UAVs Research Articles

Multi-scale design of MWCNT/glass fiber/balsa wood composite multilayer stealth structure with wide broadband absorption and excellent mechanical properties

In unmanned aircraft applications, electromagnetic wave (EMW) absorbers suffer from defects in narrow absorption bands and poor mechanical properties. To solve the problems, a lightweight multilayer stealth structure with wide broadband absorption performance and excellent mechanical properties was designed and prepared by adjusting microscopically the number of multi-walled carbon nanotubes (MWCNT) and modulating macroscopically the thickness-matching relationship of the structure to promote the absorption of EMW synergistically. Under the MWCNT of 30 wt% and the depletion layer with the thickness of 0.2 mm, the effective absorption bandwidth (EAB) covers the entire Ku-band while maintaining a minimum reflection loss (RL) of −15 dB. Besides, the radar cross-sectional area attenuation is as high as 23.1 dBm2, as well as the mechanical properties of the radar absorbing structures (RAS) were improved significantly due to the reducing structural density from balsa wood and the enhancement effect of glass fiber mats (GFM). The study constructed balsa-based RAS with excellent EMW absorbing and mechanical properties from both micro-nano scale and macro-structure, providing a research route for designing high-performance and lightweight stealth structures.

AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests.

[formula omitted] detector-based state feedback control of continuous-time MJLS subject to uncertain transition rate matrices

This paper uses detector-based mode information to address the H2 state-feedback control problem for continuous-time Markov Jump Linear Systems (MJLS). The focus is on designing robust controllers capable of handling uncertainties affecting both the Markov process and its detector, a less explored scenario in the literature. The main contribution lies in presenting a new LMI-based algorithm within a suitable synthesis framework to deal with uncertain parameters. A key advantage is the absence of constraints on the optimization variables, even in cases involving structured controllers like decentralized control. To illustrate the effectiveness of the proposed technique, a numerical example based on a linearized model of an unmanned aircraft is provided, showcasing its superiority over existing approaches.

Research on the Collision Risk of Fusion Operation of Manned Aircraft and Unmanned Aircraft at Zigong Airport.

Low-altitude airspace is developing rapidly, but the utilization rate of airspace resources is low. Therefore, in order to solve the problem of the safe operation of the fusion of large UAVs and manned aircraft in the same airspace, this paper analyzes the theoretical calculation of the collision risk of the fusion operation of manned aircraft and UAVs at Feng Ming Airport in Zigong, verifying that while assessing the safety spacing of 10 km in the lateral direction, it further simulates the possibility of calculating the theoretical smaller safety spacing. The study will propose a new theory of error spacing safety margin and improve it according to the traditional Event collision risk model, combining the error spacing safety margin to establish an improved collision model more suitable for the fusion operation of manned and unmanned aircraft and reduce the redundancy of calculation. The error factors affecting manned and unmanned aircraft at Zigong Airport are analyzed, and theoretical calculations are analyzed by combining the actual data of Zigong Airport. Finally, the Monte Carlo simulation method is used to solve the error, substitute the calculation results, and simulate a section of the trajectory of the fusion operation for the reverse argument. The theoretical calculation results show that the collision risk from 10 km to 8 km satisfies the lateral target safety level (TSL) specified by ICAO under both traditional and improved models. The collision risk calculated by the improved model incorporating the error spacing safety margin is smaller, which enhances the safety of the model calculations. The results of the study can provide theoretical references for the fusion operation of manned and unmanned aircraft.

Nonlinear Surrogate Model Design for Aerodynamic Dataset Generation Based on Artificial Neural Networks

In this work we construct a surrogate model using artificial neural networks (ANN) to predict the steady-state behavior of an unmanned combat aircraft. We employ various strategies to improve the model’s accuracy, including the consideration of design tolerances, creating independent surrogate models for the different flow regimes and encoding non-numeric input features. We also explore alternative machine learning models, albeit they demonstrated a lower reliability than ANNs. Two scenarios are considered for the target variable: one focusing solely on predicting the pitching moment coefficient, and the other incorporating the roll moment coefficient as well. We investigate different methods for handling multiple targets, finding that constructing a single model with multiple outputs consistently outperforms developing separate models for each target variable. Overall, the ANN provides predictions that show excellent agreement with the experimental data, demonstrating its effectiveness and reliability in aerodynamic modeling.

Blue Unmanned Aircraft Systems Explained: The Current Drone Market, Flight Regulations, and Debunking Common Misconceptions

The unmanned aircraft system (UAS) sector is a rapidly evolving domain characterized by fast technological advancement, diverse use cases, and an intricate regulatory environment. As a result, understanding and navigating the UAS industry presents a complex challenge for operators, manufacturers, regulatory authorities, and data users alike. This study provides a comprehensive analysis of the UAS market in the United States, with a particular focus on the Defense Innovation Unit’s (DIU’s) Blue Small UAS (sUAS) initiative. This paper provides an overview of the current UAS market in the U.S., accounting for 49 manufacturers from 11 countries. The study offers a breakdown of market shares and identifies the significant players, primarily the U.S. and China, where 53% and 18% of all manufacturers reside, respectively. It also discusses the impact of the newly introduced Remote ID regulations and the compliance among UAS manufacturers with these regulations. Moreover, the study covers the Blue sUAS list, addressing its origins and purpose, as well as the often-misunderstood implications for the broader UAS market. The study clarifies that Blue-listed UAS are not exclusively superior or the only options for purchase by the U.S. Department of Defense (DoD) or other U.S. government (USG) agencies. Moreover, it highlights that the Blue sUAS list’s suitability for military applications does not automatically render it optimal for civilian use, especially in infrastructure applications. This research aims to deepen understanding and stimulate informed discussion around the U.S. UAS market, regulatory compliance, the Blue sUAS list, and the broader implications for drone usage in both military and civilian contexts.

Spray Deposition and Losses to Soil from a Remotely Piloted Aircraft and Airblast Sprayer on Coffee

Spray Deposition and Losses to Soil from a Remotely Piloted Aircraft and Airblast Sprayer on Coffee

The radiative and geometric properties of melting first-year landfast sea ice in the Arctic

Abstract. In polar regions, sea ice is a crucial mediator of the interaction between Earth's atmosphere and oceans. Its formation and breakup is intimately connected with large-scale climatic processes, local weather patterns, and the use of sea ice in coastal Arctic regions by Indigenous people. In order to investigate the physical phenomena at the heart of this process, a set of targeted, intensive observations were made over spring sea ice melt and breakup in Kotzebue Sound, Alaska. These observations were planned and executed through a collaborative effort in which an Indigenous Elder advisory council from Kotzebue and scientists participated in co-production of hypotheses and observational research, including a stronger understanding of the physical properties of sea ice during spring melt. Here we present the results of observations performed using high-endurance, fixed-wing uncrewed aerial vehicles (UAVs) containing custom-built scientific payloads. Repeated flights over the measurement period captured the early stages of the transition from a white, snow-covered state to a broken-up, bare blue-green state. We found that the reflectance of sea ice features depends strongly on their size. Snow patches get darker as they get smaller, an effect owed to the geometric relationship between the bright interior and the darker, melting feature edges. Conversely, bare patches get darker as they get larger. For the largest ice features observed, bare blue-green ice patches were found to be ≈ 20 % less reflective than average across all observational cases, while large snowy white ice patches were found to be ≈ 20 % more reflective than that same average.

Predictive air combat decision model with segmented reward allocation

In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage.

Mapping the Complexity of Legal Challenges for Trustworthy Drones on Construction Sites in the United Kingdom

Drones, unmanned aircraft controlled remotely and equipped with cameras, have seen widespread deployment across military, industrial, and commercial domains. The commercial sector, in particular, has experienced rapid growth, outpacing regulatory developments due to substantial financial incentives. The UK construction sector exemplifies a case where the regulatory framework for drones remains unclear. This article investigates the state of UK legislation on commercial drone use in construction through a thematic analysis of peer-reviewed literature. Four main themes, including opportunities, safety risks, privacy risks, and the regulatory context, were identified along with twenty-one sub-themes such as noise and falling materials. Findings reveal a fragmented regulatory landscape, combining byelaws, national laws, and EU regulations, creating business uncertainty. Our study recommends the establishment of specific national guidelines for commercial drone use, addressing uncertainties and building public trust, especially in anticipation of the integration of “autonomous” drones. This research contributes to the responsible computing domain by uncovering regulatory gaps and issues in UK drone law, particularly within the often-overlooked context of the construction sector. The insights provided aim to inform future responsible computing practices and policy development in the evolving landscape of commercial drone technology.

Use of drones in the monitoring of the European pond turtle Emys orbicularis in habitats of the Poleski National Park (Poland)

The study tested the suitability of consumer drones for monitoring the abundance of the European pond turtle Emys orbicularis, an endangered species whose reliable monitoring is crucial for active species conservation. The widely available semi-professional DJI Mavic Air2 model was used, as it can be used to obtain 48 Mpix images or 4K videos. Until now, the species has been monitored visually with binoculars, but this method only works well in specific habitats, such as ponds and drainage ditches. It has been shown that the best method in inaccessible habitats, such as tall rushes, sedges, etc., is to plan a drone flight along a specific trajectory and take a series of photos. In addition, the size of the area can be accurately assessed, and animal density can be calculated. The research is essential for retardation of negative changes in the state of biodiversity in Poland.

Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery

Effective management of bark beetle infestations requires prompt detection of attacked trees. Early attack is also called green attack, since tree foliage does not yet show any visible signs of tree decline. In several bark beetle systems, including mountain pine beetle and European spruce bark beetle, unpiloted aerial vehicle (UAV)-based remote sensing has successfully detected early attack. We explore the utility of remote sensing for early attack detection of southern pine beetle (SPB; Dendroctonus frontalis Zimm.), paired with detailed ground surveys to link tree decline symptoms with SPB life stages within the tree. In three of the northernmost SPB outbreaks in 2022 (Long Island, New York), we conducted ground surveys every two weeks throughout the growing season and collected UAV-based multispectral imagery in July 2022. Ground data revealed that SPB-attacked pitch pines (Pinus rigida Mill.) generally maintained green foliage until SPB pupation occurred within the bole. This tree decline behavior illustrates the need for early attack detection tools, like multispectral imagery, in the beetle’s northern range. Balanced random forest classification achieved, on average, 78.8% overall accuracy and identified our class of interest, SPB early attack, with 68.3% producer’s accuracy and 72.1% user’s accuracy. After removing the deciduous trees and just mapping the pine, the overall accuracy, on average, was 76.9% while the producer’s accuracy and the user’s accuracy both increased for the SPB early attack class. Our results demonstrate the utility of multispectral remote sensing in assessing SPB outbreaks, and we discuss possible improvements to our protocol. This is the first remote sensing study of SPB early attack in almost 60 years, and the first using a UAV in the SPB literature.

An improved wireless power transmission system for micro unmanned aerial vehicles

AbstractDue to its limited load capacity, the piggybacking of wireless charging systems for micro unmanned aerial vehicles has become a challenging task in the design of micro unmanned aerial vehicles. Therefore, this paper proposes an improved lightweight design method for micro unmanned aerial vehicles to carry a wireless charging system. The improved system uses an inductor–capacitor–capacitor‐parallel (LCC‐P) topology compensation network instead of a bilateral inductor–capacitor–capacitor (LCC) topology compensation network to achieve constant current charging (CC) and constant voltage charging (CV). Then the adaptive frequency conversion control is used to realize the automatic conversion from CC charging to CV charging during the charging process. Compared with the traditional wireless charging system based on a direct current‐direct current converter (DC‐DC converter) to achieve CC charging and CV charging, this improved system reduces the total weight of the receiving end of the wireless charging system based on ensuring the charging efficiency, which meets the demand of wireless charging systems carried by micro drone aircraft. The test results of the constructed physical system coincide with the simulation design results, and the total weight of the receiving end is only 8 g, which verifies the correctness and effectiveness of the improved design scheme.

Optimization of 3D Printing While Traveling En Route to Extend Range of Unmanned Aircraft Systems for Multilocation Mission Scenarios

Abstract The nexus of two relatively recent technologies, additive manufacturing and unmanned aircraft systems (UAS), has enabled new and unique capabilities that have only started to be realized in integrated systems. This article explores and quantifies the impact of 3D printing parts for UAS, or entire UAS systems, on an agent platform, while this agent travels to multiple locations as part of a mission objective. The fully printed or enhanced UAS can then be released at launch points farther away from the goal locations. This, in turn, can accelerate mission completion times and reduce travel costs depending upon the ratio between vehicle speed and 3D printing rate. Thousands of scenarios are optimized across the design space to minimize the travel path length for the agent platform as a result of 3D printing en route to the locations of interest. Results indicate that based on the print capability and agent travel speed, an exponential decay in the amount of travel distance of the agent platform occurs. For unity ratios of print speed and agent speed in the considered design space, a decrease of 55% in the total required distance of our agent is observed. This reduction in total travel distance can reduce time, fuel, cost, and other aspects including other environmental and social impacts. A generalized optimization formulation is also presented at the end to enable similar analyses with other en route range-extending technology such as battery charging.

Noise in sustainable transformation of aviation

Rising awareness of aviation noise's health and annoyance impacts does not coincide with the industry's sustainable transition which is primarily driven by climate change. While electrification and sustainable fuels are seen as solutions, current strategies often encourage aviation sector growth including airport and traffic expansion. National initiatives aim to accelerate aviation's sustainable transformation, aligning with increased transportation accessibility and mobility goals. There is a push for directives for electric aviation, drones, and urban air mobility (UAM). Local health and annoyance, however, are overshadowed by the pretense of the global focus. Increased noise is anticipated with projected increases in air travel and added noise from electric and drone aircraft! This paper integrates noise and annoyance considerations into sustainable aviation transformation. A system analysis, using soft system methodologies, reveals comprehensive socio-technical perspectives from aviation, urban planning, citizens, and regulation. Findings indicate future noise challenges and unfavorable conditions for noise reduction innovations. The study advocates for society-wide strategies to manage aviation noise and annoyance, emphasizing recommendations like careful design, operational considerations, noise simulations, and democratic community involvement in implementation. These insights anticipate a surge in noise-related complaints and opposition. This paper is of interest also for future aviation developers.

Characterization of multi-rotor aircraft using truss support structure

Characterization of multi-rotor drone aircraft noise is vital to expanded use within the United States. To provide compatibility with current propagation and perception models, acoustic noise characterization focuses on the creation of equivalent lower hemispheres, and application of methods developed for manned aircraft for description of the noise directivity patterns. But a previous measurement determined that a refinement of these methods was required for the application to drone noise. One of these refinements was the use of a larger truss support rig, which was selected to provide sufficient clearance for the aircraft. The rig also provided regular holes where the microphones’ support arms could be mounted; the rigid structure possessed no flat faces and minimal cylindrical truss structures to minimize scattering of reflected signals. In addition to the lighting rig array, a number of additional microphones were placed along the flight path, and in-plane but outside of the measurement array for validation of the acoustic sources. With this improved array, another measurement of a multi-rotor aircraft was conducted, equivalent acoustic sources constructed, and validation performed. This measurement validates the use of the square array to characterize these types of aircraft.

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Secure UAV (Drone) and the Great Promise of AI

UAVs have found their applications in numerous applications from recreational activities to business in addition to military and strategic fields. However, research on UAVs is not going on as quickly as the technology. Especially, when it comes to the security of these devices, the academia is lagging behind the industry. This gap motivates our work in this article as a stepping stone for future research in this area. A comprehensive survey on the security of UAVs and UAV-based systems can help the research community keep pace with, or even lead the industry. Although there are several reviews on UAVs or related areas, there is no recent survey broadly covering various aspects of security. Moreover, none of the existing surveys highlights current and future trends with a focus on the role of an omnipresent technology such as AI. This article endeavors to overcome these shortcomings. We conduct a comprehensive review on security challenges of UAVs as well as the related security controls. Then we develop a future roadmap for research in this area with a focus on the role of AI. The future roadmap is established based on the identified current trends, under-researched topics, and a future look-ahead.

Privacy’s Sky-High Battle: The Use of Unmanned Aircraft Systems for Law Enforcement in the European Union

Benefiting from the rapid advancements in Unmanned Aircraft Systems (UAS) technology with enhanced tracking and data collection capabilities, law enforcement authorities re-discovered air as a dimension where state power can be exercised in a more affordable, accessible, and compact way. On the other hand, during law enforcement operations, UAS can collect various types of data that can be personal or sensitive, threatening the right to privacy and data protection of the data subjects. Risks include challenges related to data security, bulk data collection, the diminished transparency and fairness resulting from the inconspicuous nature of UAS, as well as ethical concerns intertwined with privacy and data protection. Upon examination of the legal framework including the General Data Protection Regulation the Law Enforcement Directive, various Aviation rules, and the new proposal for the Artificial Intelligence Act, it becomes apparent that the EU legal framework’s adequacy in safeguarding privacy and data protection against law enforcement use of UAS is context-dependent, varying across use cases. The current framework lacks clarity, leading to arbitrary application and limited protection for data subjects. Enforcement of safeguards is insufficient, and the Aviation Regulations, applicable to law enforcement UAS, require member states' opt-in, which has not occurred as of the authors’ knowledge. The Artificial Intelligence Act addresses UAS operations but focuses on market risks rather than obligations imposed on law enforcement authorities. Consequently, the existing framework is rendered inadequate for medium to high-risk law enforcement operations, leaving individuals vulnerable and insufficiently protected against intrusive UAS surveillance. Rectifying this involves addressing the enforcement gap and making the necessary amendments to relevant regulatory aspects. Additionally, the implementation of specific technical measures and steps to foster effective cooperation among stakeholders in UAS deployment for law enforcement is imperative.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.