Drones

Deep reinforcement learning technology applied to three-dimensional Unmanned Aerial Vehicle (UAV) air game maneuver decision-making often results in low utilization efficiency of training data and algorithm convergence difficulties. To address these issues, this study proposes an expert experience storage mechanism that improves the algorithm’s performance with less experience replay time. Based on this mechanism, a maneuver decision algorithm using the Dueling Double Deep Q Network is introduced. Simulation experiments demonstrate that the proposed mechanism significantly enhances the algorithm’s performance by reducing the experience by 81.3% compared to the prioritized experience replay mechanism, enabling the UAV agent to achieve a higher maximum average reward value. The experimental results suggest that the proposed expert experience storage mechanism improves the algorithm’s performance with less experience replay time. Additionally, the proposed maneuver decision algorithm identifies the optimal policy for attacking target UAVs using different fixed strategies. Full article

In this paper, we consider a drone deployment problem in situations where the number of drones to be deployed is small compared to the number of users on the ground. In this problem, drones are deployed in the air to collect information, but they cannot collect information from all ground users at once due to the limitations of their communication range. Therefore, the drones need to continue to move until they collect the information for the all ground users. To efficiently realize such drone deployment, we propose two deployment methods. One is an integer linear programming (ILP)-based deployment method and the other is an adjacent deployment method. In the ILP-based deployment method, the positions of the drones at each point in time are determined by solving an ILP problem in which the objective function is the total number of users from whom data can be collected. In contrast, in the adjacent deployment method, drones are sequentially deployed in areas with probabilities determined according to the number of user nodes in adjacent areas at which other drones are already deployed. Through numerical experiments, we show that these deployment methods can be used to efficiently collect data from user nodes on the ground. Full article

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations. Full article

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice. Full article

A large part of small-sized UAVs that are used for surface scanning, video- and photography, or other similar applications are of the multirotor type. These small aircraft perform mainly in hovering or nearly hovering flight mode, and the endurance of these vehicles depends greatly on the efficiency of their motors and the aerodynamic efficiency of their thrust-generating systems, including propellers, ducted fans, etc. Propellers may therefore work in different regimes: in a regime where the propeller performs work to move the vehicle through the air, and the static or hovering regime, in which standing air is accelerated. In both cases, the concept of efficiency can be used to describe the propeller’s performance. There have been several previous studies on static and advancing propellers’ performances. In these studies, when determining the efficiency of a static propeller, the thrust and power coefficients are most commonly compared to evaluate the propeller’s performance. Sometimes, the inducted velocities are calculated via the momentum theory. As small-scale propellers work on very low Reynolds (Re) numbers below 500,000, the flow type transition and boundary layer separation make it very hard to predict the actual efficiency of the propellers in static mode. Therefore, the aim of this paper is to introduce a method to determine the static efficiency of small-scale propellers directly and empirically via a comparison between the output and input power, wherein the output power is determined via the measured thrust and mean induced velocity. The used method combines thrust, torque, and angular velocity measurements with slipstream monitoring. The performed tests showed a decrease in efficiency, with the Re number rising in spite of the rising values of the thrust coefficient. This study led to two main conclusions: thrust and power coefficients are not always the key parameters to determine the efficiency of a propeller; the role of the Re number in the propeller’s efficiency is not yet clear and requires further investigation. The presence of Re number effects has been proven in numerous works, but the impact of those effects seems to not be as trivial as the claim that the lower the Re number, the weaker the propeller’s performance. Full article

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs. Full article

The implementation of morphing wing applications in aircraft design has sparked significant interest as it enables the dimensional properties of the aircraft to be modified during flight. By allowing manipulation of the 2D and 3D parameters on the aircraft’s wings, tail surfaces, or fuselage, a variety of possibilities have arisen. Two primary schools of thought have emerged in the field of morphing wing applications: the mechanisms school and the smart surfaces approach that uses shape-memory materials and smart actuators. Among the research in this field, the Fishbone Active Camber (FishBAC) approach has emerged as a promising avenue for controlling the deflection of the wing’s trailing edge. This study revisits previous research on morphing wings and the FishBAC concept, evaluates the current state of the field, and presents an original design process flow that includes the design of a unique and innovative UAV called the Stingray within the scope of the study. A novel morphing concept developed for the Stingray UAV, Rear Spar Articulated Wing Camber (RSAWC), employs a fishbone-like morphing wing rib design with rear spar articulation in a cost-effective manner. The design process and flight tests of the RSAWC are presented and directly compared with a conventional wing. Results are evaluated based on performance, weight, cost, and complexity. Semi-empirical data from the flight testing of the concept resulted in approximately a 19% flight endurance increment. The study also presents future directions of research on the RSAWC concept to guide the researchers. Full article

Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of the faba bean yield, the dual-sensor (RGB and multi-spectral) data based on unmanned aerial vehicle (UAV) was collected and analyzed. For this, support vector machine (SVM), ridge regression (RR), partial least squares regression (PLS), and k-nearest neighbor (KNN) were used for yield estimation. Additionally, the fusing data from different growth periods based on UAV was first used for estimating faba bean yield to obtain better estimation accuracy. The results obtained are as follows: for a single-growth period, S2 (12 July 2019) had the best accuracy of the estimation model. For fusion data from the muti-growth period, S2 + S3 (12 August 2019) obtained the best estimation results. Furthermore, the coefficient of determination (R²) values for RF were higher than other machine learning algorithms, followed by PLS, and the estimation effects of fusion data from a dual-sensor were evidently better than from a single sensor. In a word, these results indicated that it was feasible to estimate the faba bean yield with high accuracy through data fusion based on dual-sensor data and different growth periods. Full article

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios. Full article

In this paper, we investigate the deep learning applications of radio automatic modulation recognition (AMR) applications in unmanned aerial vehicle (UAV)-to-ground AMR systems. The integration of deep learning in a UAV-aided signal processing terminal can recognize the modulation mode without the provision of parameters. However, the layers used in current models have a small data processing range, and their low noise resistance is another disadvantage. Most importantly, large numbers of parameters and high amounts of computation will burden terminals in the system. We propose a multi-subsampling self-attention (MSSA) network for UAV-to-ground AMR systems, for which we devise a residual dilated module containing ordinary and dilated convolution to expand the data processing range, followed by a self-attention module to improve the classification, even in the presence of noise interference. We subsample the signals to reduce the number of parameters and amount of calculation. We also propose three model sizes, namely large, medium, and small, and the smaller the model, the more suitable it will be for UAV-to-ground AMR systems. We conduct ablation experiments with state-of-the-art and baseline models on the common AMR and radio machine learning (RML) 2018.01a datasets. The proposed method achieves the highest accuracy of $97.00\%$ at a 30 dB signal-to-noise ratio (SNR). The weight file of the small MSSA is only 642 KB. Full article

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera. Full article

Future wireless communications have been envisaged to benefit from integrating drones and free space optical (FSO) communications, which would provide links with line-of-sight propagation and large communication capacity. The theoretical performance analysis for a drone-assisted downlink FSO system is investigated. Furthermore, this paper utilizes the Málaga distribution to characterize the effect of atmospheric turbulence on the optical signal for the drone–terrestrial user link, taking into account atmospheric attenuation, pointing errors, and angle-of-arrival fluctuations. The probability density function and cumulative distribution function are then expressed in closed-form using the heterodyne detection and indirect modulation/direct detection techniques, respectively. Thereafter, the analytical expressions including the average bit error rate (BER) and the ergodic capacity are given. Particularly, the asymptotic behavior of the average BER of the considered system is presented using heterodyne detection at high optical power. The Monte Carlo simulation results certify the theoretical analytical results. Correspondingly, the field-of-view of the receiver is analyzed for optimal communication performance. Full article

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm. Full article

Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is neglected. We develop a framework for traffic speed extraction and prediction based on UAV imagery processing, which consists of two parts: a traffic information extraction module based on UAV imagery recognition and a traffic speed prediction module based on deep learning. First, we use deep learning methods to automate the extraction of road information, implement vehicle recognition using convolutional neural networks and calculate the average speed of road sections based on panchromatic and multispectral image matching to construct a traffic prediction dataset. Then, we propose an attention-enhanced traffic speed prediction module that considers the spatiotemporal characteristics of traffic data and increases the weights of key roads by extracting important fine-grained spatiotemporal features twice to improve the prediction accuracy of the target roads. Finally, we validate the effectiveness of the proposed method on real data. Compared with the baseline algorithm, our algorithm achieves the best prediction performance regarding accuracy and stability. Full article

In recent years, low-power wide-area network (LPWAN) has received widespread popularity with long-range and wide-area communication at low power for the Internet of Things (IoT) systems. Among many vendors of LPWAN, long-range low-power wireless communications, also called LoRa, is one of the competing standards and is well known in both academia and industrial communities as an emerging research area. Among the LoRa applications, unmanned aerial vehicles (UAV) systems are emerging with the benefits of extended battery life and a long communication range. In this paper, we investigate the network capacity with the mixture of concurrent and sequential transmission (MCST) scheme over LoRa networks. From the simulation results, it can be seen that MCST is suitable for implementation in the LoRa network. Specifically, MCST can achieve higher throughput with low transmission latency and energy consumption compared to the existing CSMA approach LoRa MAC. Besides, we also propose a modified MCST over the LoRa (mMCST/LoRa) scheme to mitigate the transmission latency further. The simulation results reveal a better performance in terms of throughput, latency and energy consumption, regardless of the frame payload size and the number of nodes in the network. Full article

The navigation of small unmanned aerial vehicles (UAVs) mainly depends on global positioning systems (GPSs). However, GPSs are vulnerable to attack by spoofing, which causes the UAVs to lose their positioning ability. To address this issue, we propose a deep learning method to detect the spoofing of GPS signals received by small UAVs. Firstly, we describe the GPS signal dataset acquisition and preprocessing methods; these include the hardware system of the UAV and the jammer used in the experiment, the time and weather conditions of the data collection, the use of Spearman correlation coefficients for preprocessing, and the use of SVM-SMOTE to solve the spoofing data imbalance. Next, we introduce a PCA-CNN-LSTM model. We used principal component analysis (PCA) of the model to extract feature information related to spoofing from the GPS signal dataset. The convolutional neural network (CNN) in the model was used to extract local features in the GPS signal dataset, and long short-term memory (LSTM) was used as a posterior module of the CNN for further processing and modeling. To minimize randomness and chance in the simulation experiments, we used the 10-fold cross-validation method to train and evaluate the computational performance of our spoofing machine learning model. We conducted a series of experiments in a numerical simulation environment and evaluated the proposed model against the most advanced traditional machine learning and deep learning models. The results and analysis show that the PCA-CNN-LSTM neural network model achieved the highest accuracy (0.9949). This paper provides a theoretical basis and technical support for spoofing detection for small-UAV GPS signals. Full article

To improve the autonomous flight capability of endo-atmospheric flight vehicles, such as cruise missiles, drones, and other small, low-cost unmanned aerial vehicles (UAVs), a novel minimum-effort waypoint-following differential geometric guidance law (MEWFDGGL) is proposed in this paper. Using the classical differential geometry curve theory, the optimal guidance problem of endo-atmospheric flight vehicles is transformed into an optimal space curve design problem, where the guidance command is the curvature. On the one hand, the change in speed of the flight vehicle is decoupled from the guidance problem. In this way, the widely adopted constant speed hypothesis in the process of designing the guidance law is eliminated, and, hence, the performance of the proposed MEWFDGGL is not influenced by the varying speed of the flight vehicle. On the other hand, considering the onboard computational burden, a suboptimal form of the MEWFDGGL is proposed to solve the problem, where both the complexity and the computational burden of the guidance law dramatically increase as the number of waypoints increases. The theoretical analysis demonstrates that both the original MEWFDGGL and its suboptimal form can be applied to general waypoint-following tasks with an arbitrary number of waypoints. Finally, the superiority and effectiveness of the proposed MEWFDGGL are verified by a numerical simulation and flight experiments. Full article

Unmanned aerial vehicles (UAVs) are promising in large-area data collection due to their flexibility and easy maintenance. In this work, we study a UAV-enabled wireless sensor network (WSN), where K UAVs are dispatched to collect a certain amount of data from each node on the ground. Most existing works assume that the flight energy is either distance-related or duration-related, which may not suit the practical scenario. Given the practical speed-related flight energy model, we focus on deriving the optimal energy and delay tradeoff for the K UAVs such that each node can successfully upload a certain amount of data to one of the K UAVs. Intuitively, the higher flight speed of the UAV results in the shorter completion time of the data collection task, which may however cause the higher flight energy consumption of UAVs during the task. Specifically, we first model the total energy consumption of the UAV during the flight for collecting data within the WSN and then design the flight speed as well as the flight trajectory of each UAV for achieving different Pareto-optimal tradeoffs between the maximum single-UAV energy consumption among all UAVs and the task completion time. To achieve this goal, we propose a novel multi-objective ant colony optimization framework based on the adaptive coordinate method (MOACO-ACM). Firstly, the adaptive coordinate method is developed to decide the nodes visited by each of the K UAVs, respectively. Secondly, the ant colony algorithm is incorporated to optimize the visiting order of nodes for each UAV. Finally, we discuss the impact of UAVs’ speeds scheduling on the tradeoff between the task completion time and the maximum single-UAV energy consumption among all UAVs. Extensive simulations validate the effectiveness of our designed algorithm and further highlight the importance of UAVs’ flight speeds in achieving both energy-efficient and time-efficient data collection. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems. Full article