Unmanned Aerial Systems Research Articles

Lower atmospheric profiling for climate studies pertaining to aerosols, radiation and turbulence using Unmanned Aerial System in India: Initial results

Lower atmospheric profiling for climate studies pertaining to aerosols, radiation and turbulence using Unmanned Aerial System in India: Initial results

Reducing Tonal Noise of Contra-Rotating Unmanned Aerial System Rotors via Blade Rake Angle Adjustment

Abstract This paper investigates the effect of blade rake angle on the tonal noise produced by contra-rotating rotor systems suitable for use on small multi-rotor unmanned aerial systems (UAS). This investigation utilises semi-analytical, numerical, and experimental methods to investigate the physics of the noise generation mechanisms, generated noise levels and the psychoacoustic characteristics of this noise. Computational fluid dynamics (CFD) simulations and semi-analytical models are employed to predict the periodic unsteady loading on the rotor blades, and the loading data are used to predict the radiated noise for rotor systems with different blade rake angles. Experimental measurements of the noise produced by a rotor system with no blade rake angle were used as a baseline case, and predictions were used to synthesise and auralise the sound produced by rotor systems with different rake angles for psychoacoustic analysis. The results show that increasing the blade rake angle generally reduces the amplitude of prominent interaction tones due to the reduction in the unsteady loading along the blade span caused by bound potential field interactions as the blade rake angle increases. This causes a reduction in the perceived tonal noise level.

Determinação de Riscos de Sobrevoo por Visão Computacional

AbstractIn the 20th century, aviation has proven itself as one of the most importanttechno-social revolutions of modern human history. Now,Unmanned Aerial Systems (UAS) are on the verge of leading thenext techno-social revolution, remodelling several areas and movinga global market that does not stop growing. The establishmentof UAV Traffic Management (UTM) systems is necessary to makethis revolution happen, and one of the main obstacles is the risksinvolved in UAS flight, especially in urban environments. A methodologyto estimate risks on the overflown environment was carriedout by the usage of convolutional neural networks on the imageryof such environments, allowing the definition of safer routes andtheir management on the fly. Development and experimentationprocesses were carried out, with promising results including a convolutionalneural network for pixel-wise domain that was capableof estimate risks from satellite imagery and return overflight risksheat maps.

Study Regarding Methods and Techniques Used for Analysis of Spray Drift and Droplet Size Distribution by Agricultural Spraying Machines

The main purpose of this paper is to analyze the methods and techniques used for analysis of spray drift and droplet size distribution by agricultural spraying machines. According to related standards and accepted definitions spray drift is the volume of plant protection product (PPP) that is transported by air currents from the target surface to another unintended area, during the application of PPP by agricultural spraying machines or by unmanned aerial spraying systems (UASS). Spray drift of PPP leads to pollution of watercourses, sensitive areas or direct damage (phytotoxicity) to neighboring crops. This paper analyses in force international standards and available literature, reports and scientific papers, that relate with issues regarding spray drift measurements. Spray drift mitigation and management strategies are grouped in several key issues, that are related to the environment (e.g. meteorological variables), equipment design and PPP physical and chemical properties. For drift studies a variety of methods and techniques are used, each one with strengths and weaknesses, such as image analysis, image processing, real time measurements (Lidar and laser-based techniques).

Design and Construction of UAV-Based Measurement System for Water Hyperspectral Remote-Sensing Reflectance.

Acquiring a large number of in situ water spectral measurements is fundamental for constructing water color remote-sensing retrieval models and validating the accuracy of water color remote-sensing products. However, traditional manual site-based water spectral measurements are time-consuming and labor-intensive, resulting in an insufficient number of in situ water spectral samples to date. To resolve this issue, this study develops an unmanned aerial vehicle-based hyperspectral remote-sensing reflectance measurement system (UAV-RRS) capable of continuous on-the-move water spectral measurements. This paper provides a detailed introduction to the system components and conducts precise experiments on the correction and calibration of the spectral sensors. Using this system, an in situ-UAV-satellite multi-source remote-sensing reflectance comparison experiment was conducted in the middle reaches of the Qiantang River, East China, to evaluate the accuracy and reliability of UAV-RRS and extend the analysis to satellite data across different spatial scales. The results demonstrate that, in small-scale water bodies, UAV-RRS achieves higher spatial precision and spectral accuracy, offering a valuable solution for high-precision, low-altitude continuous water body observations.

Optimal two-channel switching false data injection attacks against remote state estimation of the unmanned aerial vehicle cyber-physical system

Optimal two-channel switching false data injection attacks against remote state estimation of the unmanned aerial vehicle cyber-physical system

Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management.

Efficient charging station deployment in unmanned aerial vehicle systems for enhanced mission efficiency

Efficient charging station deployment in unmanned aerial vehicle systems for enhanced mission efficiency

An Open-Source Machine Learning–Based Methodological Approach for Processing High-Resolution UAS LiDAR Data in Archaeological Contexts: A Case Study from Epirus, Greece

This study shows and discusses an innovative approach devised for archaeological feature detection using unmanned aerial system (UAS) LiDAR and an open-source probabilistic machine learning framework. The methodology employs a Random Forest classification algorithm within CloudCompare’s 3DMASC plugin to analyse dense LiDAR point clouds. The main steps include classifier training, hyperparameter adjustment and point cloud segmentation to produce digital terrain models (DTM), digital feature models (DFM) and digital surface models (DSM). Experimenting different parameters led to the determination of the best set to be employed for the training model. Subsequent data enhancement with the Relief Visualisation Toolbox (RVT) refines the visibility of archaeological features, particularly within complex and heavily vegetated terrain. The use case selected to validate this approach is the site of Kastrí-Pandosia in Epirus (Greece), which is particularly suitable for LiDAR analysis by UAS. This approach significantly improves archaeological detection and interpretation, revealing previously inaccessible or obscured microtopographic and structural features. The results highlight the site’s defensive walls, terracing and potential anthropogenic routes, underlining the methodology’s effectiveness in detecting archaeological landscapes at multiple levels. This study emphasises the utility of accessible and open-source solutions for the identification of archaeological features, promoting cost-effective methods to improve the documentation of sites in remote or difficult locations.

Time series model for predicting the disturbance of lychee canopy by wind field in unmanned aerial spraying system

Time series model for predicting the disturbance of lychee canopy by wind field in unmanned aerial spraying system

Unleashing profitability of vineyards through the adoption of unmanned aerial vehicles technology systems: the case of two Italian wineries

PurposePrecision agriculture technologies play an important role in optimising practices to increase yields and reduce costs, contributing to socio-economic progress and environmental well-being, and playing a key role in addressing climate change. Viticulture is a strategic, input-intensive agricultural sector where precision technologies can make the use of resources more efficient without compromising profitability. The aim of this study is to evaluate the profitability of implementing precision farming systems, such as unmanned aerial vehicle surveying for the production of vigour maps, compared to the conventional cultivation system in two Italian wineries.MethodsThe profitability of using precision farming tools in viticulture compared to conventional management techniques has been investigated in two Italian wineries over a four-year period, before and after the introduction of UAV technology.ResultsThe results demonstrate the usefulness and economic viability of precision agriculture technologies in viticulture. The vigour maps produced by the data collected with UAV technology allow both the identification of problems such as diseases, and consequently the planning of phytosanitary treatments, and selective grape harvesting, which allows a significant improvement in the quality of the harvested grapes.ConclusionThe results demonstrate the usefulness of precision technologies for cost-effective and sustainable vineyard management, satisfying a market segment made up of stakeholders who are increasingly sensitive to environmental issues.

Design of Unmanned Aerial Vehicle Frequency Hopping Communication System

The development of drone technology has led to the widespread application of its communication system in multiple fields. This article first introduces the composition and principles of unmanned aerial vehicle communication systems, with a focus on analyzing the principles and parameters of frequency hopping communication technology, which enhances anti-interference and security through pseudo-random frequency switching. Next, we will explore time-frequency analysis methods, including linear and nonlinear methods, and explain their applications and advantages in processing complex signals. Finally, we will combine the two to improve the accuracy and reliability of signal processing.

Cybersecurity Risk in Unmanned Aircraft Systems (UASs): Strategic Cybersecurity Threats of Unmanned Aerial Systems

Unmanned Aerial Systems (UASs) have emerged as critical components across various sectors, including military, commercial, and civilian applications. However, their increasing prevalence has raised significant cybersecurity concerns. This paper explores the strategic cybersecurity threats associated with UASs, focusing on vulnerabilities inherent in their architecture, communication protocols, and operational frameworks. identifying key risk areas such as data interception, command-and-control (C2) breaches, and adversarial attacks on autonomous decision-making systems by analyzing recent incidents and emerging threat vectors. Additionally, examine the implications of these threats on national security, privacy, and infrastructure integrity. The paper advocates for a multifaceted approach to UAS cybersecurity, emphasizing the need for robust regulatory frameworks, enhanced encryption methods, and continuous threat assessment strategies. By addressing these cybersecurity challenges, stakeholders can better safeguard the operational integrity of UASs, thereby improving their utility and reliability in an increasingly complex digital landscape.

Logistics Hub Surveillance: Optimizing YOLOv3 Training for AI-Powered Drone Systems

Background: Integrating artificial intelligence in unmanned aerial vehicle systems may enhance the surveillance process of outdoor expansive areas, which are typical in logistics facilities. In this work, we propose methods to optimize the training of such high-performing systems. Methods: Specifically, we propose a novel approach to tune the training hyperparameters of the YOLOv3 model to improve high-altitude object detection. Typically, the tuning process requires significant computational effort to train the model under numerous combinations of hyperparameters. To address this challenge, the proposed approach systematically searches the hyperparameter space while reducing computational requirements. The latter is achieved by estimating model performance from early terminating training sessions. Results: The results reveal the value of systematic hyperparameter tuning; indicatively, model performance varied more than 13% in terms of mean average precision (mAP), depending on the hyperparameter setting. Also, the early training termination method saved over 90% of training time. Conclusions: The proposed method for searching the hyperparameter space, coupled with early estimation of model performance, supports the development of highly efficient models for UAV-based surveillance of logistics facilities. The proposed approach also identifies the effects of hyperparameters and their interactions on model performance.

Fast Entry Trajectory Planning Method for Wide-Speed Range UASs

Convex optimization has gained increasing popularity in trajectory planning methods for wide-speed range unmanned aerial systems (UASs) with multiple no-fly zones (NFZs) in the entry phase. To address the issues of slow or even infeasible solutions, a modified fast trajectory planning method using the approaches of variable trust regions and adaptive generated initial values is proposed in this paper. A dimensionless energy-based dynamics model detailing the constraints of the entry phase is utilized to formulate the original entry trajectory planning problem. This problem is then transformed into a finite-dimensional convex programming problem, using techniques such as successive linearization and interval trapezoidal discretization. Finally, a variable trust region strategy and an adaptive initial value generation strategy are adopted to accelerate the solving process in complex flight environments. The experimental results imply that the strategy proposed in this paper can significantly reduce the solution time of trajectory planning for wide-speed range UASs in complex environments.

Evaluating warm‐season turfgrass: Multispectral and red–green–blue index comparison

AbstractAcross the United States and much of the globe, turfgrass is managed at a multitude of scales and requires new technology for efficient monitoring and management. The introduction of unmanned aerial systems (UAS) provides researchers with readily available red–green–blue (RGB) cameras with high‐resolution capabilities, which may add considerable value to researchers and producers when assessing turfgrass performance. A study was conducted at the Ft. Lauderdale Research and Extension Center (FLREC) in Davie, FL in the spring (February–April) of 2023 to evaluate the performance of RGB vegetation indices (VIs) on ‘Celebration’ bermudagrass [Cynodon. Dactylon (L.) Pers.] and ‘CitraBlue’ St. Augustinegrass [Stenotaphrum secundatum (Walt.)] in comparison to visual ratings and Normalized Difference Vegetation Index (NDVI). Images were obtained by an RGB camera coupled to a remotely piloted Parrot ANAFI Thermal drone capturing images with 75% overlap. Both the Green Leaf Index and Green Chromatic Coordinate were highly correlated to quality readings in both grasses, with correlation coefficients as high as 0.92 in bermudagrass (outcompeting NDVI) and 0.88 in St. Augustinegrass (NDVI was more correlated). Each grass species had a unique correlation with each index, showing how different species and possibly cultivars may be better monitored with species‐specific VIs.

Designing Wind-dispersed Microfliers for Early Wildfire Warning: Aerodynamic Insights Inspired by the Glider-shaped Alsomitra Macrocarpa Seeds

Wildfires not only pose a danger to humans and animals, they are also harmful to the climate and air quality by producing vast quantities of CO2. Early wildfire warning systems are crucial for minimizing environmental damage, economic losses, and threats to human life. Current unmanned aerial system for wildfire detection is limited by its low-capacity power source, which significantly reduce its flight stability and travel distance. This work designs wind-dispersed microflyer system for early wildfire warning, inspired by the glider-shaped Alsomitra Macrocarpa seeds. Famous for remarkable aerodynamic stability and exceptionally low terminal velocity, the seeds of this plant have large and papery wings and can glide long distances without reliance on gusts or updrafts. The aerodynamic analysis and wind tunnel experiment were conducted utilizing the shapes of these seeds. The analysis revealed that the Alsomitra Macrocarpa seed has an excellent aerodynamic shape with weak flow separation, which is a key reason for its superior lift-to-drag ratio. The designed microflyer with a sensor will be deployed from the air and monitor wildfire-related parameters like temperature effectively and reliably, demonstrating its potential for long-distance wind-assisted fire warning system.

Evaluation of Light Electric Flying-Wing Unmanned Aerial System Energy Consumption During Holding Maneuver

This study evaluates the energy consumption of a light electric flying-wing unmanned aerial system (UAS) during low-altitude holding maneuvers. Two flight patterns were investigated: circular holding at a specified altitude and a figure-eight trajectory. Test flights were conducted under varying meteorological and wind conditions, including scenarios where wind aligned and crossed the flight path. Key flight parameters such as pitch, yaw, heading deviation, flight altitude, ground speed, and airspeed were monitored. Concurrently, current and battery voltage were measured to compute the instantaneous power consumption of the propulsion system. This approach allowed for the determination and comparison of energy consumption across the two holding patterns. The outcomes contribute to a better understanding of power efficiency during prolonged flight maneuvers, supporting advancements in autonomous low-altitude UAS operations.

Investigation on the effects of C-band high-power microwave on unmanned aerial vehicle system

When the unmanned aerial vehicle (UAV) is irradiated by high-power microwave (HPM), its internal electronic components are susceptible to be affected. This paper establishes a software platform for real-time monitoring of each module's operational status and conducts irradiation tests. The results reveal that HPM radiation causes disruptions such as GPS interference, UAV shutdown, and datalink interruption. In this paper, the UAV and its internal cables are modeled to study the effect of microwaves on the UAV, and the field-circuit co-simulation under HPM radiation is carried out. The results show that HPM can cause high voltage pulses to couple onto the connecting cables of the above modules, which may lead to the abnormal operation of UAV devices. Additionally, it explains the reason for the different effect thresholds by combining simulation and test results. Finally, several suggestions are provided for improving the UAV's resistance to out-of-band electromagnetic interference.

Estimating biomass and above-ground carbon stocks of mangrove forests by using unmanned aerial systems (Southern Vietnam)

Abstract Mangrove forests are considered potential carbon sinks in the atmosphere, surpassing other terrestrial ecosystems and playing a crucial role in the global carbon cycle. As the world strives towards climate neutrality and zero greenhouse gas emissions, the importance of mangrove forests is becoming increasingly evident. The application of technology and science to measure, monitor, and manage mangrove forests for enhanced efficiency, accuracy, and cost reduction is paramount. The study was conducted in mangrove forests in southern Vietnam, a total of 96 trees from various species were measured in the field to validate the accuracy of the UAV method using statistical indices such as Root Mean Square Error (RMSE) and Coefficient of Determination (R2). We constructed a correlation model between canopy height and diameter at breast height (DBH), where canopy height was the independent variable and DBH was the dependent variable. The ground-based biomass model based on height variables was used to estimate mangrove forests biomass and above-ground carbon stocks. We estimated mangrove species using an object-oriented classification method to determine mangrove species boundaries. The estimated heights from UAV correlated closely with ground-truth heights, with R2 = 0.99 and RMSE = 0.2 m. There was a strong correlation between canopy height from UAV (CHMuav) and DBH, with R2 = 0.95 and RMSE = 0.40 cm. The estimated canopy height (CHMuav) ranged from 1 m to 21.5 m. The object-oriented classification model for mangrove forests achieved an overall classification accuracy (OA) of 89% and a Kappa coefficient of 0.85. Above-ground biomass of Rhizophora apiculata forest with an average of 45 Mg ha−1; Avicennia alba species with an average of 22 Mg ha−1; Above-ground biomass of mixed-species with an average of 25 Mg ha−1. The above-ground carbon stocks of Rhizophora apiculata, Avicennia alba, and mixed-species have been estimated. Using the Unmanned Aerial Vehicle (UAV) and Real-Time Kinematic (RTK) methods reduced the uncertainty in estimating above-ground biomass and carbon stocks of mangrove forest.