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Non-line-of-sight (NLOS) communication with severe loss always leads to performance degradation in unmanned aerial vehicle (UAV) payload communication. In this paper, a UAV NLOS communication scheme based on single-carrier frequency domain equalization with cyclic prefix and cyclic suffix (CP/CS-SC-FDE) is designed. First, the reasons behind the generation of later intersymbol interference (LISI) in UAV NLOS communication are investigated. Then, the frame structure of conventional single-carrier frequency domain equalization with cyclic prefix (CP-SC-FDE) is improved, and the UAV NLOS communication frame structure based on cyclic prefix (CP) and cyclic suffix (CS) is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this UAV communication scheme can eliminate intersymbol interference (ISI) in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel and other standard NLOS channels. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization.

Climate change is becoming a worldwide emergency. In order to prevent catastrophic levels of climate change, three broad categories of action are ongoing: cutting emissions, adapting to climate impacts, and financing required adjustments. Cutting emissions requires stopping the use of fossil fuels in favor of renewable energy sources. Adapting to climate change and financing required adjustments need instruments for the understanding of the source causes and how effective the potential measures are. In this context, the use of Unmanned Aerial Vehicles for environmental monitoring is continuously increasing thanks to their ability to collect a wide range of environmental data, from the quality of air to the health status of vegetation, waters, and lands. This paper describes the research activities that are being performed for the design and development of a 100 kg Max Take Off Mass prototype zero-emission Unmanned Aerial Vehicle, named Daphne, destined for environmental monitoring, surveillance, and inspection missions. The developed prototype will drive the next industrialization of the vehicle. A particular focus is given to the design of the power system, based on the use of Proton Exchange Membrane fuel cells fueled with green hydrogen, the integration of the sensors allowing for multipurpose observations and measurements, and the design and validation of the relative multi-purpose missions via an innovative approach based on Model-Based System Engineering.

With the development of unmanned aerial vehicle (UAV) technology, deep learning is becoming more and more widely used in object detection in UAV aerial images; however, detecting and identifying small objects in low-illumination scenes is still a major challenge. Aiming at the problem of low brightness, high noise, and obscure details of low-illumination images, an object detection algorithm, LI-YOLO (Low-Illumination You Only Look Once), for UAV aerial images in low-illumination scenes is proposed. Specifically, in the feature extraction section, this paper proposes a feature enhancement block (FEB) to realize global receptive field and context information learning through lightweight operations and embeds it into the C2f module at the end of the backbone network to alleviate the problems of high noise and detail blur caused by low illumination with very few parameter costs. In the feature fusion part, aiming to improve the detection performance for small objects in UAV aerial images, a shallow feature fusion network and a small object detection head are added. In addition, the adaptive spatial feature fusion structure (ASFF) is also introduced, which adaptively fuses information from different levels of feature maps by optimizing the feature fusion strategy so that the network can more accurately identify and locate objects of various scales. The experimental results show that the mAP50 of LI-YOLO reaches 76.6% on the DroneVehicle dataset and 90.8% on the LLVIP dataset. Compared with other current algorithms, LI-YOLO improves the mAP 50 by 3.1% on the DroneVehicle dataset and 6.9% on the LLVIP dataset. Experimental results show that the proposed algorithm can effectively improve object detection performance in low-illumination scenes.

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones.

Unmanned aerial vehicles (UAVs) have garnered more attention across various industries in recent years, leading to significant development in their design and application globally. Due to the high coupling between UAV states, model uncertainties, and various disturbances, precise longitudinal control of UAVs remains a significant research challenge. Oriented for the speed and altitude control of an electrical-powered fixed-wing UAV, this paper introduces a new control strategy based on the fuzzy guaranteed cost control (F-GCC) technique, which results in a nonlinear longitudinal state feedback control law with strict stability criterion, effectively addressing the issue of state coupling. Moreover, the strategy also includes a thrust estimation model of the electrical propulsion system to significantly reduce the nonlinearity, simplifying the controller design while effectively preserving tracking control performance. Through numerical validation, the UAV longitudinal nonlinear controller designed using the F-GCC technique offers better transient response and stronger robustness than the traditional linear and ADRC controllers.

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

In recent years, UAV-enabled wireless energy transfer (WET) has attracted significant attention for its ability to provide ground devices with efficient and stable power by flexibly navigating three-dimensional (3D) space and utilizing favorable line-of-sight (LoS) channels. At the same time, energy beamforming utilizing multiple antennas, in which energy beams are focused toward devices in desirable directions, has been highlighted as a key technology for substantially enhancing radio frequency (RF)-based WET efficiency. Despite its significant utility, energy beamforming has not been studied in the context of UAV-enabled WET system design. In this paper, we propose the joint design of UAV altitude and channel statistics based energy beamforming to minimize the overall charging time required for all energy-harvesting devices (EHDs) to meet their energy demands while reducing the additional resources and costs associated with channel estimation. Unlike previous works, in which only the LoS dominant channel without small-scale fading was considered, we adopt a more general air-to-ground (A2G) Rician fading channel, where the LoS probability as well as the Rician factor is dependent on the UAV altitude. To tackle this highly nonconvex and nonlinear design problem, we first examine the scenario of a single EHD, drawing insights by deriving an optimal energy beamforming solution in closed form. We then devise efficient methods for jointly designing altitude and energy beamforming in scenarios with multiple EHDs. Our numerical results demonstrate that the proposed joint design considerably reduces the overall charging time while significantly lowering the computational complexity compared to conventional methods.

As a novel mode of urban air mobility (UAM), unmanned aerial vehicles (UAVs) pose a great amount of risk to ground people. Assessing ground risk and mitigation effects correctly is a focused issue. This paper proposes a grid-based risk matrix framework for assessing the ground risk associated with two types of UAVs, namely fixed-wing and quadrotor. The framework has a three-stage structure of “intrinsic risk assessment—mitigation effect—final map generation”. First, the intrinsic risk to ground populations caused by potential UAV crashes is quantified. Second, the mitigation effects are measured by establishing a mathematical model with a focus on the ground sheltering and parachute systems. Finally, a modular approach is presented for generating a ground risk map of UAVs, aiming to effectively characterize the effects of each influencing factor on the failure process of UAVs. The framework facilitates the modular analysis and quantification of the impact of diverse risk factors on UAV ground risk. It also provides a new perspective for analyzing ground risk mitigation measures, such as ground sheltering and UAV parachute systems. A case study experiment on a realistic urban environment in Shenzhen shows that the risk map generated by the presented framework can accurately characterize the distribution of ground risk posed by various UAVs.

The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

On the basis of a self-developed albatross imitation unmanned aerial vehicle (UAV), three different dihedral angle configurations for the wing’s mid and outer sections are explored: fixed at −50°, fixed at −5°, and varying arbitrarily between −50° and −5°. By solving the optimal loitering dynamic soaring trajectory optimization problem for each configuration, the effect of dihedral angle variation on the dynamic soaring performance of the bio-inspired wings is investigated. The results indicate that under all three configurations, the UAV achieves energy-neutral flight in specific wind field environments. Compared to the fixed dihedral angle of −5°, the UAV demonstrated superior dynamic soaring performance when the dihedral angle was fixed at −50°. When the dihedral angle varied dynamically, the UAV outperformed both fixed configurations across all relevant parameters. Specifically, compared to the fixed dihedral angle of −5°, the total energy increased by 25.43%, and the minimum required wind gradient decreased by 15.56%. Similarly, compared to the fixed dihedral angle of −50°, the total energy increased by 2.52%, and the minimum required wind gradient decreased by 2.07%. These findings suggest that the use of variable dihedral angle technology in bio-inspired UAV wings can significantly enhance dynamic soaring performance and provide theoretical support for the design of morphing wings with superior dynamic soaring capabilities.

A distributed network of multiple unmanned aerial vehicles (UAVs) equipped with airborne passive bistatic radar (APBR) can form a passive detection network through cooperative networking technology, a novel passive early warning detection system. Its multi-target tracking performance has a significant impact on situational awareness of the detection area. This paper proposes a passive multi-sensors joint optimization control algorithm based on task adaptive switching, with the aim of addressing the impact of limited UAV sensors’ field of view (FOV) on multi-target tracking performance in APBR networks. Firstly, for a single UAV node, the Poisson Labeled Multi-Bernoulli (PLMB) filter is selected as the local filter of each node, with the objective of obtaining the local multi-target density independently. Subsequently, the consensus arithmetic average fusion rule is employed to address the multi-sensors density fusion problem in APBR networks. This enables the acquisition of the global multi-target density and multi-target tracks of the network. The task adaptive switching mechanism of the nodes is constructed further based on the partially observable Markov decision process (POMDP), and the objective functions for the UAV to perform the search task and the tracking task are derived based on differential entropy, respectively. Ultimately, a multi-node joint optimization control algorithm is devised. The simulation experiment demonstrates that the proposed algorithm is capable of effective control of multiple nodes to solve the multi-target search and tracking problem when the node FOV is limited. This further improves the multi-target tracking and fusion capability of the distributed APBR network.

Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.

High-resolution remote sensing of turbidity in the coastal environment with unmanned aerial vehicles (UAVs) can be adversely affected by the presence of obstructions of vessels and marine objects in images, which can introduce significant errors in turbidity modeling and predictions. This study evaluates the use of two deep-learning-based inpainting methods, namely, Decoupled Spatial–Temporal Transformer (DSTT) and Deep Image Prior (DIP), to recover the obstructed information. Aerial images of turbidity plumes in the coastal environment were first acquired using a UAV system with a multispectral sensor that included obstructions on the water surface at various obstruction percentages. The performance of the two inpainting models was then assessed through both qualitative and quantitative analyses of the inpainted data, focusing on the accuracy of turbidity retrieval. The results show that the DIP model performs well across a wide range of obstruction percentages from 10 to 70%. In comparison, the DSTT model produces good accuracy only with low percentages of less than 20% and performs poorly when the obstruction percentage increases.

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

The emergence of pathological manifestations on facades persists globally, with recurring failures occurring often due to repeated construction details or design decisions. This study selected a building with a recurring architectural design and evaluated the stain pattern on its facade using a UAV with an infrared thermal camera. The results showed that advanced technology offers a non-invasive and efficient approach for comprehensive inspections, enabling early detection and targeted interventions to preserve architectural assets without requiring ancillary infrastructure or risking workers at height. The precise identification of damage clarified the real causes of the observed pathological manifestations. Capturing the images allowed accurate inspection, revealing hollow and damp spots not visible to the human eye. Novel results highlight patterns in the appearance of dirt on facades, related to water flow that could have been redirected through proper geometric element execution. The presented inspection methodology, staining standards, and construction details can be easily applied to any building, regardless of location. Sills, drip pans, and flashings must have drip cuts, adequate inclination, and projections to prevent building degradation.

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks.

The aim of this study was to investigate the effect of structural changes in variable fertilizer application devices on the distribution of particle deposition in UAVs. With the rapid development of drone technology, particularly in particulate spreading, drones have demonstrated significant potential due to their efficiency and precision. This paper evaluates the impact of different variable adjustment modes of the device on particulate deposition distribution through drone spreading experiments and particulate deposition data analysis. In this study, device structure change is the main variable factor, and flight altitude, flight speed and ambient wind speed are single quantitative factors. Experiments were conducted by varying the structure of the device to test the detailed deposition distribution of the device under group a, b, and c structures. Experimental results indicate that by choosing different variable combinations, the spreading device can achieve various fertilizer deposition states to meet regional needs. Among all 27 variable groups, the fertilizer particle deposition data for group b1b2b3 is relatively uniform, with three-quarters of particulate deposition values being 3 g/m2 and the maximum value being 4 g/m2. However, even with a relatively uniform distribution of fertilizer particles, the coefficient of variation for group b1b2b3 remains high (36.5%), with a range of 4.5% to 41%. Under different group adjustments, the particle distribution shows the smallest variability range in group b1b2b3, with a range of 15.71–26.44% and a variability difference of 10.73%. The particle distribution shows the largest variability range in group a1a2b3, with a range of 0.78–35.06% and a variability difference of 34.28%. These research conclusions provide important guidance for the study and practice of drone spreading systems.

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

Rangeland grasshoppers are an endemic species that play an essential role in the rangeland ecosystem but can cause severe economic damage when populations reach outbreak levels. Remotely piloted aerial application systems (RPAASs) offer an alternative method to carry out aerial insecticide applications in relatively small areas. The objective of this study was to investigate the efficacy of a granular bait, 2% Sevin (with the active ingredient carbaryl), applied by an RPAAS. The bait was applied on four replicated 4.05-hectare (10-acre) plots at a rate of 2.27 kg/ha (5 lbs/acre) with an RPAAS on a private ranch in New Mexico. Applications resulted in a normalized population reduction of 70.32% ± 16.54% standard error of the mean (SEM) of bait-susceptible species. Although some of the observed reduction in population may be attributed to aging, the net effect was most likely due to the ingestion of bait based on field observations of rapid mortality after ingestion and other factors, like past experience with carbaryl bait treatments on grasshoppers. Plots required at least two flights due to the Federal Aviation Administration’s (FAA) maximum takeoff weight requirement for small RPAASs. Combined, these results indicate that RPAASs can provide treatment capabilities in relatively small areas, i.e., population hotspots, preferably before outbreak levels are reached.

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios.

While there is immense potential in using unmanned aerial vehicles (UAVs) to facilitate precision water management, there is currently no consensus on practical strategies to operationally implement these technologies to guide water resources management decisions, particularly within smallholder farming contexts. To address this gap, this study employs bibliometric techniques to assess the current state of UAV applications for evapotranspiration (ET) estimation in agricultural settings. The analysis of 49 peer-reviewed papers from Scopus was conducted using Biblioshiny and VOSviewer to enhance comprehension of this expanding research field. The study highlights a significant increase in scholarly research on utilising UAVs for precision water management over the past decade. The investigations indicate that UAVs in agriculture are gaining prominence and exhibit substantial potential for various precision agriculture (PA) applications. Significant cost reductions for UAV technology and remote sensing (RS) are anticipated soon, primarily driven by the availability of open-source platforms for processing tasks, such as Google Earth Engine. This research aims to inform smallholder farmers about the benefits of integrating UAVs into their farming practices, enhancing operational efficiency and productivity. Policymakers can use these findings to develop regulatory frameworks and incentive schemes that facilitate UAV adoption among smallholder farmers. Additionally, technology developers can leverage insights from this study to identify areas needing innovation and optimisation tailored to small-scale agriculture. Hence, this study seeks to bridge the gap between technological advancements and practical agricultural applications, promoting sustainable farming practices and enhancing the socioeconomic welfare of smallholder farmers.

To address the larger numbers of small objects and the issues of occlusion and clustering in UAV aerial photography, which can lead to false positives and missed detections, we propose an improved small object detection algorithm for UAV aerial scenarios called YOLOv8 with tiny prediction head and Space-to-Depth Convolution (HSP-YOLOv8). Firstly, a tiny prediction head specifically for small targets is added to provide higher-resolution feature mapping, enabling better predictions. Secondly, we designed the Space-to-Depth Convolution (SPD-Conv) module to mitigate the loss of small target feature information and enhance the robustness of feature information. Lastly, soft non-maximum suppression (Soft-NMS) is used in the post-processing stage to improve accuracy by significantly reducing false positives in the detection results. In experiments on the Visdrone2019 dataset, the improved algorithm increased the detection precision mAP0.5 and mAP0.5:0.95 values by 11% and 9.8%, respectively, compared to the baseline model YOLOv8s.

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Compared to images captured from ground-level perspectives, objects in UAV images are often more challenging to track due to factors such as long-distance shooting, occlusion, and motion blur. Traditional multi-object trackers are not well-suited for UAV multi-object tracking tasks. To address these challenges, we propose an online multi-object tracking network, OMCTrack. To better handle object occlusion and re-identification, we designed an occlusion perception module that re-identifies lost objects and manages occlusion without increasing computational complexity. By employing a simple yet effective hierarchical association method, this module enhances tracking accuracy and robustness under occlusion conditions. Additionally, we developed an adaptive motion compensation module that leverages prior information to dynamically detect image distortion, enabling the system to handle the UAV’s complex movements. The results from the experiments on the VisDrone2019 and UAVDT datasets demonstrate that OMCTrack significantly outperforms existing UAV video tracking methods.

This work focuses on the use of load sensors to help with spraying tasks using unmanned aerial vehicles (UAVs). The study details the construction of a prototype for load measurement to validate the proof of concept. To simulate the application of agricultural pesticides, the UAV follows a predefined route and an image processing system detects the presence of diseased plants. After detection, the UAV pauses its route momentarily and activates the spraying device. The payload sensor monitors the fertilizer application process, which determines whether the amount of pesticide has been fully applied. If the storage tank is empty or the remaining quantity is insufficient for another operation, the system will command the UAV to return to the base station for refueling. Experimental validations were carried out in an indoor controlled environment to verify the proposal and the functionality of the in-flight payload monitoring system. Additionally, the UAV’s flight controller demonstrated robust performance, maintaining stability despite the challenges posed by liquid-load oscillations and varying payloads during the spraying process. In summary, our main contribution is a real-time payload monitoring system that monitors weight during flight to avoid over- or under-spraying. In addition, this system supports automatic refueling, detecting low levels of pesticides and directing the UAV to return to base when necessary.

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level.

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

In the present study, a performance analysis of three different VTOL configurations is presented within an urban air mobility context. A classical lightweight helicopter was employed as a reference configuration to design a dual-rotor side-by-side helicopter and a hexacopter drone layout. An analytical model based on general momentum and blade element theories was developed for single- and multiple-rotor configurations in horizontal and vertical flight conditions. Suitable battery pack and electric motor designs were produced to evaluate the endurance and range of the different configurations for a specific mission. This paper provides fundamental insights into the endurance and range capabilities of multiple-rotor unmanned aerial vehicles (UAVs) and a qualitative discussion on the safety and acceptability features of each configuration implemented in an advanced air mobility context. As a result, the side-by-side helicopter configuration was identified as the best solution to be introduced within urban environments, fulfilling all the performance and mission requirements.

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.

The evaluation of propulsion systems used in UAVs is of paramount importance to enhance the flight endurance, increase the flight control performance, and minimize the power consumption. This evaluation, however, is typically performed experimentally after the preliminary hardware design of the UAV is completed, which tends to be expensive and time-consuming. In this paper, a comprehensive theoretical UAV propulsion system assessment is proposed to assess both static and dynamic performance characteristics via an integrated simulation model. The approach encompasses the electromechanical dynamics of both the motor and its controller. The proposed analytical model estimates the propeller and motor combination performance with the overarching goal of enhancing the overall efficiency of the aircraft propulsion system before expensive costs are incurred. The model embraces an advanced blade element momentum theory underpinned by the development of a novel mechanism to predict the propeller performance under low Reynolds number conditions. The propeller model utilizes XFOIL and various factors, including post-stall effects, 3D correction, Reynolds number fluctuations, and tip loss corrections to predict the corresponding aerodynamic loads. Computational fluid dynamics are used to corroborate the dynamic formulations followed by extensive experimental tests to validate the proposed estimation methodology.

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.

The integration of technological advancements has significantly impacted artificial intelligence (AI), enhancing the reliability of AI model outputs. This progress has led to the widespread utilization of AI across various sectors, including automotive, robotics, healthcare, space exploration, and defense. Today, air defense operations predominantly rely on laser designation. This process is entirely dependent on the capability and experience of human operators. Considering that UAV systems can have flight durations exceeding 24 h, this process is highly prone to errors due to the human factor. Therefore, the aim of this study is to automate the laser designation process using advanced deep learning algorithms on 3D point clouds obtained from different sources, thereby eliminating operator-related errors. As different data sources, dense 3D point clouds produced with photogrammetric methods containing color information, and point clouds produced with LiDAR systems were identified. The photogrammetric point cloud data were generated from images captured by the Akinci UAV’s multi-axis gimbal camera system within the scope of this study. For the point cloud data obtained from the LiDAR system, the DublinCity LiDAR dataset was used for testing purposes. The segmentation of point cloud data utilized the PointNet++ and RandLA-Net algorithms. Distinct differences were observed between the evaluated algorithms. The RandLA-Net algorithm, relying solely on geometric features, achieved an approximate accuracy of 94%, while integrating color features significantly improved its performance, raising its accuracy to nearly 97%. Similarly, the PointNet++ algorithm, relying solely on geometric features, achieved an accuracy of approximately 94%. Notably, the model developed as a unique contribution in this study involved enriching the PointNet++ algorithm by incorporating color attributes, leading to significant improvements with an approximate accuracy of 96%. The obtained results demonstrate a notable improvement in the PointNet++ algorithm with the proposed approach. Furthermore, it was demonstrated that the methodology proposed in this study can be effectively applied directly to data generated from different sources in aerial scanning systems.

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions.

The accuracy of relative pose estimation is an important foundation for ensuring the safety and stability of autonomous aerial refueling (AAR) of unmanned aerial vehicles (UAV), and in response to this problem, a relative pose estimation method of UAVs based on the spatial equivalent plane is proposed in this paper. The UAV is equivalent to a spatial polygonal plane, and according to the measurement information of the Global Navigation Satellite System (GNSS) receivers, the equivalent polygonal plane equation is solved through the three-point normal vector and the minimum sum of squares of the distance from the four points to the plane. The equations of the distance between the geometric centers of the two polygonal planes, the angle between planes, and the angle between lines are used to calculate the relative pose information of the UAVs. Finally, the simulation environment and initial parameters are utilized for numerical simulation and results analysis. The simulation results show that without considering the motion model of the UAV, the proposed method can accurately estimate the relative pose information of the UAVs. In addition, in the presence of measurement errors, the relative pose estimation method based on the equivalent triangle plane can identify the position of the measurement point with the error, and the relative pose estimation method based on the equivalent quadrilateral plane has good robustness. The simulation results verify the feasibility and effectiveness of the proposed method.

Unmanned Aerial Vehicles (UAVs) have garnered significant attention in recent years due to their unique features. Utilizing UAVs for bridge inspection offers a promising solution to overcome challenges associated with traditional methods. While UAVs present considerable advantages, there are challenges associated with their use in bridge inspection, particularly in ensuring effective data collection. The primary objective of this study is to tackle the challenges related to data collection in bridge inspection using UAVs. A comprehensive method for pre-flight preparation in data collection is proposed. A well-structured flowchart has been created, covering crucial steps, including identifying the inspection purpose, selecting appropriate hardware, planning and optimizing flight paths, and calibrating sensors. The method has been tested in two case studies of bridge inspections in the State of New Mexico. The results show that the proposed method represents a significant advancement in utilizing UAVs for bridge inspection. These results indicate improvements in accuracy from 7.19% to 21.57% in crack detection using the proposed data collection method. By tackling the data collection challenges, the proposed method serves as a foundation for the application of UAVs for bridge inspection.

With the rapid development of the unmanned aerial vehicles (UAVs) industry, there is increasing demand for UAV surveillance technology. Automatic Dependent Surveillance-Broadcast (ADS-B) provides accurate monitoring of UAVs. However, the system cannot encrypt messages or verify identity. To address the issue of identity spoofing, radio frequency fingerprinting identification (RFFI) is applied for ADS-B transmitters to determine the true identities of UAVs through physical layer security technology. This paper develops an ensemble learning ADS-B radio signal recognition framework. Firstly, the research analyzes the data content characteristics of the ADS-B signal and conducts segment processing to eliminate the possible effects of the signal content. To extract features from different signal segments, a method merging end-to-end and non-end-to-end data processing is approached in a convolutional neural network. Subsequently, these features are fused through EL to enhance the robustness and generalizability of the identification system. Finally, the proposed framework’s effectiveness is evaluated using collected ADS-B data. The experimental results indicate that the recognition accuracy of the proposed ELWAM-CNN method can reach up to 97.43% and have better performance at different signal-to-noise ratios compared to existing methods using machine learning.

Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhances dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FasterBlock-CGLU-C2f (FC-C2f), which reduces the model’s parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model’s classification ability and detection accuracy. The experimental results on the SeaDroneSee dataset indicate that DFLM-YOLO improves the mean average precision (mAP) by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for Unmanned Aerial Vehicles (UAVs) to conduct object detection missions in open water efficiently.

In this study, we propose a scalable deep learning approach to automated phenotyping using UAV multispectral imagery, exemplified by yellow rust detection in winter wheat. We adopt a high-granularity scoring method (1 to 9 scale) to align with international standards and plant breeders’ needs. Using a lower spatial resolution (60 m flight height at 2.5 cm GSD), we reduce the data volume by a factor of 3.4, making large-scale phenotyping faster and more cost-effective while obtaining results comparable to those of the state-of-the-art. Our model incorporates explainability components to optimise spectral bands and flight schedules, achieving top-three accuracies of 0.87 for validation and 0.67 and 0.70 on two separate test sets. We demonstrate that a minimal set of bands (EVI, Red, and GNDVI) can achieve results comparable to more complex setups, highlighting the potential for cost-effective solutions. Additionally, we show that high performance can be maintained with fewer time steps, reducing operational complexity. Our interpretable model components improve performance through regularisation and provide actionable insights for agronomists and plant breeders. This scalable and explainable approach offers an efficient solution for yellow rust phenotyping and can be adapted for other phenotypes and species, with future work focusing on optimising the balance between spatial, spectral, and temporal resolutions.

This paper addresses the challenge of olive tree segmentation using drone imagery, which is crucial for precision agriculture applications. We tackle the data scarcity issue by augmenting existing detection datasets. Additionally, lightweight model variations of state-of-the-art models like YOLOv8n, RepViT-SAM, and EdgeSAM are combined into two proposed pipelines to meet computational constraints while maintaining segmentation accuracy. Our multifaceted approach successfully achieves an equilibrium among model size, inference time, and accuracy, thereby facilitating efficient olive tree segmentation in precision agriculture scenarios with constrained datasets. Following comprehensive evaluations, YOLOv8n appears to surpass the other models in terms of inference time and accuracy, albeit necessitating a more intricate fine-tuning procedure. Conversely, SAM-based pipelines provide a significantly more streamlined fine-tuning process, compatible with existing detection datasets for olive trees. However, this convenience incurs the disadvantages of a more elaborate inference architecture that relies on dual models, consequently yielding lower performance metrics and prolonged inference durations.

This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.

Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.

For atypical traditional villages, their invaluable historical traces and cultural memories are preserved in the existing village landscapes. Rapid and accurate acquisition of the spatial information of various surface elements in a village is an important prerequisite for a scientific, reasonable, feasible planning and design scheme for conserving, progressing, and developing atypical villages. Taking Qianfeng Village as an example, this research proposes the atypical traditional village landscape classification model based on unmanned aerial vehicle (UAV) imagery (ATVLUI) by virtue of the UAV RGB images and the object-oriented fuzzy logic membership classification technique that extracts objects according to their spectrums, textures, geometries, and context relationships, aiming at precise extraction of atypical traditional village landscapes. Based on the landscape information, the landscape pattern indexes are calculated to explore the spatial distribution characteristics of different landscapes and analyze the current conditions of Qianfeng Village as the epitome of atypical traditional villages. Accordingly, suggestions for protecting, planning, and developing atypical villages are proposed. The results show that: (1) the ATVLUI boasts excellent identification for village landscapes in a complex scenario, with a classification accuracy for traditional structures of 84%, an overall accuracy of 93%, and a Kappa coefficient of 0.89. This model is proven superior to K-nearest neighbors (KNN), decision tree (DT), and random tree (RT); (2) according to the area and proportion calculations, the structures account for 33.94% of Qianfeng Village’s total area, in which 29.69% and 4.25% are modern and traditional structures, respectively. The number of traditional structures is 202, accounting for 13% of the total number of structures; (3) within the village, connectivity between and extension of the modern structures can be recognized, suggesting a trajectory where the traditional structures are being gradually substituted by modern ones. The ecological environment at the periphery of the village is favorable. The building-to-building common boundaries are long. The modern structures are densely distributed. The discretely distributed traditional structures gather as small clusters. In general, different structures are highly interlaced to form a fragmented distribution pattern.

In this paper, a LabVIEW-based online monitoring and safety evaluation system for UAVs is designed to address the deficiencies in UAV flight state parameter monitoring and safety evaluation. The system consists of a lower unit for UAV recording and an upper unit on the ground. The lower unit collects and detects flight data and connects to the upper unit through a wireless digital transmission module via a serial port. The upper unit receives the data and carries out the monitoring and safety situation evaluation of the UAV. The lower unit of the system adopts multi-sensors to collect UAV navigation information in real time to achieve flight detection, while the upper unit adopts LabVIEW to design the UAV online monitoring and safety situation prediction system, enabling monitoring and safety situation prediction during UAV navigation. The test results show that the system can detect and comprehensively display the navigation information of the UAV in real time, and realize the safety evaluation and warning function of the UAV.