs) are extensively employed in civilian and military applications because of their excellent maneuverability. Achieving fully autonomous quadrotor flight and precision landing on a wireless charging station in the presence of wind disturbance has become a crucial research topic. This paper presents a composite control technique for UAV altitude and attitude tracking in harsh environments, i.e., wind disturbance. A composite controller was developed based on nonlinear disturbance observer (NDOB) control theory to allow the UAV to land in the presence of random external wind disturbances and ground effects. The NDOB estimated the unknown wind disturbance, and the estimation was fed into the derivative sliding mode nonlinear disturbance observer-based control (DSMNDOBC), allowing the UAV to perform autonomous precision landing. Two loop designs were applied: the inner loop for stabilization and the outer loop for altitude tracking. The quadrotor model dynamics and the proposed controller, DSMNDOBC, were simulated employing MATLAB/Simulink®, and the results were compared with the one obtained by the proportional derivative (PD) controller and the sliding mode controller (SMC). The simulation results indicated that the DSMNDOBC has superior altitude and attitude control compared to the PD and SMC controllers and better disturbance estimation and attenuation performance.

Near-field beamforming method based on motion model analysis for UAVs communication

This study presents a near-field beamforming method for the moving swarm composed of unmanned aerial vehicles (UAVs) to address the challenges of position errors that hinder effective beamforming. Firstly, a model with positional errors in moving UAVs was developed. The sources and characteristics of UAVs’ position errors were analyzed and classified into low-frequency motion errors and high-frequency vibration errors, and their effects on UAVs array beamforming were analyzed using coordinate transformation and decomposition. Secondly, a parameter estimation algorithm was proposed based on the Fast Fourier Transformation (FFT) and the Least Square (LS) method to estimate the unknown parameters of the UAV's low-frequency motion errors. Lastly, a near-field beamforming algorithm for UAVs was proposed based on the Interpolation Kalman filter (IKF). The effectiveness of the proposed model and algorithms was verified through simulations, which provided a theoretical basis for the near-field beamforming under the existence of the UAVs position errors.

An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table

Aiming at the complexity and poor adaptability of the calibration process in the traditional unmanned aerial vehicles (UAV) indoor visual positioning, this paper proposes an omnidirectional spatial tracking and localization method for indoor UAV based on the two-axis rotary table. Firstly, the position of the UAV fuselage feature points in the camera coordinate system of the turntable camera is computed by the Pespective-n-Point algorithm utilizing known position information of a plurality of feature points with pixel coordinate information in the corresponding image. Pixel coordinate information is extracted by obtaining UAV body-specific feature points from rotary table camera shots. Then, UAV localization in omnidirectional space can be obtained by using the calibrated rotary axis parameters of the rotary table and the rotation angle of the rotary table and substituting them into Rodriguez’s formula to unify the UAV position information acquired by the rotary table camera at different positions into a unified coordinate system. Finally, the angle at which the rotary table should rotate is calculated from the obtained UAV pose and the spatial position of the camera optical center and the rotary axis of the rotary table. The calculated angle is fed back to the turntable as feedback information. The rotary table receiving the feedback information is rotated to a position where the UAV is located at the center of the camera image. Thereby the tracking and localization of the UAV is realized. The experimental results show that the spatial range of localization is greatly expanded with the localization accuracy reaching the level of binocular visual localization. The omnidirectional spatial tracking and localization of indoor UAV can be conveniently realized by this method.

Privacy-Preserving Location Authentication for Low-altitude UAVs: A Blockchain-based Approach

Efficient and trusted regulation of unmanned aerial vehicles (UAVs) is an essential but challenging issue in the future era of Internet of Low-altitude Intelligence, due to the difficulties in UAVs' identity recognition and location matching, potential for falsified information reporting, etc. To address this challenging issue, in this paper, we propose a blockchain-based UAV location authentication scheme, which employs a distance bounding protocol to establish a location proof, ensuring the authenticity of UAV positions. To preserve the privacy of UAVs, anonymous certificates and zero-knowledge proof are used. The security of the proposed scheme is analyzed. Experiments demonstrate the efficiency and feasibility of the proposed scheme.

ERROR ANALYSIS OF VISUAL ODOMETRY FOR A SMALL SIZE UNMANNED AERIAL VEHICLE

Abstract. Usage of small size Unmanned Aerial Vehicle (UAV) with autonomous flight capability became widespread at both civil and military applications because of its low costs and ease of use. However, inertial or GPS based positioning and orientation sensors can be disabled at battlefields and the UAV losses its path in a short time. Visual odometry becomes a solution for the aforementioned hassle. Real-time detection of ground control points, matching the same control points at the stereo pair images and correcting the position and orientation data with least square adjustment are the challenges of visual odometry. In this study, error analysis of position and orientation of a UAV flying through mountainous region which cannot obtain any augmenting data for position and orientation is conducted by error propagation law. The flight of UAV is simulated with exact initial position and orientation data. Optic images of ground obtained by nadir directed camera are georeferenced using the digital elevation model of the region, interior and exterior camera parameters. The digital elevation model is used to correct the relief displacement. The stereo-pair image is obtained when the UAV flies 200 m and image coordinates of the common control points are detected by colinearity equations. Position and orientation of UAV is corrected by least square adjustment and the process is repeated for each obtained image acquired at 200 m interval. The analysis revealed that the precisions of horizontal and vertical positioning becomes 75 and 200 meter, respectively. The precision of roll angle becomes worse than 0.5 degree, while the precisions of pitch and yaw angle are estimate to be better that 0.1 degree.

A hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures, but current approaches take a long time to cover the entire area. Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments. This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time. The proposed framework consists of two stages, the exploration stage and the exploitation stage, resolving the task in a sequential manner. At the exploration stage, the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs, and the Voronoi-based planner is used to enable the UAVs to reach their best region. After obtaining the global map, viewpoints are generated and divided while taking into account the battery capacity of each UAV. Finally, a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction. Several experiments are conducted in both a simulated and real environment to show the time efficiency, robustness, and effectiveness of the proposed method. Furthermore, the whole system is implemented in real applications.

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

Application of LSTM neural network based UAV attitude control in attitude estimation

From now on, Unmanned Aerial Vehicle (UAV) technologies have become more mature, and UAV has been skillfully used in various regions under this mature technology. However, during UAV flight, they often encounter interference from external factors such as airflow, wind and temperature changes, which pose challenges to their stability and flight accuracy. This paper proposes an algorithm what is based on LSTM (Long, Short-Term Memory network), aiming that the flight attitude is affected when it is subject to external interference, while still ensuring the robustness of its flight performance in this state. In this paper, the optimization method of anti-jamming adaptive adjustment based on LSTM. In the meantime, a UAV dynamic model and neural network control based on dynamic modeling are established. Then article established the LSTM rule and designed the PID controller of LSTM. And to prove the effectiveness of the designed PID controller by pilot experiments with Matlab / simulated link simulation and flight experiments. Finally, step size response, autonomous tracking, robustness and anti-interference were tested by experimental simulations.

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Information Acquisition and Seismic Damage Prediction of Masonry Structures in Rural Areas Based on UAV Inclined Photogrammetry

Using a novel methodology that integrates incremental dynamic analysis (IDA) and unmanned aerial vehicle positioning (POS) analysis, this study aims to assess the seismic risk of brick structures in rural China. This method can collect a lot of data and accurately anticipate seismic damage by combining UAV oblique photography with IDA analysis. Because rural China has many masonry structures, the project will design unique seismic risk mitigation strategies. High-resolution cameras on Unmanned Aerial Vehicles capture realistic photographs of rural brick buildings. The collected data is carefully examined to reveal architectural and structural elements. The project uses dynamic post-processing software from the CHC Geomatics Office to improve UAV-reference station position accuracy. This program analyzes UAV POS data disparities. The findings allow rural Chinese brick buildings to be assessed for seismic sensitivity during unexpected ground shaking occurrences. UAV tilt-photography reduces manpower and expenditures, improving inquiry efficiency. This combination improves seismic risk response. The IDA and UAV POS analysis are essential for earthquake preparedness and risk mitigation. This data-driven method informs lawmakers, urban planners, and disaster management authorities worldwide, improving earthquake engineering and catastrophe resilience programs. This work improves seismic threat assessment and masonry structure fortification, making earthquake-prone buildings safer. Thus, rural communities benefit from it.

Optimal Placement of UAVs to Provide Surveillance Coverage for a Ground Vehicle in a Collaborative Search-and-Rescue Operation

A drone-truck combined search-and-rescue operation involves a ground vehicle and a swarm of unmanned aerial vehicles (UAVs), where the UAVs provide surveillance coverage to guide the ground vehicle to navigate through the environment and carry out the search and rescue, and the ground vehicle functions as a service hub for carrying and recharging the UAVs. An effective strategy for providing persistent UAV surveillance coverage around the ground vehicle consists of initially forming the UAV swarm coverage and then controlling the UAV formation to follow the ground vehicle. This paper focuses on the formation of coverage and presents a method for planning an optimal placement of the UAVs to form seamless surveillance coverage around the ground vehicle. The optimization problem is formulated to determine the number and positions of UAVs that minimize the energy consumption in deploying and collecting those UAVs, subject to a set of constraints in UAV positioning, communication, and coverage, specifically the available number of UAVs, allowable range of UAV altitude, allowable energy consumption for deploying and collecting each UAV, communication ranges of UAVs and ground vehicle, safety distance between UAVs for collision and interference avoidance, and seamless coverage. A bi-layer optimization procedure is developed, with an outer layer searching through the allowable numbers of UAVs and an inner layer searching for the optimal positions for each specific number of UAVs. The optimal number and positions of UAVs are chosen by comparing among the solutions for different numbers of UAVs. A simulation study is carried out to validate the proposed optimization formulation and solution approach, where the simulation settings of UAVs, particularly the critical parameters including the UAV energy constants, visibility angle, altitude, and communication range, use the representative values presented in the cited literature. The simulation results show that the proposed approach is effective in planning the optimal number and positions of UAVs to provide seamless surveillance coverage for a ground vehicle. The next step of research will set priorities on comprehending the complexity of the solution space and enhancing the global optimality of the solution.

Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle for Efficient Placement of Unmanned Aerial Vehicles in Natural Disaster Areas

A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses the Deutsch–Jozsa quantum circuit to generate quantum populations synergistically working as haploid recombination and mutation operators that take advantage of quantum entanglement, providing exploitative and explorative features to produce new individuals. In place of the conventional lookup table or mathematical equation, we introduced a fuzzy heuristic to adapt the rotation angle employed in quantum gates. The hybrid nature of this algorithm becomes evident through its utilization of both classical and quantum computing components. Experimental evaluations were conducted using two distinct test sets. The first set, termed the “best case”, represents conditions that are the most favorable for determining the UAV positions, while the second set, the “worst-case”, simulates highly challenging conditions for locating the UAV positions, thereby posing a significant test for the proposed algorithm. We carried out statistical comparative analyses, assessing the HQGAFARA against other hybrid quantum algorithms that employ different rotation angles and against the classical genetic algorithm. The experimental results demonstrated that the HQGAFARA performed comparably, if not better, to the classical genetic algorithm regarding precision. Furthermore, quantum algorithms showcased their computational prowess in experiments related to the convergence time.

Joint optimization of UAV position and user grouping for UAV‐assisted hybrid NOMA systems

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non‐orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non‐convexity, calling for an effective solution. To address this issue, a two‐stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory‐based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two‐stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.

Improved modeling and fast in-field calibration of optical flow sensor for unmanned aerial vehicle position estimation

To better reveal the principle of off-the-shelf OFS that used for UAV position estimation, two measurement models are derived, namely the continuous-time and discrete-time models. Then the intrinsic equivalence of these two models is also proven. Based on these two models, a calibration method for OFS is introduced, which only makes use of in-situ rotation to determine the OFS’s parameters including its focal length and installation angles with respect to the UAV. Furthermore, an OFS-based UAV position estimation algorithm is presented, which stems from the proposed discrete-time model. The proposed OFS models, calibration method, and position estimation algorithm form a full solution for UAV displacement measurement via OFS, and its feasibility and effectiveness is verified through flight tests on a quadcopter UAV in outdoor environment.