Unmanned Aerial Vehicle Environment Research Articles

Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning

The sparrow search algorithm (SSA) is a meta-heuristic optimization algorithm based on the predatory behavior of sparrows. However, SSA tends to fall into the local optimum when solving optimization problems with complex constraints. To improve its optimization efficiency and overall performance, this paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory. Specifically, the wrap-around L2-discrepancy (WD), as a uniformity metric for uniform design of experiments, is fully utilized with macro-regulation, adaptive dynamic management strategy, and boundary redistribution management mechanism to quantify the population uniformity in each iteration of ISSA. Inspired by the concept of uniform design, WD is initially adopted to gauge population uniformity, and the threshold acceptance (TA) algorithm is employed to produce an initial population with improved uniformity, hence augmenting the population’s diversity and quickening the rate of convergence. Secondly, a macroscopical individual iterative strategy of the producer is adjusted to avoid the population converging to the origin. Then, a dynamic population uniformity affiliation function based on WD is introduced to adjust the population uniformity affiliation function according to the relative amount of change in the global optimum, and the number of danger perceivers is adjusted according to the affiliation function. What is more, a new boundary update strategy is also proposed in ISSA based on population uniformity. By comparing ISSA on 23 standard test functions and recently updated validation function set CEC2022 with the original SSA, and some classical as well as newly developed algorithms, the superiority of the present ISSA is thoroughly confirmed. As an application case, the ISSA algorithm is utilized to solve the path planning problem of the complex environment of unmanned aerial vehicle (UAV) based on threat models by applying it to 2D maps containing circular and polygonal obstacles, as well as 3D maps containing mountain peaks and cylindrical obstacles. The simulation results show that ISSA can find more effective routes through various environments with obstacles.

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

Numerical investigation on effects of low Reynolds number conditions on fan shaped film cooling performances

This study investigates the effects of low Reynolds number (Re) conditions on the cooling performance of fan-shaped film cooling holes in the gas turbine engines of high-altitude long-endurance Unmanned Aerial Vehicles (UAVs). The significance of film cooling technology becomes paramount due to the low Re operational environment of UAVs. A novel Very Large Eddy Simulation (VLES) method was employed to systematically analyze a range of mainstream Reynolds numbers (ReD) from 480 to 32 000 and blowing ratios (M) from 1.0 to 2.5 while keeping the density ratio (DR) constant at 1.75. Compared to other turbulence models, the VLES model showed the highest similarity in predicting thermal field distributions and the most accurate prediction of film cooling performance at low Re. The results reveal that at low ReD, significant flow concentration within the hole leads to reduced velocity uniformity at the outlet, resulting in poor film coverage downstream. Additionally, the coolant jet tends to detach from the wall, adversely affecting cooling performance. In contrast, higher ReD exhibited improved coolant jet adhesion to the wall and cooling efficiency, attributed to the reduced intensity of counter-rotating vortex pair and decreased hot gas entrainment, thereby enhancing cooling effectiveness. Notably, for the same ReD values, cases with M = 1.5 consistently showed better cooling effectiveness compared to other M conditions. This study provides new insight into the cooling performance of fan-shaped film cooling holes under low Reynolds number conditions, which is crucial for enhancing the cooling efficiency of gas turbines in high-altitude UAVs.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Leader–follower UAVs formation control based on a deep Q-network collaborative framework

This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon$$\\end{document}-imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.

A message verification scheme based on physical layer-enabled data hiding for flying ad hoc network

The use of Unmanned Aerial Vehicles (UAVs) for military as well as civilian applications such as search and rescue operations, disaster management, parcel delivery and agriculture, is becoming increasingly popular, mainly due to their versatile nature and relatively inexpensive operating costs. However, one of the most significant barriers in the widespread adoption of UAVs for the aforementioned applications is the increasing concern for wireless communication security within the network. Unfortunately, the nodes that comprise these networks are extremely constrained in terms of storage space, processing power and battery life, which require light-weight security protocols that align with these limitations. The current standards for message authentication and verification predominantly rely on lightweight cryptographic techniques. However, there is continual scope for improvement, particularly in the realm of computational efficiency, to better align with the constraints inherent to UAVs. This paper explores data hiding in a multi-UAV environment, to form a light-weight message verification scheme to confirm the identity of the transmitting node. First, five venues having the potential to hide data are analyzed. Based on the analysis of bit error rate obtained from the simulated multi-UAV environment, both cyclic prefix and padding bits are selected. Next, a codeword is generated for each transmission and it is embedded along with the unique ID key, κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa $$\\end{document} of the transmitting node into each venue respectively, and the output is transmitted as Hello packets. Subsequently, the receiving node extracts and decodes the codeword in order to verify the authenticity of the transmitting node. An evaluation of the proposed scheme has been conducted by using a simulated UAV environment. In the best case scenario, the proposed authentication scheme can achieve a successful verification rate of at least 80% at a maximum hop-count of 10 hops. Through computational cost analysis, in comparison to the conventional methods, the proposed scheme was found to have a significantly lower total execution time of 1.7μs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.7\\mu s$$\\end{document}. In addition, the security analysis shows that when the proposed scheme is paired with physical unclonable functions (PUFs), it is able to resist common security attacks, including man-in-the-middle, replay and cloning attacks.

Multi-sensor data fusion for autonomous flight of unmanned aerial vehicles in complex flight environments

The flight environment of unmanned aerial vehicles faces various challenges. To effectively navigate and perform tasks, they need to effectively integrate multiple sensors. This study applies the adaptive weighted average method, combined with data from global positioning system, inertial measurement unit, three-dimensional optical detection and ranging, and uses linear Kalman filtering to smooth the merged velocity data. High-order B-spline curves for route planning and applying flight constraint formulas to better adapt are used to the dynamics of unmanned aerial vehicles. The research results indicated that the improved adaptive weighting algorithm had high comprehensive performance for multi-sensor data fusion, with the highest accuracy, robustness, real-time performance, and consistency of 94.2%, 93.7%, 100%, and 95.6%, respectively. The flight path lengths planned by the A* algorithm and higher-order B-spline curve were 15.7 and 16.3 m, respectively, and the flight time was 8.2 and 7.1 s, respectively. The flight path planned by higher-order B-spline curve was further away from obstacles. The use of adaptive weighted fusion and linear Kalman filtering facilitates the fusion of multi-sensor data, and autonomous flight routes planned using high-order B-spline curves can also meet the needs of unmanned aerial vehicle flight in complex flight environments.

Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing

Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system.

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Most conventional speech recognition systems have mainly concentrated on voice-driven control of personal user devices such as smartphones. Therefore, a speech recognition system used in a special environment needs to be developed in consideration of the environment. In this study, a speech recognition framework for voice-driven control of unmanned aerial vehicles (UAVs) is proposed in a collaborative environment between manned aerial vehicles (MAVs) and UAVs, where multiple MAVs and UAVs fly together, and pilots on board MAVs control multiple UAVs with their voices. Standard speech recognition systems consist of several modules, including front-end, recognition, and post-processing. Among them, this study focuses on recognition and post-processing modules in terms of in-vehicle speech recognition. In order to stably control UAVs via voice, it is necessary to handle the environmental conditions of the UAVs carefully. First, we define control commands that the MAV pilot delivers to UAVs and construct training data. Next, for the recognition module, we investigate an acoustic model suitable for the characteristics of the UAV control commands and the UAV system with hardware resource constraints. Finally, two approaches are proposed for post-processing: grammar network-based syntax analysis and transaction-based semantic analysis. For evaluation, we developed a speech recognition system in a collaborative simulation environment between a MAV and an UAV and successfully verified the validity of each module. As a result of recognition experiments of connected words consisting of two to five words, the recognition rates of hidden Markov model (HMM) and deep neural network (DNN)-based acoustic models were 98.2% and 98.4%, respectively. However, in terms of computational amount, the HMM model was about 100 times more efficient than DNN. In addition, the relative improvement in error rate with the proposed post-processing was about 65%.

Enhancing security and robustness of Cyphal on Controller Area Network in unmanned aerial vehicle environments

A controller area network (CAN) has been widely deployed in automotive environments due to the decentralized environment which enables the easy control of many components in a vehicle. The CAN is now being adopted in other areas, including aviation and robots, especially unmanned aerial vehicles (UAV). Cyphal has been developed for years to provide communication over the CAN in UAV environments. For instance, the plug-and-play mode of Cyphal enables the automated configuration of the unique source IDs to nodes on the CAN bus. This reduces the overhead to assign static nodes to each node newly attached to the CAN bus. As such a configuration may occur the collision of the frames to the small size of the source ID, Cyphal currently recommends the use of such features only for non-critical applications. While attaching a new node to the CAN bus needs the authentication process to prevent the unauthorized devices attached and used by adversaries, Cyphal does not consider such security issues. To address these issues, we propose a security design to improve the robustness and security of the plug-and-play mode in Cyphal. Our proposal provides the collision avoidance and authentication of modules. To give efficiency in lightweight modules, our design uses the symmetric key-based method. We evaluate how the collision is avoided in our design compared to Cyphal, and also analyze the security of our proposal. Evaluation results demonstrate that our proposed scheme is effective in enabling the plug-and-play mode of Cyphal for the critical environment.

Adaptive Decoding Mechanisms for UAV-Enabled Double-Uplink Coordinated NOMA

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

Idle-Less Slotted ALOHA Protocol for Drone Swarm Identification

In this paper, we modify the slotted ALOHA protocol to quickly identify a swarm of drones in unmanned aerial vehicle (UAV) communication environments. Considering the unreliable UAV channel and the fundamental tradeoff between collision and idle slots in slotted ALOHA, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">idle-less slotted ALOHA</i> (ILS-ALOHA) protocol that detects and utilizes idle slots to transmit control messages to drones. In ILS-ALOHA, the ground control station recognizes the idle slot promptly at the beginning of each slot and then transmits acknowledgment (ACK) and beacon messages during the remaining period of the idle slot. Therefore, it can control the access of the drone swarm more adaptively and feed back the ACK to the drones more frequently in an error-prone UAV environment without allocating additional slots. The analysis and simulation results show that the proposed ILS-ALOHA protocol outperforms the conventional slotted ALOHA-based protocols in terms of identification time, channel throughput, and probability of successful identification.

Modeling of Object Monitoring Using 3D Cellular Automata

The purpose of this work is to develop a formal model for describing the properties of the environment for the functioning of unmanned aerial vehicles and to increase the speed of calculating the trajectory of their flight when monitoring critical infrastructure objects based on the mathematical apparatus of 3D cellular automata. This goal is achieved by solving the following problems: developing a method for describing the operating environment of unmanned aerial vehicles based on 3D cellular automata, developing a method for calculating the flight path of unmanned aerial vehicles. The construction of formal models is based on the apparatus of 3D cellular automata. The most significant results are a formalized description of the space, properties of zones and objects that restrict movement, as well as the development of a method for modeling the flight of an unmanned aerial vehicle in space when solving the monitoring problem, which will increase the speed of calculating the flight path. The significance of the results obtained lies in solving the complex problem of calculating the trajectory of movement of unmanned aerial vehicles for monitoring critical infrastructure objects using the apparatus of 3D cellular automata. The conducted studies have shown the effectiveness of using 3D - cellular automata to solve the problems of finding flight paths when monitoring critical infrastructure objects in various conditions. The proposed approach to the implementation of cellular automata will allow creating an effective monitoring system.

Synthetic Image Generation for Data Augmentation to Train an Unconscious Person Detection Network in a UAV Environment

In this paper, we propose a data augmentation method using synthetic data generation for detecting an unconscious person in drone images. First, we extract the most salient and delicate foreground mask from a reference image that simulates an unconscious person situation using U² Net, which is a Salient Object Detection (SOD) model. Second, we apply shadow generation to the foreground mask for the natural appearance of the object. The unconscious person object generated by the foreground mask is synthesized with the background image of the Unmanned Aerial Vehicle (UAV) environment according to the altitude using object resizing. Therefore, we generate the most similar data to the image acquired by the drone. We verified the synthetic data-based image dataset using various object detection models, such as YOLOv4, YOLOv5, and EfficientDet. As a result, the Average Precision (AP) is higher than that of the real-world dataset. Our proposed method could be used to generate synthetic data for detecting an unconscious person and reducing the time cost and human resources needed for various tasks.

Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment

Unmanned aerial vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt visual line of sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs&#x2019; commercial operations to cover the urban and populated area-controlled airspace beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs&#x2019; autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a multiaccess-edge computing (MEC) that can read the gathered data from different UAV&#x2019;s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This article proposes probabilistic and deep-reinforcement-learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

Path planning in unmanned aerial vehicles: An optimistic overview

SummaryWith the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

Monocular Depth Estimation With Improved Long-Range Accuracy for UAV Environment Perception

Environment perception by computing the depth is a key task for unmanned aerial vehicle (UAV) type systems. Due to the limited load they can carry, most drones are equipped with a single camera. This prevents general-purpose depth perception methods based either on light detection and ranging (LiDAR) or stereo reconstruction to be effectively used on such platforms. Due to the success of convolutional neural networks (CNNs), monocular depth estimation (MDE) methods have become more and more trustworthy, so their usage on drones is convenient. However, very few such methods have been proposed in the literature, mainly due to the few existing constraints and high diversity that unstructured aerial environments pose. To bridge this gap, we propose a novel approach for MDE, capable to work on aerial images. The method initially proposes an original CNN, particularly adapted to such scenarios. This is done by finding an optimal feature extractor, introducing a new scene understanding module, a new loss and a novel softmax transformation layer that facilitate a better convergence. Furthermore, since both short- and long-range accuracy is required for a robust UAV perception, we introduce a learning-based correction method that redistributes the depth points across the entire depth interval. The proposed CNN gives accurate results, while the additional refinement further improves the accuracy with only a few additional computational resources (around 1–2 ms). We initially show the capabilities of our method on synthetic images captured in unstructured aerial scenarios. Then, we prove that our method can work in real-life situations, computing depth from a single image (at multiple pitch angles) captured by a drone flying in a series of field and forest-like environments. In all these situations, the depth is densely estimated with increased accuracy and reliability.

Low-Complexity Algorithm for Outage Optimal Resource Allocation in Energy Harvesting-Based UAV Identification Networks

We study an unmanned aerial vehicle (UAV) identification network equipped with an energy harvesting (EH) technique. In the network, the UAVs harvest energy through radio frequency (RF) signals transmitted from ground control stations (GCSs) and then transmit their identification information to the ground receiver station (GRS). Specifically, we first derive a closed-form expression of the outage probability to evaluate the network performance. Then we obtain the closed-form expression of the optimal time allocation when the bandwidth is equally allocated to the UAVs. We also propose a fast-converging algorithm for time and the bandwidth allocation, which is necessary for the UAV environment with high mobility, to optimize the outage performance of EH-based UAV identification network. Simulation results show that the proposed algorithm outperforms the conventional bisection algorithm and achieves near-optimal performance.

Marine locomotion: A tethered UAV-Buoy system with surge velocity control

Unmanned aerial vehicles (UAVs) are reaching offshore. In this work, we formulate the novel problem of a marine locomotive quadrotor UAV, which manipulates the surge velocity of a floating buoy by means of a cable. The proposed robotic system can have a variety of novel applications for UAVs where their high speed and maneuverability, as well as their ease of deployment and wide field of vision, give them a superior advantage. In addition, the major limitation of limited flight time of quadrotor UAVs is typically addressed through an umbilical power cable, which naturally integrates with the proposed system. A detailed high-fidelity dynamic model is presented for the buoy, UAV, and water environment. In addition, a stable control system design is proposed to manipulate the surge velocity of the buoy within certain constraints that keep the buoy in contact with the water surface. Polar coordinates are used in the controller design process since they outperform traditional Cartesian-based velocity controllers when it comes to ensuring correlated effects on the tracking performance, where each control channel independently affects one control parameter. The system model and controller design are validated in numerical simulation under different settings, configurations, and wave scenarios.

Towards using UAV for improving 5G cellular communication systems

Unmanned aerial vehicles (UAVs) have sparked a lot of interest in the wireless networking community as an emerging subject in aerial robotics. The UAV environment can be used to improve UAV communications in various ways. These smart devices cater for a broad variety of wireless technologies and applications because of UAV's inherent features related to versatile mobility in 3D space, autonomous operations as well as intelligent positioning. This study will investigate the convergence synergies between 5G/B5G mobile systems and UAV technologies, with the UAV being integrated into current cellular networks as a modern aerial user equipment (UE). In this integration, UAVs play the function of cellular flying customers, and are hence referred to as cellularly linked UAVs (a.k.a. UAVUE, drone-UE, 5G-connected drone, or aerial user). The major goal of this research is to provide a thorough analysis of the integration task, as well as major technical breakthroughs from 5G/B5G and current work in prototyping architecture and field trials that support cell-based UAVs. This study examines recent 3GPP standards advances as well as socio-economic challenges that must be addressed before this promising technology can be properly implemented. There are already some accessible issues clearing the way for potential study opportunities.