Drones, Volume 7, Issue 4 (April 2023) – 61 articles

Vespa velutina nigrithorax, a species of hornet, is spreading globally, with increasingly negative effects on human health. To effectively eliminate V. velutina, its nest should be destroyed and its queen removed; however, the nests are difficult to reach. Thus, we analyzed the requirements for a drone-assisted hornet exterminator using field observations and physical tests on a sample hornets’ nest, and based on these, a UAV exterminator equipped with a nest-perforating device (based on an airsoft rifle) and pesticide-spraying system was designed and manufactured. Pesticides and bullets were manufactured using ecofriendly materials. An actuator at the rear of the device adjusted the pitch of the perforator and sprayer, and a monitoring system was installed to aid the operator in targeting. The operating parameters of the UAV exterminator were evaluated in laboratory tests, with a spray distance of 5 m deemed suitable. To evaluate the system’s pest-control performance, several V. velutina nests were targeted in field tests. An insecticidal effect of over 99% was achieved using two pyrethrum-based pesticides (15% pyrethrum extract and 10% pyrethrum extract with additives). In addition, compared to conventional nest-removal methods, the UAV exterminator reduced the work time by 85% and the cost by 54.9%. Full article

In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar. Full article

Barriers are utilized for various tasks in security, environmental monitoring, penetration detection and reconnaissance. It is highly necessary to consider how to support pedestrian tracking and target detection in theme park areas having multiple obstacles. In this paper, we create security barriers through cooperation between mobile robots and UAVs for use in theme park areas where multiple obstacles of undetermined forms are placed. We formally define the problem and the goals. The goals are the following: to maximize the number of convex hull obstacle-aware tracking barriers using mobile robots and UAVs, to satisfy given detection accuracy, and to ensure that all environments are protected by convex hull obstacle-aware tracking barriers without disturbance from irregular obstacles. To address the problem, we propose two different algorithms, to improve security barriers and avoid various forms of obstacles, in a bid to work towards a 6G-enabled virtual emotion environment. Then, the proposed schemes are executed through simulations with various settings, and the numerical results evaluated with detailed discussions and demonstrations. Full article

This article presents a novel autonomous navigation approach that is capable of increasing map exploration and accuracy while minimizing the distance traveled for autonomous drone landings. For terrain mapping, a probabilistic sparse elevation map is proposed to represent measurement accuracy and enable the increasing of map quality by continuously applying new measurements with Bayes inference. For exploration, the Quality-Aware Best View (QABV) planner is proposed for autonomous navigation with a dual focus: map exploration and quality. Generated paths allow for visiting viewpoints that provide new measurements for exploring the proposed map and increasing its quality. To reduce the distance traveled, we handle the path-cost information in the framework of control theory to dynamically adjust the path cost of visiting a viewpoint. The proposed methods handle the QABV planner as a system to be controlled and regulate the information contribution of the generated paths. As a result, the path cost is increased to reduce the distance traveled or decreased to escape from a low-information area and avoid getting stuck. The usefulness of the proposed mapping and exploration approach is evaluated in detailed simulation studies including a real-world scenario for a packet delivery drone. Full article

The detection and mapping of winter wheat and the canopy cover of associated weeds, such as chickweed and hairy buttercup, are essential for crop and weed management. With emerging drone technologies, the use of a multispectral camera with the red-edge band, such as Altum, is commonly used for crop and weed mapping. However, little is understood about the contribution of the red-edge band in mapping. The aim of this study was to examine the addition of the red-edge band from a drone with an Altum multispectral camera in improving the detection and mapping of the canopy cover of winter wheat, chickweed, and hairy buttercup. The canopy cover of winter wheat, chickweed, and hairy buttercup were classified and mapped with the red-edge band inclusively and exclusively using a random forest classification algorithm. Results showed that the addition of the red-edge band increased the overall mapping accuracy of about 7%. Furthermore, the red-edge wavelength was found to better detect winter wheat relative to chickweed and hairy buttercup. This study demonstrated the usefulness of the red-edge band in improving the detection and mapping of winter wheat and associated weeds (chickweed and hairy buttercup) in agricultural fields. Full article

Energy balance closure is an important feature in studies of ecosystem exchanges of energy and greenhouse gases using the eddy covariance method. Previous analyses show that this is still a problem with imbalances in the order of 0.6–0.7 to full closure (for only a few sites). It has been suggested that mesoscale transport processes that are not captured by the eddy covariance measurements are the main reason behind the closure problem. So far, very little action has been taken to investigate another potential cause of the problem, namely, the role of spatial variation in net radiation at the scale of typical flux footprints. The reason for this knowledge gap is mainly due to the lack of suitable methods to perform such investigations. Here, we show that such measurements can be performed with an unmanned aerial vehicle equipped with radiation sensors. A comparison using a reference radiometer on a fixed mast with a hovering UAV equipped with pyranometers for incoming and outgoing shortwave radiation and an infrared thermometer for surface temperature measurements shows that incoming and outgoing shortwave radiation can be measured with a standard error of 7.4 Wm⁻² and 1.8 Wm⁻², respectively. An application of the system was made over a five-year-old forest flux site in Sweden. Here, the net longwave radiation was estimated from the measured surface temperature and the calculated incoming longwave radiation. The results show that during the mission around noon on a clear day, distinct ‘hotspots’ existed over the plantation with the albedo varying between 15.5 and 17.9%, the surface temperature varying between 22.2 and 25.5 °C and the net radiation varying between 330 and 380 Wm⁻². These variations are large enough to have a significant impact on the energy balance closure problem. Our conclusion is that we now have the tools to investigate the spatial variability of the radiation regime over flux sites and that this should be given more attention in the future. Full article

In applications of the $L_1$ adaptive flight control system, we found two limitations to be extended: (1) the system cannot meet the demands of engineering in terms of nonlinearity and adaptation in most flight scenarios; (2) the adaptive control law generates a transient response in the tracking error, hindering the system from reaching the steady-state error, and ultimately decreasing control accuracy. In response to these problems, an extended flight control system for $L_1$ adaptive theory is proposed and rigorously proved. This system involves considering the nonlinear function matrix of state variables, which serves as an extension of the regression matrix in the original $L_1$ adaptive control system, thus enhancing its nonlinear characteristics. The problem of calculating the adaptive laws, caused by the extended regression matrix, is solved by using the pseudo-inverse matrix. To eliminate the transient response, the state vector and its estimate are recorded and employed just like an integrator. Finally, the proposed system is verified on a high-subsonic flight subject to nonlinear uncertainties, with simulation results showing improved control accuracy and enhanced robustness. The proposed system resolves the limitations of the $L_1$ adaptive control system in nonlinearity, providing the possibility for further theoretical development to improve the performance of adaptive control systems. Full article

The accurate and efficient detection of power lines and towers in aerial drone images with complex backgrounds is crucial for the safety of power grid operations and low-altitude drone flights. In this paper, we propose a new method that enhances the deep learning segmentation model UNet algorithm called TLSUNet. We enhance the UNet algorithm by using a lightweight backbone structure to extract the features and then reconstructing them with contextual information features. In this network model, to reduce its parameters and computational complexity, we adopt DFC-GhostNet (Dubbed Full Connected) as the backbone feature extraction network, which is composed of the DFC-GhostBottleneck structure and uses asymmetric convolution to capture long-distance targets in transmission lines, thus enhancing the model’s extraction capability. Additionally, we design a hybrid feature extraction module based on convolution and a transformer to refine deep semantic features and improve the model’s ability to locate towers and transmission lines in complex environments. Finally, we adopt the up-sampling operator CARAFE (Content-Aware Re-Assembly of FEature) to improve segmentation accuracy by enhancing target restoration using contextual neighborhood pixel information correlation under feature decoding. Our experiments on public aerial photography datasets demonstrate that the improved model requires only 8.3% of the original model’s computational effort and has only 21.4% of the original model’s parameters, while achieving a reduction in inference speed delay by 0.012 s. The segmentation metrics also showed significant improvements, with the mIOU improving from 79.75% to 86.46% and the mDice improving from 87.83% to 92.40%. These results confirm the effectiveness of our proposed method. Full article

In the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establishes a dynamic model of an iced high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) with disturbance and measurement noise. Then, by combining multilayer perceptrons (MLP) with a nonlinear dynamic inversion (NDI) controller, we propose an MLP-NDI controller to compensate for online inversion errors and provide a brief proof of control stability. Two experiments were conducted: on one hand, we compared the MLP-NDI controller with other typical controllers; on the other hand, we evaluated its robustness and adaptiveness under different icing conditions. Results indicate that the MLP-NDI controller outperforms other typical controllers with higher tracking accuracy and exhibits strong robustness in the presence of icing errors and measurement noise, which has huge potential to ensure flight safety. Full article

An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay. Full article

Dynamic soaring helps albatross achieve long-distance migration. From a bionic view, dynamic soaring has great potential to enhance unmanned aerial vehicles (“UAVs”) flight range and endurance. The previous application studies focus on flight strategies to guide UAV soaring. However, the energy harvesting efficiency problem emerges. The lack of clear dynamic soaring influencing factors hinders dynamic soaring UAV design and flight strategy design from a theoretical perspective. Hence this paper aims to analyze the influence law of different UAV mass, initial airspeed, and entering angle. Trajectories and flight data in different factors are obtained through trajectory optimization. The results show that UAV mass has a positive influence on energy harvesting. The initial airspeed and entering angle affect both energy efficiency and trajectory. For UAV design, weight balance needs to be considered rather than a pursuit of the lightest. For flight strategy design, finding an optimal initial state will improve energy efficiency. Full article

This paper describes the design and construction of an energy replenishment service station for a quadrotor. The prototype includes a small number of actuators, making it a low-cost solution. The system consists of three batteries: two charged and one discharged (within the quadrotor). Once the quadrotor lands, the battery with the highest charge is selected, which is then exchanged for the discharged battery. In order to perform this action, position control is used, in which the desired value depends on the location of the sensor that detects the highest voltage. In addition, the system has a mechanical design that facilitates the coupling of the unmanned aerial vehicle (UAV) with the structure for battery exchange, ensuring that the discharged battery is always in the same position. Furthermore, the design of a mechanism to release and hold the battery placed in the quadrotor is presented, which works by means of voltage and force sensors that identify the instant that the battery is discharged and when the UAV has landed on the exchange platform, thus initiating the exchange process. Likewise, the criteria for selecting the elements used, acquiring and processing signals, and routines for changing batteries are detailed. Full article

A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas. Full article

Post-disaster search and rescue is critical to disaster response and recovery efforts and is often conducted in hazardous and challenging environments. However, the existing post-disaster search and rescue operations have problems such as low efficiency, limited search range, difficulty in identifying the nature of the target, and wrong target location. Therefore, this study develops an air–ground integrated intelligent cognition visual enhancement system based on a UAV (VisionICE). The technique combines a portable AR display device, a camera-equipped helmet, and a quadcopter UAV for efficient patrols over a wide area. First, the system utilizes wireless image sensors on the UAV and helmet to capture images from the air and ground views. Using the YOLOv7 algorithm, the cloud server calculates and analyzes these visual data to accurately identify and detect targets. Lastly, the AR display device obtains real-time intelligent cognitive results. The system allows personnel to simultaneously acquire air and ground dual views and achieve brilliant cognitive results and immersive visual experiences in real time. The findings indicate that the system demonstrates significant recognition accuracy and mobility. In contrast to conventional post-disaster search and rescue operations, the system can autonomously identify and track targets of interest, addressing the difficulty of a person needing help to conduct field inspections in particular environments. At the same time, the system can issue potential threat or anomaly alerts to searchers, significantly enhancing their situational awareness capabilities. Full article

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster. Full article

The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system. Full article

When surveying a large target area with a real-time kinematic unmanned aerial vehicle (RTK-UAV), the RTK signal tends to be disconnected when city canyons or macrocells are included. Thus, the accuracy is reduced due to the lack of RTK signal or the fact that RTK signal is not available in certain areas. The available methods to solve this problem are costly. Therefore, we used one GCP and performed post-process kinematics (PPK) to verify whether the accuracy reduction caused by the lack of RTK signal in certain areas could be solved. A data set detailing the percentage of time during which the RTK signal was received (100%, 90%, 5%, and 0%) was obtained, and ATs were conducted both with and without PPK using GCPs located at the four corners and center. In 40 experiments, the trend of root mean square error (RMSE) values based on the distance between the GCP used and the 41 check points (CPs) was analyzed. In the absence of PPK, the error tended to increase depending on the distance between the GCP and CPs, but there was no significant difference after PPK as up to 10 cm horizontal error and up to 20 cm vertical error were observed within a 1 km radius of the GCP. As a result, even if the RTK signal is disconnected during shooting, it is possible to achieve an accuracy within 3 GSD up to a radius of 1 km from the GCP. Full article

Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method of cluster cooperative passive localization is selected and the measurement model is constructed. Then, the problem of improving passive location accuracy is transformed into the problem of obtaining more target information. From the perspective of information theory, using the A criterion as the optimization target, the passive localization process for static targets is further deduced. The Recursive Neural Network (RNN) is used to predict the probability distribution of the target’s location in the next moment so as to improve the localization method and make it suitable for the localization of moving targets. The particle swarm algorithm is improved by using grouping and time period strategy, and the algorithm flow of moving target location is constructed. Finally, through the simulation verification and algorithm comparison, the advantages of the algorithm in this paper are presented. Full article

Uncrewed aerial vehicles (UAVs), also known as drones, are ubiquitous and their use cases extend today from governmental applications to civil applications such as the agricultural, medical, and transport sectors, etc. In accordance with the requirements in terms of demand, it is possible to carry out various missions involving several types of UAVs as well as various onboard sensors. According to the complexity of the mission, some configurations are required both in terms of hardware and software. This task becomes even more complex when the system is composed of autonomous UAVs that collaborate with each other without the assistance of an operator. Several factors must be considered, such as the complexity of the mission, the types of UAVs, the communication architecture, the routing protocol, the coordination of tasks, and many other factors related to the environment. Unfortunately, although there are many research works that address the use cases of multi-UAV systems, there is a gap in the literature regarding the difficulties involved with the implementation of these systems from scratch. This review article seeks to examine and understand the communication issues related to the implementation from scratch of autonomous multi-UAV systems for collaborative decisions. The manuscript will also provide a formal definition of the ecosystem of a multi-UAV system, as well as a comparative study of UAV types and related works that highlight the use cases of multi-UAV systems. In addition to the mathematical modeling of the collaborative target detection problem in distributed environments, this article establishes a comparative study of communication architectures and routing protocols in a UAV network. After reading this review paper, readers will benefit from the multicriteria decision-making roadmaps to choose the right architectures and routing protocols adapted for specific missions. The open challenges and future directions described in this manuscript can be used to understand the current limitations and how to overcome them to effectively exploit autonomous swarms in future trends. Full article

Normalized point clouds (NPCs) derived from unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have been applied to extract relevant forest inventory information. However, detecting treetops from topographically normalized LiDAR points is challenging if the trees are located in steep terrain areas. In this study, a novel point cloud normalization method based on the imitated terrain (NPCIT) method was proposed to reduce the effect of vegetation point cloud normalization on crown deformation in regions with high slope gradients, and the ability of the treetop detection displacement model to quantify treetop displacements and tree height changes was improved, although the model did not consider the crown shape or angle. A forest farm in the mountainous region of south-central China was used as the study area, and the sample data showed that the detected treetop displacement increased rapidly in steep areas. With this work, we made an important contribution to theoretical analyses using the treetop detection displacement model with UAV-LiDAR NPCs at three levels: the method, model, and example levels. Our findings contribute to the development of more accurate treetop position identification and tree height parameter extraction methods involving LiDAR data. Full article

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial. Full article

Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified. Full article

This paper focuses on reducing execution delays of dynamic computing tasks in UAV-assisted fault-prone mobile edge computing (FP-MEC) systems, which combine mobile edge computing (MEC) and network function virtualization (NFV) technologies. FP-MEC is suited to meet Industrial Internet (IIN) requirements such as data privacy, low latency, and low-cost industrial scalability in specific scenarios. However, the reliability of virtual network functions (VNFs) deployed on UAVs could impact system performance. Thus, this paper proposes the dynamic task scheduling optimization algorithm (DTSOA) based on deep reinforcement learning (DRL) for resource allocation design. The formulated execution delay optimization problem is described as an integer linear programming problem and it is an NP-hard problem. To overcome the intractable problem, this paper discretizes it into a series of single-time slot optimization problems. Furthermore, the experimental rigor is improved by constructing a real-time server state update system to calculate the real-time server load situation and crash probability. Theoretical analysis and experiments show that the DTSOA has better application prospects than Q-learning and the recent search method (RSM), and it is closer to the traversal search method (TSM). Full article

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science. Full article

Path planning is a crucial component of autonomous mobile robot (AMR) systems. The slime mould algorithm (SMA), as one of the most popular path-planning approaches, shows excellent performance in the AMR field. Despite its advantages, there is still room for SMA to improve due to the lack of a mechanism for jumping out of local optimization. This means that there is still room for improvement in the path planning of ARM based on this method. To find shorter and more stable paths, an improved SMA, called the Lévy flight-rotation SMA (LRSMA), is proposed. LRSMA utilizes variable neighborhood Lévy flight and an individual rotation perturbation and variation mechanism to enhance the local optimization ability and prevent falling into local optimization. Experiments in varying environments demonstrate that the proposed algorithm can generate the ideal collision-free path with the shortest length, higher accuracy, and robust stability. Full article

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers. Full article

This study proposed a novel nonlinear blended aerodynamic model for the tiltrotor unmanned aerial vehicle (UAV) during the transition phase to handle the high angle-of-attack (AoA) flight, which aggregated the flat-plate mode and the linear mode of the aerodynamic coefficients. Additionally, a harmonic disturbance observer (HDO) and super-twisting sliding mode controller (STSMC) addressed the fast-changing external disturbances and attenuated the chattering problem in the original SMC. The comparative trajectory tracking results indicated that the blended aerodynamic model accurately tracks the reference signals with no tracking errors, which demonstrated a superior performance as compared to the traditional aerodynamic model, with a reduction of 2.2%, 50%, 73.6%, and 11.2% in the time required for tracking the pitch angle, pitch rate, and velocities u and w, respectively. Conversely, the traditional one exhibited significant tracking errors, ranging from 0.016° in the pitch angle channel to 1.25°/s in the pitch rate channel, and 0.6 m/s for velocity u and 0.01 m/s for velocity w. Moreover, the comparative control input results illustrated that the least control effort was required for the proposed HDO-STSMC control scheme with a blending function, while the original ESO-SMC experienced more oscillations and sharp amplitude changes, taking twice the time to converge, with considerable tracking errors such as 1.067° in the pitch angle channel, 0.788°/s in the pitch rate channel, 1.554 m/s for velocity u, and 0.746 m/s for velocity w, which verified the feasibility and superiority of the proposed HDO-STSMC with the blending function. Two performance indices revealed the robust stability and rapid convergence of the proposed transition blended aerodynamic model with the HDO-STSMC control scheme. Full article

Maize is among the most important grain crops. Aboveground biomass (AGB) is a key agroecological indicator for crop yield prediction and growth status monitoring, etc. In this study, we propose two new methods, improved crop biomass algorithm (iCBA) and iCBA with piecewise function (iCBA-PF), to estimate maize AGB. Multispectral (MS) images, visible-band (RGB) images, and light detection and ranging (LiDAR) data were collected using unmanned aerial vehicles (UAVs). Vegetation indices (VIs) and the VI-weighted canopy volume model (CVM_VI) were calculated and used as input variables for AGB estimation. The two proposed methods and three benchmark methods were compared. Results demonstrated that: (1) The performance of MS and RGB data in AGB estimation was similar. (2) AGB was estimated with higher accuracy using CVM_VI than using VI, probably because the temporal trends of CVM_VI and AGB were similar in the maize growing season. (3) The best estimation method was the iCBA-PF (R² = 0.90 ± 0.02, RMSE = 190.01 ± 21.55 g/m²), indicating that AGB before and after maize heading should be estimated with different methods. Our method and findings are possibly applicable to other crops with a heading stage. Full article

Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the inner and outer loop mode, the outer loop adopts the Globally Stable Integral Sliding Mode (GSISM) control, and the inner loop adopts the S-Plane control. At the same time, the unknown disturbance in the model is estimated via an RBF neural network. Firstly, the outer loop controller is designed based on the GSISM, and its stability is proved using the Lyapunov theory. Then, the S-Plane controller is designed for the instruction signal of the inner loop. Considering the complexity of the derivation in the S-Plane controller, a second-order differentiator is introduced. Finally, considering the problem of external wind disturbance, the controller is modeled, studied, and processed in order to better reflect the impact of real external wind on UAV path following. Finally, the Globally Stable Sliding Mode (GSSM) control and Globally Stable Integral Sliding Mode S-Plane (GSISM S-Plane) control are used for a comparative experiment. The simulation results show that the designed GSISM+RBF S-Plane controller can accurately track the ideal path compared with the GSSM and GSISM S-Plane controller, and it has good control performance and anti-disturbance performance. Full article

This paper presents a novel integrated study of the aerodynamic performance and acoustic signature of multirotor propellers with a specific focus on the blade twist angle effect. Experimental measurements and computational fluid dynamic (CFD) simulations were utilized to examine and compare the aerodynamic performance and noise reduction between twisted and untwisted blades. A 2D phase-locked particle image velocimetry (PIV) was employed to visualize flow structures at specific blade locations in terms of tip vortices and trailing edge vortices. Good consistency between the simulations and measurements was observed in aerodynamic and acoustic performance. It is verified that the propellers with twisted blades enable a maximum increase of 9.3% in the figure of merit compared to untwisted blades while achieving the same thrust production and are further capable to reduce overall sound pressure level by a maximum of 4.3 dB. CFD results reveal that the twisted propeller remarkedly reduces far-field loading noise by suppressing trailing-edge vortices, hence mitigating kinetic energy fluctuation at the blade tip, while having minimal impact on thickness noise. This study points to the crucial role of blade twists in altering the aeroacoustic characteristics, indicating that optimal designs could lead to significant improvements in both aerodynamic and acoustic performance. Full article