UAVs Research Articles

Smart surveillance: deploying drones for efficient and adaptive monitoring solutions

The growing need for real-time and adaptable surveillance solutions has prompted the investigation of cutting-edge technologies within security and monitoring frameworks. This paper introduces an innovative surveillance approach that utilizes drones to improve situational awareness and operational efficiency across various domains, including public safety, border security, and environmental monitoring. The proposed system integrates autonomous drones equipped with sophisticated imaging capabilities, data processing techniques, and machine learning algorithms, facilitating real-time monitoring, threat identification, and comprehensive data analysis. Notable features of this system include autonomous path planning, the ability to adapt to changing environmental conditions, and effective communication with ground control stations. By merging drone technology with AI-enhanced analytics, this approach provides a scalable and cost-efficient surveillance solution that minimizes the need for human intervention while enhancing accuracy and response times. In this paper, we detail the design, implementation, and performance assessment of our system, underscoring its potential to transform conventional surveillance practices.

Remotely Piloted Aircraft for Evaluating the Impact of Frost in Coffee Plants: Interactions between Plant Age and Topography

An accurate assessment of frost damage in coffee plantations can help develop effective agronomic practices to cope with extreme weather events. Remotely piloted aircrafts (RPA) have emerged as promising tools to evaluate the impacts caused by frost on coffee production. The objective was to evaluate the impact of frost on coffee plants, using vegetation indices, in plantations of different ages and areas of climatic risks. We evaluated two coffee plantations located in Brazil, aged one and two years on the date of frost occurrence. Multispectral images were collected by a remotely piloted aircraft, three days after the occurrence of frost in July 2021. The relationship between frost damage and these vegetation indices was estimated by Pearson’s correlation using simple and multiple linear regression. The results showed that variations in frost damage were observed based on planting age and topography conditions. The use of PRA was efficient in evaluating frost damage in both young and adult plants, indicating its potential and application in different situations. The vegetation index MSR and MCARI2 indices were effective in assessing damage in one-year-old coffee plantations, whereas the SAVI, MCARI1, and MCARI2 indices were more suitable for visualizing frost damage in two-year-old coffee plantations.

Evaluation of the influence of climatic changes on the degradation of the historic buildings

While climatic change has been a widely studied topic, its impact on cultural heritage degradation remains a gap to overcome. The environment can contribute to the degradation of historic buildings and materials decay, especially temperature and humidity. So, characterizing the micro-climates of a landmark zone and understanding the influence of the recovery layers, building disposition, and street characteristics on environmental parameters are the first steps to investigating resilience strategies for heritage conservation and micro-climates. Thus, this paper presents a new approach to assess the influence of climatic warming on historic building degradation, combining multiscale experimental data and numerical modeling. The environmental parameters of the historic center of Aracati downtown, such as concentration of CO2, relative humidity, temperature, and air condition, were characterized and used together with urban volumetry to discuss the influence of on the urban microclimate and relate the data to the physical degradation of cultural heritage. An uncrewed aerial vehicle was used to register the volumetry of the historic center, and the data was used to simulate the wind conditions. Following this, numerical analysis was used to investigate the rising damp and thermal tension distributions under temperature variation over the years (from 2023 to 2100). The methodology’s applicability was demonstrated in recurrence with the Nosso Senhor do Bonfim Church, one of the most representative heritage constructions from Aracati downtown. Finally, the new methodology contributed to understanding how urban volumetry contributes to the climatic conditions of a historic area and demonstrates that the increase of the temperature can significantly affect the rising damps and the development of stress tension.

Motion primitives and sample-based trajectory planning for fixed-wing unmanned aircraft

Navigating fixed-wing unmanned aircraft presents significant challenges, primarily stemming from the intricate dynamics involved and the demand for rapid planning. In this paper, we introduce an efficient approach that concurrently addresses nonlinear control and trajectory planning challenges. To tackle the control problem, we introduce a computationally efficient motion primitive that effectively maps feasible targets to control inputs. As for trajectory planning, we propose an enhanced, rapidly exploring, random tree algorithm capable of generating high-quality trajectories with minimal exploration. Our primary motivation is to seamlessly integrate the introduced motion primitive with a sample-based trajectory-planning technique, providing a comprehensive solution to the intricate challenges posed by nonlinear control and trajectory planning for fixed-wing unmanned aircraft. Experimental results showcase our approach’s superiority, delivering enhanced control precision, faster planning speeds, and improved planning quality.

Drones reduce the time to defibrillation in a highly visited non-urban area: A randomized simulation-based trial

IntroductionOut-of-hospital cardiac arrest (OHCA) has a high global incidence and mortality rate, with early defibrillation significantly improving survival. Our aim was to assess the feasibility of autonomous drone delivery of automated external defibrillators (AED) in a non-urban area with physical barriers and compare the time to defibrillate (TTD) with bystander retrieval from a public access defibrillator (PAD) point and helicopter emergency medical services (HEMS) physician performed defibrillation. MethodsThis randomized simulation-based trial with a cross-over design included bystanders performing AED retrievals either delivered by automated drone flight or on foot from a PAD point, and simulated HEMS interventions. The primary outcome was the time to defibrillation, with secondary outcomes comparing workload, perceived physical effort, and ease of use. ResultsThirty-six simulations were performed. Drone-delivered AED intervention had a significantly shorter TTD [2.2 (95 % CI 2.0–2.3) min] compared to PAD retrieval [12.4 (95 % CI 10.4–14.4) min] and HEMS [18.2 (95 % CI 17.1–19.2) min].The self-reported physical effort on a visual analogue scale for drone-delivered AED was significantly lower versus PAD [2.5 (1 – 22) mm vs. 81 (65–99) mm, p = 0.02]. The overall mean workload measured by NASA-TLX was also significantly lower for drone delivery compared to PAD [4.3 (1.2–11.7) vs. 11.9 (5.5–14.5), p = 0.018]. ConclusionThe use of drones for automated AED delivery in a non-urban area with physical barriers is feasible and leads to a shorter time to defibrillation. Drone-delivered AEDs also involve a lower workload and perceived physical effort than AED retrieval on foot.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

Enhancing safety feedback to the design of small, unmanned aircraft by joint assessment of impact area and human fatality.

Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product in case of a crashing UAS, where is the size of the crash impact area on the ground, and is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.

An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles

This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 min. Further algorithm developments and the application of the devised problem/methodologies are also discussed.

Effect of rotation speed fluctuation on rotor noise generation: A numerical and experimental study

The practical operations of rotors for unmanned aircraft systems can undergo time-varying rotation speeds due to various uncertainty factors. This work investigates the aeroacoustic effect of the rotor’s rotation speed fluctuation using both numerical and experimental approaches. The measured real-time angular speed signal reveals that a hovering rotor experiences highly deterministic rotation speed variations in one revolution, which are closely related to the motor property. Delayed detached eddy simulations are performed, and the fluctuating rotation speed is realized by controlling the mesh displacement at each time step. The results suggest that significant additional tones are produced at the excitation frequency and neighbour frequencies, even though the amplitudes of the rotation speed fluctuations at the dominant frequencies are much smaller than the mean rotation speed. By comparing the full signal to individual harmonic components, it is found that the phase difference between each mode is unimportant. Therefore, the total aeroacoustic effect can be regarded as a linear superposition of different harmonic components. Aeroacoustic source analysis is also performed to isolate the contribution of various source types and correlate the far-field noise with the near-field source distribution. The results show that the extra tonal noise is mostly dominated by the loading noise, with a weaker contribution from the thickness noise. The agreement between the experiments and the simulations indicates that the rotation speed fluctuation is likely responsible for the generation of middle-frequency tonal noise at harmonics of the blade passing frequency.

A Systematic Approach towards the Integration of Initial Airworthiness Regulatory Requirements in Remotely Piloted Aircraft System Conceptual Design Methodologies

The regulatory framework of Remotely Piloted Aircraft Systems (RPASs) has recently experienced an extraordinary evolution. This article seeks to improve the integration of certification considerations in RPAS conceptual design approaches so as to enhance the safety, certifiability and competitiveness of their resulting designs. The first part of the research conducts a two-stage analysis of contemporary regulations related to an RPAS’s initial airworthiness. In the first stage, the broad international regulation paradigm is evaluated attending to a set of criteria that are tightly related to both airworthiness and design considerations. The second stage keeps the most promising documents from a design–integration standpoint, which are assessed according to their applicability considering both design and operational aspects. The results of this analysis provide insights regarding the main issues in airworthiness design criteria extraction and integration in design methodologies. To aid the designer in surmounting these challenges, a flexible procedure named DECEX is developed. Considering the documents and findings from the survey, and attending to the scope of the design methodology being developed, it aids in establishing a complete regulatory document corpus and in comparing and extracting the applicable airworthiness design criteria. Two case studies for different RPAS types are conducted to demonstrate its application.

Recent Development Trends in Plant Protection UAVs: A Journey from Conventional Practices to Cutting-Edge Technologies—A Comprehensive Review

Uncrewed aerial vehicles (UAVs) for plant protection play a vital role in modern agricultural operations. In recent years, advancements in UAVs and pest control technologies have significantly enhanced operational efficiency. These innovations have addressed historical challenges in agricultural practices by improving automation and precision in managing insect pests, diseases, and weeds. UAVs offer high operational efficiency, wide adaptability to different terrain, and safe applications. The development and demand for these technologies have increased to boost agricultural production. In agricultural settings where conventional machinery struggles to carry out farming operations, UAVs have transformed farming practices by providing high operational efficiency and significant profitability. The integration of UAVs and other smart technologies has driven advancements. The UAV sector has received substantial attention as a convergence of production, service, and delivery, introducing synergy through the presence of several developing areas. The market for this technology is expected to grow in the future. In this comprehensive review, we analyzed an overview of historical research, diverse techniques, the transition from conventional to advanced application, development trends, and operational milestones across diverse cropping systems. We also discussed adoption and subsidy policies. In order to properly understand UAV operational efficiency, we also analyzed and discussed smart atomization systems, spray drift, droplet deposition detection technologies, and the capabilities of related technologies. Additionally, we reviewed the role of software programs, data-driven tools, biodegradable materials, payloads, batteries, sensing technologies, weather, and operational and spraying factors. Regulatory limitations, operating and farmer’s training, economic effects, and guidelines were also acknowledged in this review. This review highlights deficiencies and provides essential knowledge of the use of UAVs for agriculture tasks in different regions. Finally, we examine the urgency of UAV technology implementations in the agricultural sector. In conclusion, we summarize the integration of UAVs and their related technologies with applications and future research prospects, offering directions for follow-up research on the key technologies of UAVs and encouraging the enhancement of agricultural production management in terms of efficiency, accuracy, and sustainability.

Repurposing weather modification for cloud research showcased by ice crystal growth.

The representation of cloud processes in models is one of the largest sources of uncertainty in weather forecast and climate projections. While laboratory settings offer controlled conditions for studying cloud processes, they cannot reproduce the full range of conditions and interactions present in natural cloud systems. To bridge this gap, here we leverage weather modification, specifically glaciogenic cloud seeding, to investigate ice growth rates within natural clouds. Seeding experiments were conducted in supercooled stratus clouds (at to C) using an uncrewed aerial vehicle, and the created ice crystals were measured 4-10 min downwind by in situ and ground-based remote sensing instrumentation. We observed substantial variability in ice crystal growth rates within natural clouds, attributed to variations in ice crystal number concentrations and in the supersaturation, which is difficult to reproduce in the laboratory and which implies faster precipitation initiation than previously thought. We found that for the experiments conducted at C, the ice crystal populations grew nearly linearly during the time interval from 6 to 10 min. Our results demonstrate that the targeted use of weather modification techniques can be employed for fundamental cloud research (e.g. ice growth processes, aerosol-cloud interactions), helping to advance cloud microphysics parameterizations and to improve weather forecasts and climate projections.

Psyllium husk mucilage as a novel seed encapsulant for agriculture and reforestation

AbstractIntroductionAn efficient, economical, and flexible reforestation system capable of uncrewed aerial vehicle (UAV) deployment is a global necessity. Hydrogels have been previously investigated for their use in agriculture, however, research is limited in terms of UAV deployment. Psyllium Husk Mucilage (PHM) a natural hydrogel seed mucilage, has been previously investigated in other agricultural settings and shows promise in fulfilling the needs of UAV seeding.Materials and MethodsA greenhouse trial was conducted under controlled laboratory conditions using two soil conditions 50% and 90% field capacity. PHM and bentonite clay blends containing dextran (DEX) and sodium alginate (SA) were tested for efficacy for two agricultural crops Green beans (Phaseolus vulgaris) and Lebanese cucumber (Cucumis sativus), as well as three Australian native species Acacia stenophylla, Cymbopogon refractus, and Eucalyptus coolabah. Agricultural Trials were conducted across a 4‐week period, whilst A. stenophylla was 8 weeks, and C. refractus and E. coolabah was 12 weeks.ResultsUtilisation of PHM had varying effectiveness based on seed and soil moisture. In nonagricultural seed trials, PHM hydrogel succeeded with A. stenophylla but dried out and rotted in the extended trials with C. refractus and E. Coolabah, leading to plant death. The encapsulated agricultural seeds were largely outperformed in growth trials by C‐Planted at 50% FC, and C‐Surface at 90% FC.ConclusionPHM exhibits potential for successful UAV seeding in low‐moisture agricultural conditions and is promising for fast‐germinating plants or other riverbank species with high water content requirements. Without extending degradation time PHM is not suitable for slow‐growing species.

Drag reduction and locomotory power in dolphins: Gray's paradox revealed.

For 88 years, biologists and engineers have sought to understand the hydrodynamics enabling dolphins to swim at speeds seemingly beyond their energetic capabilities, a phenomenon known as Gray's paradox. Hydromechanical models calculating the drag of swimming dolphins estimated power requirements for sustained high-speed swimming, which were physiologically impossible. Using an uncrewed aerial vehicle, we calculated the total power of free-ranging dusky dolphins (Lagenorhynchus obscurus) at speeds from 0.9 to 6.9 m s-1, deriving drag coefficients (Cd) and drag. Our results showed that the Cd decreased exponentially with speed, reducing drag by up to 89% at speeds >2 m s-1, with an additional 17% reduction during porpoising (>4.0 m s-1). At 6.9 m s-1, drag was 32 N, with a power of 15.8 W kg-1, nearly identical to the mass-specific allometric prediction for the maximum aerobic capacity of other mammals and physiologically possible. The Cd at speeds >2.5 m s-1 indicated reduced turbulence in the boundary layer around the dolphin's body, thereby reducing drag. The ability of dusky dolphins to swim at sustained high speeds resulted from an exponential decrease in Cd, which was further reduced by porpoising, thereby explaining the low drag and locomotory power that resolved Gray's paradox.

Computational investigation of both geometric and fluidic compressible turbulent thrust vectoring, using a Coanda based nozzle

This study addresses the challenge of enhancing aircraft maneuverability, particularly for vertical landing and takeoff, focusing on the fluidic aerial Coanda high efficiency orienting jet nozzle that employs the Coanda effect to achieve thrust vectoring. This research advances understanding of the interplay between geometric and fluidic factors in thrust vectoring. Stationary, turbulent, and compressible flow conditions are assumed, employing Favre-averaged Reynolds-averaged Navier–Stokes approach with the standard k-ε model. Computational solutions were obtained using a pressure-based finite volume method and a structured computational grid. The key findings include thrust vectoring enhancement due to an increase in the total mass flow rate, septum position (at no shock wave-related issues), and Reynolds number. In addition, shock wave formation (at specific mass flow rates and septum positions) considerably affects thrust vectoring. These insights are crucial for optimizing Coanda-based nozzle design in advanced propulsion systems, including in unmanned aircraft vehicles.

Early Warning Design for Environmental Radioactivity Using Unmanned Aircraft in a Radiation Monitoring System as an Effort to Maintain Public Health

Early Warning Design for Environmental Radioactivity Using Unmanned Aircraft in a Radiation Monitoring System as an Effort to Maintain Public Health

Erratum for the Research Article “Field deployment of Wolbachia -infected Aedes aegypti using uncrewed aerial vehicle” by Y.-H. Lin et al .

Erratum for the Research Article “Field deployment of <i>Wolbachia</i> -infected <i>Aedes aegypti</i> using uncrewed aerial vehicle” by Y.-H. Lin <i>et al</i> .

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

Research on Mobile Network Parameters Using Unmanned Aerial Vehicles.

The study of phenomena related to the propagation of electromagnetic waves is a necessity with the development of mobile telephony networks. This paper deals with the problem of the quality of mobile telephony signals. The study uses a BTS scanner, dedicated to scanning the frequency spectrum and decoding information from base stations. The device is capable of scanning mobile networks from the second, third, fourth, and fifth generation (2G/3G/4G/5G) using a passive sensor. The article presents preliminary results of monitoring cellular network signals using a scanner mounted on an unmanned aircraft. The collected data call for the need to evaluate the signal parameters of the cellular network depending on the altitude of signal reception. This property emerges as very important in the case of areas with a high level of urbanization.