Drones, Volume 6, Issue 9 (September 2022) – 44 articles

A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability. Full article

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones. Full article

This work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the model-based design process; (3) developed appropriate sliding mode control laws for each degree of freedom; (4) implemented disturbance observers on the proposed SMC controller to achieve finer disturbance rejection such as crosswind effect and other mutational disturbances; (5) optimized the SMC controller’s parameters based on particle swarm optimization (PSO) method; and (6) evaluated and compared the quadcopter’s tracking performance under a range of noise and disturbances. Comparisons of PID control strategies against the SMC were documented under the same conditions. Consequently, the SMC controller with disturbance observer facilitates accurate and fast UAV adaptation in uncertain dynamic environments. Full article

Even as human–robot interactions become increasingly common, conventional small Unmanned Aircraft Systems (sUAS), typically multicopters, can still be unsafe for deployment in an indoor environment in close proximity to humans without significant safety precautions. This is due to their fast-spinning propellers, and lack of a fail-safe mechanism in the event of a loss of power. A blimp, a non-rigid airship filled with lighter-than-air gases is inherently safer as it ’floats’ in the air and is generally incapable of high-speed motion. The Spherical Indoor Coandă Effect Drone (SpICED), is a novel, safe spherical blimp design propelled by closed impellers utilizing the Coandă effect. Unlike a multicopter or conventional propeller blimp, the closed impellers reduce safety risks to the surrounding people and objects, allowing for SpICED to be operated in close proximity with humans and opening up the possibility of novel human–drone interactions. The design implements multiple closed-impeller rotors as propulsion units to accelerate airflow along the the surface of the spherical blimp and produce thrust by utilising the Coandă effect. A cube configuration with eight uni-directional propulsion units is presented, together with the closed-loop Proportional–Integral–Derivative (PID) controllers, and custom control mixing algorithm for position and attitude control in all three axes. A physical prototype of the propulsion unit and blimp sUAS was constructed to experimentally validate the dynamic behavior and controls in a motion-captured environment, with the experimental results compared to the side-tetra configuration with four bi-directional propulsion units as presented in our previously published conference paper. An up to 40% reduction in trajectory control error was observed in the new cube configuration, which is also capable of motion control in all six Degrees of Freedom (DoF) with additional pitch and roll control when compared to the side-tetra configuration. Full article

Many public safety agencies in the US have initiated a UAS-based procedure to document and map crash scenes. In addition to significantly reducing the time taken to document evidence as well as ensuring first responder safety, UAS-based mapping reduces incident clearance time and thus the likelihood of a secondary crash occurrence. There is a wide range of cameras used on these missions, but they are predominantly captured by mid-priced drones that cost in the range of $2000 to $4000. Indiana has developed a centralized processing center at Purdue University that has processed 252 crash scenes, mapped using 29 unique cameras, from 35 public agencies over the past three years. This paper includes a detailed case study that compares measurements obtained from a traditional ground-based real-time kinematic positioning base station and UAS-based photogrammetric mapping. The case study showed that UAS derived scale errors were within 0.1 ft (3 cm) of field measurements, a generally accepted threshold for public safety use cases. Further assessment was done on the 252 scenes using ground control scale error as the evaluation metric. To date, over 85% of the measurement errors were found to be within 0.1 ft (3 cm). When substantial errors are identified by the Purdue processing center, they are flagged for further dialog with the agency. In most of the cases with larger errors, the ground control distance was incorrectly measured, which is easily correctable by returning to the scene and performing new distance control measurements. Full article

This paper presents a robust control strategy for controlling the flight of an unmanned aerial vehicle (UAV) with a passively (fixed) tilted hexarotor. The proposed controller is based on a robust extended-state observer to estimate and reject internal dynamics and external disturbances at runtime. Both the stability and convergence of the observer are proved using Lyapunov-based perturbation theory and an ultimate bound approach. Such a controller is implemented within a highly realistic simulation environment that includes physics motors, showing an almost identical behavior to that of a real UAV. The controller was tested for flying under normal conditions and in the presence of different types of disturbances, showing successful results. Furthermore, the proposed control system was compared with another robust control approach, and it presented a better performance regarding the attenuation of the error signals. Full article

Soil salinization is one of the most important causes of land degradation and desertification, often threatening land management and sustainable agricultural development. Due to the low resolution of satellites, fine mapping of soil salinity cannot be completed, while high-resolution images from UAVs can only achieve accurate mapping of soil salinity in a small area. Therefore, how to realize fine mapping of salinity on a large scale based on UAV and satellite data is an urgent problem to be solved. Therefore, in this paper, the most relevant spectral variables for soil salinity were firstly determined using Pearson correlation analysis, and then the optimal inversion model was established based on the screened variables. Secondly, the feasibility of correcting satellite data based on UAV data was determined using Pearson correlation analysis and spectral variation trends, and the correction of satellite data was completed using least squares-based polynomial curve fitting for both UAV data and satellite data. Finally, the reflectance received from the vegetated area did not directly reflect the surface reflectance condition, so we used the support vector machine classification method to divide the study area into two categories: bare land and vegetated area, and built a model based on the classification results to realize the advantages of complementing the accurate spectral information of UAV and large-scale satellite spectral data in the study areas. By comparing the modeling inversion results using only satellite data with the inversion results based on optimized satellite data, our method framework could effectively improve the accuracy of soil salinity inversion in large satellite areas by 6–19%. Our method can meet the needs of large-scale accurate mapping, and can provide the necessary means and reference for soil condition monitoring. Full article

Modern plantation forest procedures still rely heavily on manual data acquisition in the inventory process, limiting the quantity and quality of the collected data. This limitation in collection performance is often due to the difficulty of traversing the plantation forest environment on foot. This work presents an autonomous system for exploring plantation forest environments using multi-rotor UAVs. The proposed method consists of three parts: waypoint selection, trajectory generation, and trajectory following. Waypoint selection is accomplished by estimating the rows’ locations within the environment and selecting points between adjacent rows. Trajectory generation is completed using a non-linear optimization-based constant speed planner and the following is accomplished using a model predictive control approach. The proposed method is tested extensively in simulation against various procedurally generated forest environments, with results suggesting that it is robust against variations within the scene. Finally, flight testing is performed in a local plantation forest, demonstrating the successful application of our proposed method within a complex, uncontrolled environment. Full article

Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may improve data acquisition, but different image sensors may affect target detectability and model performance. We compared the performance of automated detection models based on infrared (IR) or color (RGB) images and tested whether IR images, or training data that included annotations of non-target features, improved model performance. For this assessment, we collected paired IR and RGB images of nesting penguins (Pygoscelis spp.) and aggregations of Antarctic fur seals (Arctocephalus gazella) with a small UAS at Cape Shirreff, Livingston Island (60.79 °W, 62.46 °S). We trained seven independent classification models using the Video and Image Analytics for Marine Environments (VIAME) software and created an open-access R tool, vvipr, to standardize the assessment of VIAME-based model performance. We found that the IR images and the addition of non-target annotations had no clear benefits for model performance given the available data. Nonetheless, the generally high performance of the penguin models provided encouraging results for further improving automated image analysis from UAS surveys. Full article

Cotton constitutes 81% of the world’s natural fibers. Accurate and rapid cotton yield estimation is important for cotton trade and agricultural policy development. Therefore, we developed a remote sensing index that can intuitively represent cotton boll characteristics and support cotton yield estimation by extracting cotton boll pixels. In our study, the Density of open Cotton boll Pixels (DCPs) was extracted by designing different cotton boll indices combined with the threshold segmentation method. The relationship between DCP and field survey datasets, the Density of Total Cotton bolls (DTC), and yield were compared and analyzed. Five common yield estimation models, Linear Regression (LR), Support Vector Regression (SVR), Classification and Regression Trees (CART), Random Forest (RF), and K-Nearest Neighbors (KNN), were implemented and evaluated. The results showed that DCP had a strong correlation with yield, with a Pearson correlation coefficient of 0.84. The RF method exhibited the best yield estimation performance, with average R² and rRMSE values of 0.77 and 7.5%, respectively (five-fold cross-validation). This study showed that RedGreenBlue (RGB) and Near Infrared Red (NIR) normalized, a normalized form index consisting of the RGB and NIR bands, performed best. Full article

Unmanned Aerial Vehicles (UAVs) are a popular platform for air quality measurements. For vertical measurements, rotary-wing UAVs are particularly well-suited. However, an important concern with rotary-wing UAVs is how the rotor-downwash affects measurement accuracy. Measurements from a recent field campaign showed notable discrepancies between data from ascent and descent, which suggested the UAV downwash may be the cause. To investigate and explain these observed discrepancies, we use high-fidelity computational fluid dynamics (CFD) simulations to simulate a UAV during vertical flight. We use a tracer to model a gaseous pollutant and evaluate the impact of the rotor-downwash on the concentration around the UAV. Our results indicate that, when measuring in a gradient, UAV-based measurements were ∼50% greater than the expected concentration during descent, but they were accurate during ascent, regardless of the location of the sensor. These results provide an explanation for errors encountered during vertical measurements and provide insight for accurate data collection methods in future studies. Full article

In recent years, unmanned aerial vehicles (UAV) have been increasingly used in power line inspections. Birds often nest on transmission line towers, which threatens safe power line operation. The existing research on bird’s nest inspection using UAVs mainly stays at the level of image postprocessing detection, which has poor real-time performance and cannot obtain timely bird’s nest detection results. Considering the above shortcomings, we designed a power inspection UAV system based on deep learning technology for autonomous flight, positioning and photography, real-time bird nest detection, and result export. In this research, 2000 bird’s nest images in the actual power inspection environment were shot and collected to create the dataset. The parameter optimization and test comparison for bird’s nest detection are based on the three target detection models of YOLOv3, YOLOv5-s, and YOLOX-s. A YOLOv5-s bird’s nest detection model optimized for bird’s nest real-time detection is proposed, and it is deployed to the onboard computer for real-time detection and verification during flight. The DJI M300 RTK UAV was used to conduct a test flight in a natural power inspection environment. The test results show that the mAP of the UAV system designed in this paper for bird’s nest detection is 92.1%, and the real-time detection frame rate is 33.9 FPS. Compared with the previous research results, this paper proposes a new practice of using drones for bird’s nest detection, dramatically improving the real-time accuracy of bird’s nest detection. The UAV system can efficiently complete the task of bird’s nest detection in the process of electric power inspection, which can significantly reduce manpower consumption in the power inspection process. Full article

Due to the differences between simulations and the real world, the application of reinforcement learning (RL) in drone control encounters problems such as oscillations and instability. This study proposes a control strategy for quadrotor drones using a reference model (RM) based on deep RL. Unlike the conventional studies associated with optimal and adaptive control, this method uses a deep neural network to design a flight controller for quadrotor drones, which can map the drone’s states and target values to control commands directly. The method was developed based on a deep deterministic policy gradient (DDPG) algorithm combined with the deep neural network. The RM was further employed for the actor–critic structure to enhance the robustness and dynamic stability. The RM–DDPG-based flight-control strategy was confirmed to be practicable through a two-fold experiment. First, a quadrotor drone model was constructed based on an actual drone, and the offline policy was trained on it. The performance of the policy was evaluated via simulations while confirming the transition of system states and the output of the controller. The proposed strategy can eliminate oscillations and steady error and can achieve robust results for the target value and external interference. Full article

The technologies of Unmanned Aerial Vehicles (UAVs) have seen extremely rapid development in recent years. UAV technologies are being developed much faster than the means of their legislation. There have been many means of UAV detection and neutralization proposed in recent research; nonetheless, all of them have serious disadvantages. The essential problems in the detection of UAVs is the small size of UAVs, weak radio wave reflection, weak radio signal, and sound emitting. The main problem of conventional UAV countermeasures is the short detection and neutralization range. The authors propose the concept of the airborne counter-UAV platform (consisting of several vehicles) with radar. We use a low-cost marine radar with a high resolution 2 m wide antenna, embedded into the wing. Radar scanning is implemented by changing the heading of the aircraft. For the countermeasures, the authors suggest using a small rotorcraft UAV carried by a bigger fixed-wing one. A mathematical model that allows the calculation of the coordinates of the detected drone while scanning the environment in a moving UAV with radar was created. Furthermore, the results of integrated radar performance with a detected drone and the results of successful neutralization experiments of different UAVs were achieved. Full article

Supply chain solutions are based on first-mile and last-mile deliveries; their efficiency significantly influences the total cost of operation. Drone technologies make it possible to improve first-mile and last-mile operations, but the design and optimization of these solutions offers new challenges. Within the frame of this article, the author focuses on the impact of integrated first-mile/last-mile drone-based delivery services from trucks, analyzing the impact of solutions on energy efficiency, the environmental impact and sustainability. The author describes a novel model of drone-based integrated first-mile/last-mile services which makes it possible to analyze the impact of different typical solutions on sustainability. As the numerical examples and computational results show, the integrated first-mile-last-mile drone-based service from trucks could lead to a significant reduction in energy consumption and a reduction in virtual greenhouse gas (GHG) emissions, which would lead to a more sustainable logistics system. The numerical analysis of the scenarios shows that the increased application of drones and the integration of first-mile and last-mile delivery operations could decrease energy consumption by about 87%. This reduction in energy consumption, depending on the generation source of electricity, significantly increases the reduction in greenhouse gas emission. Full article

This research focuses on the development of two bioinspired micro air vehicle (MAV) prototypes, based on morphing wings and wing grid wingtip devices. The morphing wings MAV tries to adapt the aerodynamics of the vehicle to each phase of flight by modifying the vehicle geometry, while the wing grid MAV aims to minimize the aerodynamic and weight penalty of these vehicles. This work focuses on the design methodology of the flight control system of these MAVs. A preliminary theoretical conceptual design was used to verify the requirements, wind tunnel tests were performed to determine aerodynamic characteristics, and suitable materials were selected. The hardware and software configuration designed for the control system, which fulfills the objective of adaptive and optimal control in the wingtip-based prototype of the wing grid, is described. Finally, the results of the flight control on the prototype MAVs are analyzed. Full article

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches. Full article

Since it is difficult to accurately track reference trajectories under the condition of stride constraints for an unmanned hexapod robot moving with rhythmic gait, an omnidirectional tracking strategy based on model predictive control and real-time replanning is proposed in this paper. Firstly, according to the characteristic that the stride dominates the rhythmic motion of an unmanned multi-legged robot, a body-level omnidirectional tracking model is established. Secondly, a quantification method of limb’s stretch and yaw constraints described by motion stride relying on a tripod gait is proposed, and then, a body-level accurate tracking controller based on constrained predictive control is designed. Then, in view of the low tracking efficiency of the robot under the guidance of common reference stride, a solution strategy of variable stride period and a real-time replanning scheme of reference stride are proposed based on the limb constraints and the integral mean, which effectively avoid the tracking deviation caused by the guidance of constant reference strides. Finally, the effectiveness and practicability of the proposed control strategy are demonstrated through the comparative analysis and simulation test of a hexapod robot WelCH with omnidirectional movement ability to continuously track the directed curve and the undirected polyline trajectory. Full article

The data analysis subsystem of an Unmanned Aerial Vehicle (UAV) includes two main modules: a data acquisition module for data processing and a normalization module. One of the main features of an adaptive UAV protection system is the analysis of its cyber-physical parameters. An attack on a general-purpose computer system mainly affects the integrity, confidentiality and availability of important information. By contrast, an attack on a Cyber-Physical System (CPS), such as a UAV, affects the functionality of the system and may disrupt its operation, ultimately preventing it from fulfilling its tasks correctly. Cyber-physical parameters are the internal parameters of a system node, including the states of its computing resources, data storage, actuators and sensor system. Here, we develop a data normalization technique that additionally allows us to identify the signs of a cyber-attack. In addition, we define sets of parameters that can highlight an attack and define a new database format to support intrusion detection for UAVs. To achieve these goals, we performed an experimental study of the impact of attacks on UAV parameters and developed a software module for collecting data from UAVs, as well as a technique for normalizing and presenting data for detecting attacks on UAVs. Data analysis and the evaluation of the quality of a parameter (whether the parameter changes normally, or abrupt anomalous changes are observed) are facilitated by converting different types of data to the same format. The resulting formalized CPS model allows us to identify the nature of an attack and its potential impact on UAV subsystems. In the future, such a model could be the basis of a CPS digital twin in terms of security. The presented normalization technique supports processing raw data, as well as classifying data sets for their use in machine learning (ML) analyses in the future. The data normalization technique can also help to immediately determine the presence and signs of an attack, which allows classifying raw data automatically by dividing it into different categories. Such a technique could form the basis of an intrusion detection system for CPSs. Thus, the obtained results can be used to classify attacks, including attack detection systems based on machine learning methods, and the data normalization technique can be used as an independent method for detecting attacks. Full article

Unmanned aerial vehicles (UAVs) have drawn significant attention from researchers over the last decade due to their wide range of possible uses. Carrying massive payloads concurrent with light UAVs has broadened the aeronautics context, which is feasible using powerful engines; however, it faces several practical control dilemmas. This paper introduces a medium-scale hexacopter, called the Fan Hopper, alimenting Electric Ducted Fan (EDF) engines to investigate the optimum control possibilities for a fully autonomous mission carrying a heavy payload, even of liquid materials, considering calculations of higher orders. Conducting proper aerodynamic simulations, the model is designed, developed, and tested through robotic Gazebo simulation software to ensure proper functionality. Correspondingly, an Ardupilot open source autopilot is employed and enhanced by a model reference adaptive controller (MRAC) for the attitude loop to stabilize the system in case of an EDF failure and adapt the system coefficients when the fluid payload is released. Obtained results reveal less than a 5% error in comparison to desired values. This research reveals that tuned EDFs function dramatically for large payloads; meanwhile, thermal engines could be substituted to maintain much more flight endurance. Full article

Magnetic surveys have been widely used in archaeological field investigations. However, conventional survey methods are often restricted by complicated field conditions and ambiguities in data interpretation. In this study, a novel magnetic survey system was designed for pre-archaeological investigation (preliminary survey prior to the archaeological excavation) based on a modified quadrotor unmanned aerial vehicle (UAV) and was successfully applied to an archaeological area with a complex landform in Huizhou, China. Results show that the target anomaly identified by UAV aeromagnetic survey corresponds well to the location of a potential archaeological site. Subsequent soil analyses further confirm the archaeological value of UAV aeromagnetic results and provide strong constraints on the interpretation of target anomalies. This study demonstrates that the newly proposed UAV aeromagnetic system can adapt to the various field conditions with the advantages of flexibility and efficiency, which has great potential for future archaeological investigations. Full article

In recent years, the performance of free-and-open-source software (FOSS) for image processing has significantly increased. This trend, as well as technological advancements in the unmanned aerial vehicle (UAV) industry, have opened blue skies for both researchers and surveyors. In this study, we aimed to assess the quality of the sparse point cloud obtained with a consumer UAV and a FOSS. To achieve this goal, we also process the same image dataset with a commercial software package using its results as a term of comparison. Various analyses were conducted, such as the image residuals analysis, the statistical analysis of GCPs and CPs errors, the relative accuracy assessment, and the Cloud-to-Cloud distance comparison. A support survey was conducted to measure 16 markers identified on the object. In particular, 12 of these were used as ground control points to scale the 3D model, while the remaining 4 were used as check points to assess the quality of the scaling procedure by examining the residuals. Results indicate that the sparse clouds obtained are comparable. MicMac^® has mean image residuals equal to 0.770 pixels while for Metashape^® is 0.735 pixels. In addition, the 3D errors on control points are similar: the mean 3D error for MicMac^® is equal to 0.037 m with a standard deviation of 0.017 m, whereas for Metashape^®, it is 0.031 m with a standard deviation equal to 0.015 m. The present work represents a preliminary study: a comparison between software packages is something hard to achieve, given the secrecy of the commercial software and the theoretical differences between the approaches. This case study analyzes an object with extremely complex geometry; it is placed in an urban canyon where the GNSS support can not be exploited. In addition, the scenario changes continuously due to the vehicular traffic. Full article

To solve the problem of location services in harsh environments, we propose an integrated navigation algorithm based on broadband low-earth-orbit (LEO) satellite communication and navigation integration with 3-satellite alternate switch ranging. First, we describe the algorithm principle and processing flow in detail; next, we analyze and model the ranging error source and propose a combined multipath and non-line-of-sight (NLOS) error analysis model, which avoids discussing the complex multipath number of paths and its modeling process; in addition, we also propose a multimodal Gaussian noise-based interference model and analyze and model the LEO satellite orbital disturbance. The final simulation results show that our proposed algorithm can not only effectively overcome inertial navigation system (INS) divergence, but also achieve high positioning accuracy, especially when continuous ranging values are used. It can still ensure good anti-interference performance and robustness in terms of path and noise interference and by alternately switching ranging, there are other potential advantages. Compared to some of the existing representative advanced algorithms, it has higher accuracy, stronger stability and lower cost. Furthermore, it can be used as a location reference solution for real-time location services and life search and rescue in harsh environments with incomplete visual satellites and can also be used as a technical reference design solution for the future integration of communication and navigation (ICN). Full article

Understory vegetation cover is an important indicator of forest health, and it can also be used as a proxy in the exploration of soil erosion dynamics. Therefore, quantifying the understory vegetation cover in hilly areas in southern China is crucial for facilitating the development of strategies to address local soil erosion. Nevertheless, a multi-source data synergy has not been fully revealed in the remote sensing data quantifying understory vegetation in this region; this issue can be attributed to an insufficient match between the point cloud 3D data obtained from active and passive remote sensing systems and the UAV orthophotos, culminating in an abundance of understory vegetation information not being represented in two dimensions. In this study, we proposed a method that combines the UAV orthophoto and airborne LiDAR data to detect the understory vegetation. Firstly, to enhance the characterization of understory vegetation, the point CNN model was used to decompose the three-dimensional structure of the pinus massoniana forest. Secondly, the point cloud was projected onto the UAV image using the point cloud back-projection algorithm. Finally, understory vegetation cover was estimated using a synthetic dataset. Canopy closure was divided into two categories: low and high canopy cover. Slopes were divided into three categories: gentle slopes, inclined slopes, and steep slopes. To clearly elucidate the influence of canopy closure and slope on the remote sensing estimation of understory vegetation coverage, the accuracy for each category was compared. The results show that the overall accuracy of the point CNN model to separate the three-dimensional structure of the pinus massoniana forest was 74%, which met the accuracy requirement of enhancing the understory vegetation. This method was able to obtain the understory vegetation cover more accurately at a low canopy closure level (R_low² = 0.778, RMSE_low = 0.068) than at a high canopy closure level (R_High² = 0.682, RMSE_High = 0.172). The method could also obtain high accuracy in version results with R² values of 0.875, 0.807, and 0.704, as well as RMSE of 0.065, 0.106, and 0.149 for gentle slopes, inclined slopes, and steep slopes, respectively. The methods proposed in this study could provide technical support for UAV remote sensing surveys of understory vegetation in the southern hilly areas of China. Full article

Grasshoppers are integral parts of rangeland ecosystems but also have the potential to reach population densities high enough (outbreaks) to cause serious economic damage from forage loss and affect adjacent crops. The objective of this study was to investigate the efficacy of treating grasshopper population hotspots with a liquid insecticide using a remotely piloted aerial application system (RPAAS), as opposed to fixed-wing aircraft, which is the most common method currently in use. A liquid insecticide, Sevin XLR PLUS (containing carbaryl), was applied on replicated 4.05-hectare (10-acre) plots with an RPAAS on a ranch in New Mexico. Our results demonstrated that Sevin XLR PLUS significantly suppressed grasshopper populations over a 14-day period (normalized population reduction was 79.11 ± 8.35% SEM) and quite rapidly (mostly by day 3) compared to untreated controls. These results are comparable to those achieved with fixed-wing aircraft. The RPAAS covered the whole test area in a single flight in approximately 5 min, making these population hotspot treatment applications relatively rapid, potentially more cost-effective, and more targeted in comparison to fixed-wing aircraft. Before adoption as an application method option, further research is recommended on using an RPAAS to cover larger areas in combination with using diflubenzuron-based insecticides, which are often preferred. Full article

Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings. Full article

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds. Full article

A trans-medium aircraft is a new concept aircraft that can both dive in the water and fly in the air. In this paper, a new type of water–air multi-medium span vehicle is designed based on the water entry and exit structure model of a multi-rotor UAV. Based on the designed structural model of the cross-media aircraft, the OpenFOAM open source numerical platform is used to analyze the single-medium aerodynamic characteristics and the multi-medium spanning flow analysis. The rotating flow characteristics of single-medium air rotor and underwater propeller are calculated by sliding mesh. In order to prevent the numerical divergence caused by the deformation of the grid movement, the overset grid method and the multiphase flow technology are used for the numerical simulation of the water entry and exit of the cross-medium aircraft. Through the above analysis, the flow field characteristics of the trans-medium vehicle in different media are verified, and the changes in the body load and attitude at different water entry angles are also obtained during the process of medium crossing. Full article

As an important and basic platform for remote life sensing, unmanned aerial vehicles (UAVs) may hide the vital signals of an injured human due to their own motion. In this work, a novel method to remove the platform motion and accurately extract human respiration is proposed. We utilized a hovering UAV as the platform of ultra-wideband (UWB) radar to capture human respiration. To remove interference from the moving UAV platform, we used the delay calculated by the correlation between each frame of UWB radar data in order to compensate for the range migration. Then, the echo signals from the human target were extracted as the observed multiple range channel signals. Owing to meeting the independent component analysis (ICA), we adopted ICA to estimate the signal of respiration. The results of respiration detection experiments conducted in two different outdoor scenarios show that our proposed method could accurately separate respiration of a ground human target without any additional sensor and prior knowledge; this physiological information will be essential for search and rescue (SAR) missions. Full article

Unmanned air vehicle communication (UAV) systems have recently emerged as a quick, low-cost, and adaptable solution to numerous challenges in the next-generation wireless network. In particular, UAV systems have shown to be very useful in wireless communication applications with sudden traffic demands, network recovery, aerial relays, and edge computing. Meanwhile, non-orthogonal multiple access (NOMA) has been able to maximize the number of served users with the highest traffic capacity for future aerial systems in the literature. However, the study of joint optimization of UAV altitude, user pairing, and power allocation for the problem of capacity maximization requires further investigation. Thus, a capacity optimization problem for the NOMA aerial system is evaluated in this paper, considering the combination of convex and heuristic optimization techniques. The proposed algorithm is evaluated by using multiple heuristic techniques and deployment scenarios. The results prove the efficiency of the proposed NOMA scheme in comparison to the benchmark technique of orthogonal multiple access (OMA). Moreover, a comparative analysis of heuristic techniques for capacity optimization is also presented. Full article