Position Of Unmanned Aerial Vehicle Research Articles

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.

Collaborative Tracking and Positioning of Unmanned Aerial Vehicles Over Wireless Sensor Networks

Collaborative Tracking and Positioning of Unmanned Aerial Vehicles Over Wireless Sensor Networks

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.

Reinforcement learning-based formation-surrounding control for multiple quadrotor UAVs pursuit-evasion games

This paper proposes a reinforcement learning-based formation-surrounding control method for multiple quadrotor unmanned aerial vehicles (UAVs) pursuit-evasion (MPE) games system subject to external disturbances. In the framework of the MPE games, the pursuers aim to equally surround the evaders which try to avoid being surrounded when forming the desired formation. By constructing position and attitude tracking error subsystems of quadrotor UAV, this paper proposes two control strategies which combines the feedforward control technique and reinforcement learning (RL) method. First, two novel cost functions are presented for the quadrotor UAV with external disturbances. Then, two control schemes based on RL have been developed to guarantee the stability of the tracking error subsystem. Subsequently, two critic-only neural networks (NN) weight update laws that only satisfy finite excitation conditions are proposed to estimate the optimal cost function. Furthermore, Nash equilibrium for multiple quadrotor UAVs is achieved by means of RL strategy to solve the Hamilton-Jacobi-Isaacs (HJI) equations. And the property of equally surrounding is proved for the first time by utilizing Euler's formula in this paper. Finally, the numerical simulation results are given to show the effectiveness and superior performance of the proposed control method.

A visual positioning model for UAV’s patrolling video sequence images based on DOM rectification

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.

Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems.

This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It is assumed that the six-rotor UAV systems are controlled by a human operator sending command signals to the leader. A disturbance observer and radial basis function neural networks (RBF NNs) are applied to address the problems regarding external disturbances and uncertain nonlinear dynamics, respectively. In addition, the proposed finite-time command filtered (FTCF) backstepping method effectively manages the issue of "explosion of complexity," where filtering errors are eliminated by the error compensation mechanism. In addition, an event-triggered mechanism is considered to alleviate the communication burden between the controller and the actuator in practice. It is shown that all signals of the six-rotor UAV systems are bounded and the consensus errors converge to a small neighborhood of the origin in finite time. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

3D relative directions based evolutionary computation for UAV-to-UAV interaction in swarm intelligence enabled decentralized networks

Swarm intelligence (SI) is the collective behavior of several intelligent agents (IAs), and unmanned aerial vehicles (UAV) or drones are widely used as IA in SI, of which UAV-based decentralized networks are one. Since the UAV can move in different directions without any surface in the 3D environment, the problem of UAV-to-UAV (UAV2UAV) based interaction is observed in UAV-based SI processes. In a UAV-based network, each UAV needs to follow the 3D flying pattern, and if there is any problem in determining the 3D position pattern, the network loses its arrangement, which may cause chaos in the entire network system. 3D movement of biological animal is observed based on these six relative directions: right, left, up, down, back, and front. The aim of this study is to establish an IA-enabled interaction based on the mentioned 6 relative directions in the UAV-based SI process to overcome the raised issues. First, the article presents various relative directional 3D positions of a UAV and then proposes a novel UAV-to-UAV Relative Directional Interaction (UAV2UAV-RDI) model. Here, two different algorithms are performed on two different UAVs to execute an optimization-based evolutionary computation for detecting and tracking the relative directional 3D position of a UAV. Consequently, according to the findings of this research, the interaction between two UAVs occurs and one UAV can track the relative directional 3D position of the other UAV. The performance of the proposed model is compared with the three models where the proposed model performed more directionally. Additionally, we also point out the limitations of the proposed model for future improvements.

K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks

Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved while reducing the overall communication overhead and increasing the packet delivery ratio(PDR%). In the routing phase, the proposed scheme employs inter-cluster forwarding nodes to transmit messages among different clusters. Extensive simulations demonstrate the performance of the proposed KMORP, showing a 38% better PDR compared to OLSR and over 50% less end-to-end(E2E) delay compared to typical K-Means. Furthermore, the proposed KMORP exhibited an average throughput of 955 kbps, showing a substantial improvement in network performance. The results underscore that the proposed KMORP outperforms existing techniques in terms of PDR, E2E delay, and throughput.

3D deployment of UAV-mounted base stations for heterogeneous access requirements

Recently, unmanned aerial vehicles (UAVs) have been reported a lot as aerial base stations (BSs) to assist wireless communication in Internet of Things (IoT). However, most results for UAV deployment require uniform access requirements and obstacle-free environment. This paper considers multi-UAV-mounted BSs deployment problem in obstacle-laden environment to achieve on-demand coverage for ground user equipments (UEs), with the goal of minimizing the number of required UAVs for full coverage of all UEs by optimizing the three-dimensional (3D) positions of UAVs as well as UE clustering. On one hand, the considered problem is challenging as the constraints on the non-uniform Quality of Service (QoS) requirements, the service ability of each UAV, the beamwidth of the directional antenna equipped by each UAV and obstacle avoidance are jointly taken into account. On the other hand, these practical considerations contribute to broadening the scope of applications. We formulate the problem as a mixed-integer programming problem and develop a three-step placement algorithm to achieve the goal. First, the maximum allowed service radius of each UE is derived based on the Karush-Kuhn-Tucker (KKT) condition to satisfy required signal-to-interference-plus-ratio (SINR) threshold. Second, a greed-prior chaotic search enabled adaptive artificial bee colony (GP-CA2BC) algorithm is proposed to group the UEs such that the minimum number of required UAVs can be obtained. Third, the 3D position of each UAV is optimized to further enhance the QoS. Finally, some simulation results are presented to demonstrate the superiority of the proposed scheme in decreasing the number of required UAVs as well as improving the QoS compared with the existing works.

Performance Improvement in UAV Communication Systems With Uncertain Solar Energy Supply

In this paper, we study a problem of improving the communication performance in a solar-assisted UAV (unmanned aerial vehicle) communication system (SA-UCS) where UAVs equipped with solar panels are deployed to provide communication services to ground users in a target area. Previous works have ignored the inherent uncertainty in solar energy supply caused by naturally unpredictable weather conditions and UAV vibrations, consequently degrading communication performance in SA-UCSs. To bridge the gap, we propose to explore the extreme value theory to tackle uncertain solar energy supply. Specifically, we formulate a throughput maximization problem that is subject to transmission power, link capacity, UAV position, and minimum residual energy. To handle the uncertainty in solar energy supply, we propose to find the GEV (generalized extreme value) distribution of the minimum amount of harvested solar energy, in order to bound the solar energy supply in extreme cases. To solve the formulated problem, which is in general non-convex, we develop an iterative optimization method that first decomposes the originally formulated problem into two sub-problems being solved alternately at each iteration through employing the successive convex approximation (SCA) technique. We perform extensive simulations using real-world data from the National Solar Radiation Database NSRD18. The simulation results corroborate the throughput enhancement with guaranteed energy supply available for UAV communications. Additionally, the results also show the fast convergence achieved by the developed iterative method.