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Airspeed measurement is crucial for UAV control. To achieve accurate airspeed measurements for UAVs, this paper calculates airspeed data by measuring changes in air pressure and temperature. Based on this, a data processing method based on mechanical filtering and the improved AR-SHAKF algorithm is proposed to indirectly measure airspeed with high precision. In particular, a mathematical model for an airspeed measurement system was established, and an installation method for the pressure sensor was designed to measure the total pressure, static pressure, and temperature. Secondly, the measurement principle of the sensor was analyzed, and a metal tube was installed to act as a mechanical filter, particularly in cases where the aircraft has a significant impact on the gas flow field. Furthermore, a time series model was used to establish the sensor state equation and the initial noise values. It also enhanced the Sage-Husa adaptive filter to analyze the unavoidable error impact of initial noise values. By constraining the range of measurement noise, it achieved adaptive noise estimation. To validate the superiority of the proposed method, a low-complexity airspeed measurement device based on MEMS pressure sensors was designed. The results demonstrate that the airspeed measurement device and the designed velocity measurement method can effectively calculate airspeed with high measurement accuracy and strong interference resistance.

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The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation.

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Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:•Collection of drone images and creation of orthomosaics.•Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.•Annotation of drone images into - potentially hierarchical - habitat classes and training of machine learning algorithms for habitat classification.As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.

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Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

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Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

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Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.

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Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time-frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.

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Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

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Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents. To address this challenge, a two-stage clustering method, incorporating X-means and K-means algorithms, is employed to identify optimal number and locations for Unmanned Aerial Vehicle (UAV) fire stations and drone ambulance centers. A Mixed-Integer Linear Programming (MILP) model is subsequently constructed and solved using the Gurobi optimization platform. Bayesian optimization-a machine learning technique-is exploited to elucidate the interplay between response speed and service capacity of these UAV-based emergency service stations under an optimized layout. Results affirm that integration of MILP and machine learning provides a robust framework for solving complex problems related to the siting and allocation of emergency service facilities. The proposed hybrid algorithm demonstrates substantial potential for enhancing emergency preparedness and response in urban settings.

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The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

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Coastal dunes result from complex interactions between sand transport, topography and vegetation. However, uncertainty still persists due to limited quantitative analyses, integrating plant distribution and morphologic changes. This study aims to assess the initiation and maintenance of feedback processes by analysing the early development stages of incipient foredunes, combining data on the evolution of the plant cover and communities and dune morphology. Over three years, the monitoring of a newly formed dune (1 ha plot) reveals the progressive plant colonisation and the episodic accumulation of sand around vegetated areas controlled by sediment availability. Distinct colonisation rates were observed, influenced by inherited marine conditions, namely topography and presence of beach wrack. Berm-ridges provided elevations above the critical threshold for plant colonisation and surface roughness, aiding sediment accumulation. Beach wrack above this threshold led to rapid expansion and higher plant concentration. In the initial stages, vegetation cover significantly influenced sediment accumulation patterns, with higher accumulation around areas with high plant cover and low slopes or around areas with sparse vegetation but milder slopes. As the dune system matured and complexity grew, the link between vegetation cover and accumulation became nonlinear. Mid to low coverages (5-30 %) retained most of the observed accumulation, especially when coupled with steep slopes, resulting from positive feedbacks between vegetation, topography and sand transport. As foredune developed, vegetation cover and diversity increased while inherited morphologies grew vertically, explaining the emergence of dune ridge morphological types. Flat surfaces lacking wrack materials experienced a three-year delay in colonisation and sand accumulation, leading to the formation of terrace-type incipient foredunes. These observations underline feedback processes during the early stages of dune formation, with physical feedbacks primarily driving initiation and biophysical feedbacks prevailing in subsequent colonisation stages.

Asunto(s)

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Unmanned aerial vehicles (UAVs) have garnered attention for their potential to improve wireless communication networks by establishing line-of-sight (LoS) connections. However, urban environments pose challenges such as tall buildings and trees, impacting communication pathways. Intelligent reflection surfaces (IRSs) offer a solution by creating virtual LoS routes through signal reflection, enhancing reliability and coverage. This paper presents a three-dimensional dynamic channel model for UAV-assisted communication systems with IRSs. Additionally, it proposes a novel channel-tracking approach using deep learning and artificial intelligence techniques, comprising preliminary estimation with a deep neural network and continuous monitoring with a Stacked Bidirectional Long and Short-Term Memory (Bi-LSTM) model. Simulation results demonstrate faster convergence and superior performance compared to benchmarks, highlighting the effectiveness of integrating IRSs into UAV-enabled communication for enhanced reliability and efficiency.

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The high-altitude real-time inspection of unmanned aerial vehicles (UAVs) has always been a very challenging task. Because high-altitude inspections are susceptible to interference from different weather conditions, interference from communication signals and a larger field of view result in a smaller object area to be identified. We adopted a method that combines a UAV system scheduling platform with artificial intelligence object detection to implement the UAV automatic inspection technology. We trained the YOLOv5s model on five different categories of vehicle data sets, in which mAP50 and mAP50-95 reached 93.2% and 71.7%, respectively. The YOLOv5s model size is only 13.76 MB, and the detection speed of a single inspection photo reaches 11.26 ms. It is a relatively lightweight model and is suitable for deployment on edge devices for real-time detection. In the original DeepStream framework, we set up the http communication protocol to start quickly to enable different users to call and use it at the same time. In addition, asynchronous sending of alarm frame interception function was added and the auxiliary services were set up to quickly resume video streaming after interruption. We deployed the trained YOLOv5s model on the improved DeepStream framework to implement automatic UAV inspection.

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Aiming at real-time detection of UAVs, small UAV targets are easily missed and difficult to detect in complex backgrounds. To maintain high detection performance while reducing memory and computational costs, this paper proposes the SEB-YOLOv8s detection method. Firstly, the YOLOv8 network structure is reconstructed using SPD-Conv to reduce the computational burden and accelerate the processing speed while retaining more shallow features of small targets. Secondly, we design the AttC2f module and replace the C2f module in the backbone of YOLOv8s with it, enhancing the model's ability to obtain accurate information and enriching the extracted relevant information. Finally, Bi-Level Routing Attention is introduced to optimize the Neck part of the network, reducing the model's attention to interfering information and filtering it out. The experimental results show that the mAP50 of the proposed method reaches 90.5% and the accuracy reaches 95.9%, which are improvements of 2.2% and 1.9%, respectively, compared with the original model. The mAP50-95 is improved by 2.7%, and the model's occupied memory size only increases by 2.5 MB, effectively achieving high-accuracy real-time detection with low memory consumption.

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Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.

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This manuscript presents the use of three novel technologies for the implementation of wireless green battery-less sensors that can be used in agriculture. The three technologies, namely, additive manufacturing, energy harvesting, and wireless power transfer from airborne transmitters carried from UAVs, are considered for smart agriculture applications, and their combined use is demonstrated in a case study experiment. Additive manufacturing is exploited for the implementation of both RFID-based sensors and passive sensors based on humidity-sensitive materials. A number of energy-harvesting systems at UHF and ISM frequencies are presented, which are in the position to power platforms of wireless sensors, including humidity and temperature IC sensors used as agriculture sensors. Finally, in order to provide wireless energy to the soil-based sensors with energy harvesting features, wireless power transfer (WPT) from UAV carried transmitters is utilized. The use of these technologies can facilitate the extensive use and exploitation of battery-less wireless sensors, which are environmentally friendly and, thus, "green". Additionally, it can potentially drive precision agriculture in the next era through the implementation of a vast network of wireless green sensors which can collect and communicate data to airborne readers so as to support, the Artificial Intelligence and Machine Learning-based decision-making with data.

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This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case.

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This paper focuses on the design of Convolution Neural Networks to visually guide an autonomous Unmanned Aerial Vehicle required to inspect power towers. The network is required to precisely segment images taken by a camera mounted on a UAV in order to allow a motion module to generate collision-free and inspection-relevant manoeuvres of the UAV along different types of towers. The images segmentation process is particularly challenging not only because of the different structures of the towers but also because of the enormous variability of the background, which can vary from the uniform blue of the sky to the multi-colour complexity of a rural, forest, or urban area. To be able to train networks that are robust enough to deal with the task variability, without incurring into a labour-intensive and costly annotation process of physical-world images, we have carried out a comparative study in which we evaluate the performances of networks trained either with synthetic images (i.e., the synthetic dataset), physical-world images (i.e., the physical-world dataset), or a combination of these two types of images (i.e., the hybrid dataset). The network used is an attention-based U-NET. The synthetic images are created using photogrammetry, to accurately model power towers, and simulated environments modelling a UAV during inspection of different power towers in different settings. Our findings reveal that the network trained on the hybrid dataset outperforms the networks trained with the synthetic and the physical-world image datasets. Most notably, the networks trained with the hybrid dataset demonstrates a superior performance on multiples evaluation metrics related to the image-segmentation task. This suggests that, the combination of synthetic and physical-world images represents the best trade-off to minimise the costs related to capturing and annotating physical-world images, and to maximise the task performances. Moreover, the results of our study demonstrate the potential of photogrammetry in creating effective training datasets to design networks to automate the precise movement of visually-guided UAVs.

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This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

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With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.