Search Results (86)

This paper aims to analyze the performance of a tilt-rotor fixed-wing RPAS (Remotely Piloted Aircraft System) using a segmented approach, focusing on a nominal mission for SAR (Search and Rescue) applications. The study employs optimization techniques tailored to each segment to meet power consumption requirements, and the results highlight the accuracy of the physical characterization, which incorporates nonlinear propulsive and aerodynamic models derived from wind tunnel test campaigns. Critical segments for this nominal mission, such as the vertical take off or the transition from vertical to horizontal flight regimes, are addressed to fully understand the performance response of the aircraft. The proposed framework integrates experimental models into trajectory optimization procedures for each segment, enabling a realistic and modular analysis of energy use and aerodynamic performance. This approach provides valuable insights for both flight control design and future sizing iterations of convertible UAVs (Uncrewed Aerial Vehicles). Full article

Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R² = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R² values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems. Full article

Remotely piloted aircraft are a fast-emerging sector of the aviation industry. Although technical failures have been the largest cause of accident occurrences for Remotely Piloted Aircraft Systems (RPASs), if they are to follow the path of conventionally crewed aviation, Human Factors (HFs) will increasingly contribute to accidents as the technology of RPASs improves. Examining an RPAS accident database from 2008–2019 for HF-caused accidents and coding to the Human Factors Analysis and Classification System (HFACS) taxonomy, an exploration of RPAS HFs is carried out and the predominant HF issues for RPAS pilots identified. The majority of HF accidents were coded to the Unsafe Acts level of the HFCAS. Skill errors, depth perception and environmental issues were the largest contributors to HF RPAS safety occurrences. A comparison with other sectors of aviation is also made where perception issues were found to be a greater contributor to occurrences for RPAS pilots than for other sectors of aviation. Developing appropriate training programs to develop skilled RPAS operators with good depth perception can contribute to a reduction in RPAS accident rates. The importance of reporting RPAS incidents is also discussed. Full article

The survivability approach necessitates a vulnerability assessment, which quantifies the likelihood that a platform will be rendered inoperative when exposed to a threat—whether man-made or natural. This concept is closely tied to survivability, defined as the probability that a platform will complete its assigned mission. Detection and potential exposure to a threat can significantly reduce a system’s survivability. As a result, vulnerability evaluation has become a critical aspect of designing platforms that operate in high-risk environments. Numerous techniques have been developed for vulnerability assessment, with many studies aimed at achieving increasingly accurate evaluations to improve the reliability and safety of mechanical systems. Notably, in 1985, Ball introduced the concept of survivability, outlining various design solutions and techniques for fixed-wing and rotary-wing aircraft. Since then, several vulnerability assessment programs have been launched, leading to the creation of some of the most resilient platforms in use today. The assessment of vulnerability plays a key role in determining solutions to enhance the likelihood of a system successfully completing its mission. In this context, this paper presents the application of in-house software to analyze a fixed-wing Remotely Piloted Aircraft System (RPAS). The model used to validate the software’s capabilities was developed using publicly available data, enabling a practical demonstration of the software’s functionality. Applied to this case study, the software assesses the RPAS vulnerability against various impact threats. The software not only evaluates vulnerability but also suggests protective solutions to mitigate it. This application demonstrates how the software can enhance the reliability and safety of an existing operational system while also showcasing its potential for use during the preliminary design phase of a broader range of platforms. Full article

Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of demand for remotely piloted aircraft available on the market in the recent decade, have been conducive to remote sensing data processes. The objective of this work was to evaluate the best ML and input configurations in the classification of agronomic variables in different phenological stages. The spectral variables were obtained in three phenological stages of soybean genotypes: V8 (at 45 days after emergence—DAE), R1 (60 DAE), and R5 (80 DAE). A Sensefly eBee fixed-wing RPA equipped with the Parrot Sequoia multispectral sensor coupled to the RGB sensor was used. The Sequoia multispectral sensor with an RGB sensor acquired reflectance at wavelengths of blue (450 nm), green (550 nm), red (660 nm), near-infrared (735 nm), and infrared (790 nm). The following were used to evaluate the agronomic traits: days to maturity, number of branches, productivity, plant height, height of the first pod insertion and diameter of the main stem. The random forest (RF) model showed greater accuracy with data collected in the R5 stage, whose accuracies were close to 56 for the percentage of correct classifications (CC), close to 0.2 for Kappa, and above 0.55 for the F-score. Logistic regression (RL) and support vector machine (SVM) models showed better performance in the early reproductive stage R1, with accuracies above 55 for CC, close to 0.1 for Kappa, and close to 0.4 for the F-score. J48 performed better with data from the V8 stage, with accuracies above 50 for CC and close to 0.4 for the F-score. This reinforces that the use of different specific spectra for each model can enhance accuracy, optimizing the choice of model according to the phenological stage of the plants. Full article

This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of two RPAS, taking into account their operational requirements and limitations, recognizing the operating states, and addressing potential situations encountered during formation flight. For this study, the “Conformance and Fault Injection—CoFI” methodology is employed. This methodology guides the user towards a comprehensive understanding of the system and enables the creation of a set of finite state machines representing the system’s behavior under study. Consequently, models and requirements for the behavior of multi-RPAS flying in formation are presented. By applying the CoFI methodology to inject faults into the operation and predict behavior in anomalous situations, both normal and abnormal behavior models, as well as the flight behavior requirements of the multi-RPAS formation, are outlined. This analysis is expected to facilitate the identification of formation flight behavior in multi-RPAS, thereby reducing associated operational risks. Full article

The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies on Antarctic UAV vegetation mapping, focusing on their methodologies, including surveyed locations, flight guidelines, UAV specifications, sensor technologies, data processing techniques, and the use of vegetation indices. Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. While initial studies suggest that DL models could match or surpass the performance of established classifiers, even on small datasets, the integration of these advanced models into real-time navigation systems on UAVs remains underexplored. This paper evaluates the feasibility of deploying UAVs equipped with adaptive path-planning and real-time semantic segmentation capabilities, which could significantly enhance the efficiency and safety of mapping missions in Antarctica. This review discusses the technological and logistical constraints observed in previous studies and proposes directions for future research to optimise autonomous drone operations in harsh polar conditions. Full article

The use of unmanned aerial spraying systems is currently being explored and applied worldwide. The objective of this study was to characterize the droplet population generated by hydraulic nozzles and centrifugal atomization nozzles used in sprayers mounted on remotely piloted aircraft (RPA). Two spray nozzle technologies were tested using a Malvern SprayTech laser particle size meter. The hydraulic nozzle evaluated was model 11001, which generates a wide-use fan spray. The centrifugal atomization nozzle, used in RPA sprayers, was manufactured by Yuenhoang, model DC12V. The experimental design was implemented in a completely randomized scheme, containing variations in the nozzles (hydraulic nozzle and centrifugal atomization nozzle) and application rate (AR) (5, 10, and 15 L ha⁻¹ in the test with the hydraulic nozzle; and 9.2, 12.8, and 15.6 L ha⁻¹ in the test with the centrifugal nozzle), with five replicates per treatment. The hydraulic nozzle test data showed a coefficient of variation of 6.8% VMD for all treatments, with droplet sizes within the fine classification ranging from 132.8 to 163.2 µm. It is noteworthy that the average relative span (span) of the droplet population generated by the hydraulic nozzle was 1.2, i.e., 20% higher than the desired reference value of 1. This value exceeds the general average reported for the centrifugal atomization nozzle, which has a span of 1.1. The relative span of the droplet size distribution for the hydraulic nozzles is greater than that observed with the centrifugal atomization nozzles. Excluding the extreme rotational speeds of the centrifugal atomization nozzle, the percentage of droplets generated with a volume smaller than 100 µm is lower compared to those produced by the hydraulic nozzle. Full article

Grapevines are subjected to many physiological and environmental stresses that influence their vegetative and reproductive growth. Water stress, cold damage, and pathogen attacks are highly relevant stresses in many grape-growing regions. Precision viticulture can be used to determine and manage the spatial variation in grapevine health within a single vineyard block. Newer technologies such as remotely piloted aircraft systems (RPASs) with remote sensing capabilities can enhance the application of precision viticulture. The use of remote sensing for vineyard variation detection has been extensively investigated; however, there is still a dearth of literature regarding its potential for detecting key stresses such as winter hardiness, water status, and virus infection. The main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection. To accomplish the objective, remotely sensed data such as the normalized difference vegetation index (NDVI) and thermal imaging from RPAS flights were measured from six commercial vineyards in Niagara, ON, along with the manual measurement of key viticultural data including vine water stress, cold stress, vine size, and virus titre. This study verified that the NDVI could be a useful metric to detect variation across vineyards for agriculturally important variables including vine size and soil moisture. The red-edge and near-infrared regions of the electromagnetic reflectance spectra could also have a potential application in detecting virus infection in vineyards. Full article

The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking. Full article

In line with the concept of precision agriculture, this study aimed to validate the use of digital aerial images captured using a remotely piloted aircraft (RPA) for collecting zootechnical data on cattle feedlot systems in a tropical environment. Images were captured on 21 non-consecutive days in 110 pens with up to 150 animals each. Conventional and RPA-based methods were adopted to determine animal behavior, feed trough levels, animal counts, and pen conditions. Data analysis revealed almost perfect agreement (kappa coefficient = 0.901) between trough readings taken by conventional and RPA methods as well as substantial agreement for fecal score (kappa coefficient = 0.785) and surface conditions (kappa coefficient = 0.737). However, animal counts and water quality scores showed only fair agreement, suggesting challenges in using RPA for these specific tasks. The results indicated that RPA represents a viable alternative to conventional methods for monitoring zootechnical indices in feedlots, offering benefits in terms of accuracy, efficiency, and cost-effectiveness. The implementation of RPA-based methods holds potential for improving animal management, welfare, and yield in feedlot systems. Full article

The Mexican Caribbean coastal dune is protected by national and international environmental legislation. However, through permits, concessions and authorizations for changes in land use, the coastal dune has been fragmented or suppressed, mainly for touristic activities, causing a decline in protective and ecological ecosystem services. In this study, we evaluated the strength and weakness of Mexican legislation to protect the Caribbean coastal dune ecosystem and estimated the historical and current effects on coastal dune vegetation and dune geomorphology, associated with legal allowances of land use change in the Sian Ka’an Biosphere Reserve (SKBR). Legislation at the federal, state and local level were critically reviewed, and with remote sensing techniques and the Remotely Piloted Aircraft System (RPAS), we conducted a case study in the SKBR to estimate coastal dune vegetation alteration trends during the period 2011–2020 and modifications on the dune geomorphology associated with land use change allowances. At the federal (four laws), state (eight laws) and local (nine Local and Territorial Planning Programs (POEL and POET) levels, we found a lack of consensus and alignment between regulations, starting with a lack of definition of ecosystems subject to protection. For coastal dunes, none of them consider topography, ecological function and a way to identify it in the field, making the surveillance highly complex and favoring land use changes, the removal of vegetation and dune geomorphology alteration. Remote sensing techniques showed that areas with land use authorizations exhibit negative vegetation cover trends (Mann–Kendall <−0.4), indicating a decline in vegetation cover density that is mostly anthropogenically induced. The RPAS analysis demonstrated drastic alterations to complete elimination of the coastal dune geomorphology in areas with land use change. In the Mexican Caribbean, the loss of coastal dune and associated ecosystem by the lack of congruent legislation threatens the environmental stability of the coastal areas. Full article

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. Full article

This study explores the potential of micro-credentialing and digital badges in developing and validating the knowledge, skills, and other attributes (KSaOs) required for diverse Remotely Piloted Aircraft Systems (RPAS) operations. The rapid proliferation of drone usage has outpaced the development of necessary KSaOs for safe and efficient drone operations. This research aims to bridge this gap by identifying the unique and specific KSaOs required for different types of drone operations and examining how micro-credentialing and digital badges can provide tangible evidence of these KSaOs. The study also investigates the potential benefits and challenges of implementing digital badges in the RPAS sector and how these challenges can be addressed. Furthermore, it explores how digital badges can contribute to the standardization and recognition of RPAS competencies across different national regulatory bodies. The methodology involves observational studies of publicly available videos of drone operations, with a focus on agriculture spraying operations. The findings highlight the importance of both generic and specific KSaOs in RPAS operations and suggest that digital badges may provide an effective means of evidencing mastery of these competencies. This research contributes to the ongoing discourse on drone regulation and competency development, offering practical insights for regulators, training providers, and drone operators. Full article

Discontinuity is a key element used by geoscientists and civil engineers to characterize rock masses. The traditional approach to detecting and measuring rock discontinuity relies on fieldwork, which poses dangers to human life. Photogrammetric pattern recognition and 3D measurement techniques offer new possibilities without direct contact with rock masses. This study proposes a new approach to detect discontinuities using close-range photogrammetric techniques and convolutional neural networks (CNNs) trained on a small amount of data. Investigations were conducted on basalts in Bala, Ankara, Türkiye. A total of 34 multi-view images were collected with a remotely piloted aircraft system (RPAS), and discontinuity lines were manually delineated on a point cloud generated from these images. The lines were back-projected onto the raw images to increase the amount of data, a process we call multi-view (3D) augmentation. We further evaluated radiometric and geometric augmentation methods, the contribution of multi-view augmentation to the proposed model, and the transfer learning performance of six different CNN architectures. The highest performance was achieved with U-Net + SE-ResNeXt-50 with an F1-score of 90.6%. The CNN model trained from scratch with local features also yielded a similar F1-score (91.7%), which is the highest performance reported in the literature. Full article