Editor’s Choice Articles

Path planning of unmanned aerial vehicles (UAVs) is one of the vital components that supports their autonomy and deployment ability in real-world applications. Few path-planning techniques have been thoroughly considered for multirotor UAVs for pursuing ground moving targets (GMTs) with variable speed and direction. Furthermore, most path-planning techniques are generally devised without taking into consideration wind disturbances; as a result, they are less suitable for real-world applications as the wind effect usually causes the UAV to drift and tilt from its original course, impacting the mission’s main objective of having an adequate non-deviant camera aim point and steady coverage over the GMT. This paper presents a novel UAV path-planning technique, based on the artificial potential field (APF) for following GMTs in windy environments, to provide steady and continuous coverage over the GMT, by proposing a new modified attractive force to enhance the UAV’s sensitivity to wind speed and direction. The modified wind resistance attractive force function accommodates for any small variation of relative displacement caused by wind leading the UAV to drift in a certain direction. This enables the UAV to maintain its position by tilting (i.e., changing its roll and pitch angles) against the wind to retain the camera aim point on the GMT. The proposed path-planning technique is hardware-independent, does not require an anemometer for measuring wind speed and direction, and can be adopted for all types of multirotor UAVs equipped with basic sensors and an autopilot flight controller. The proposed path-planning technique was evaluated in a Gazebo-supported PX4-SITL and a robot operating system (ROS) for various simulation scenarios. Its performance demonstrated superiority in handling wind disturbances and showed high suitability for deployment in real-world applications. Full article

Dam removal is a river restoration technique that has complex landscape-level ecological impacts. Unmanned aerial vehicles (UAVs) are emerging as tools that enable relatively affordable, repeatable, and objective ecological assessment approaches that provide a holistic perspective of restoration impacts and can inform future restoration efforts. In this work, we use a consumer-grade UAV, structure-from-motion (SfM) photogrammetry, and machine learning (ML) to evaluate geomorphic and vegetation changes pre-/post-dam removal, and discuss how the technology enhanced our monitoring of the restoration project. We compared UAV evaluation methods to conventional boots-on-ground methods throughout the Bellamy River Reservoir (Dover, NH, USA) pre-/post-dam removal. We used a UAV-based vegetation classification approach that used a support vector machine algorithm and a featureset composed of SfM-derived elevation and visible vegetation index values to map other, herbaceous, shrub, and tree cover throughout the reservoir (overall accuracies from 83% to 100%), mapping vegetation succession as well as colonization of exposed sediments that occurred post-dam removal. We used SfM-derived topography and the vegetation classifications to map erosion and deposition throughout the reservoir, despite its heavily vegetated condition, and estimate volume changes post-removal. Despite some limitations, such as influences of refraction and vegetation on the SfM topography models, UAV provided information on post-dam removal changes that would have gone unacknowledged by the conventional ecological assessment approaches, demonstrating how UAV technology can provide perspective in restoration evaluation even in less-than-ideal site conditions for SfM. For example, the UAV provided perspective of the magnitude and extent of channel shape changes throughout the reservoir while the boots-on-ground topographic transects were not as reliable for detecting change due to difficulties in navigating the terrain. In addition, UAV provided information on vegetation changes throughout the reservoir that would have been missed by conventional vegetation plots due to their limited spatial coverage. Lastly, the COVID-19 pandemic prevented us from meeting to collect post-dam removal vegetation plot data. UAV enabled data collection that we would have foregone if we relied solely on conventional methods, demonstrating the importance of flexible and adaptive methods for successful restoration monitoring such as those enabled via UAV. Full article

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers. Full article

Although Unmanned Aerial Vehicles (UAVs)-aided wireless sensor networks (WSNs) have gained many applications, it is not for long that research works have been produced to define effective algorithms and protocols. In this article, we address the UAV-enabled WSN (U-WSN), explore the performance and the capability of the UAV, define the UAV functionalities as a communication node, and describe the architectures and the relevant typical technologies that emerge from this new paradigm. Furthermore, this article also identifies the main factors which influence the U-WSN design and analyzes the open issues and challenges in U-WSN. These insights may serve as motivations and guidelines for future designs of UAV-enabled WSNs. Full article

Coaxial rotor systems are appealing for multirotor drones, as they increase thrust without increasing the vehicle’s footprint. However, the thrust of a coaxial rotor system is reduced compared to having the rotors in line. It is of interest to increase the efficiency of coaxial systems, both to extend mission time and to enable new mission capabilities. While some parameters of a coaxial system have been explored, such as the rotor-to-rotor distance, the influence of rotor pitch is less understood. This work investigates how adjusting the pitch of the lower rotor relative to that of the upper one impacts the overall efficiency of the system. A methodology based on blade element momentum theory is extended to coaxial rotor systems, and in addition blade-resolved simulations using computational fluid dynamics are performed. A coaxial rotor system for a medium-sized drone with a rotor diameter of 71.12 cm is used for the study. Experiments are performed using a thrust stand to validate the methods. The results show that there exists a peak in total rotor efficiency (thrust-to-power ratio), and that the efficiency can be increased by 2% to 5% by increasing the pitch of the lower rotor. The work contributes to furthering our understanding of coaxial rotor systems, and the results can potentially lead to more efficient drones with increased mission time. Full article

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning. Full article

Ice accretion on commercial aircraft operating at high Reynolds numbers has been extensively studied in the literature, but a direct transformation of these results to an Unmanned Aerial Vehicle (UAV) operating at low Reynolds numbers is not straightforward. Changes in Reynolds number have a significant impact on the ice accretion physics. Previously, only a few researchers worked in this area, but it is now gaining more attention due to the increasing applications of UAVs in the modern world. As a result, an attempt is made to review existing scientific knowledge and identify the knowledge gaps in this field of research. Ice accretion can deteriorate the aerodynamic performance, structural integrity, and aircraft stability, necessitating optimal ice mitigation techniques. This paper provides a comprehensive review of ice accretion on fixed-wing UAVs. It includes various methodologies for studying and comprehending the physics of ice accretion on UAVs. The impact of various environmental and geometric factors on ice accretion physics is reviewed, and knowledge gaps are identified. The pros and cons of various ice detection and mitigation techniques developed for UAVs are also discussed. Full article

Generating accurate estimates of group sizes or behaviours of cetaceans from boat-based surveys can be challenging because much of their activity occurs below the water surface and observations are distorted by horizontal perspectives. Automated observation using drones is an emerging research tool for animal behavioural investigations. However, drone-based and boat-based survey methods have not been quantitatively compared for small, highly mobile cetaceans, such as Delphinidae. Here, we conduct paired concurrent boat-based and drone-based surveys, measuring the number of individuals in 21 groups and the behaviour within 13 groups of bottlenose dolphin (Tursiops truncatus). We additionally assessed the ability to detect behaviour events by the drone that would not be detectable from the boat. Drone-derived abundance counts detected 26.4% more individuals per group on average than boat-based counts (p = 0.003). Drone-based behaviour observations detected travelling 55.2% more frequently and association in subgroups 80.4% more frequently than boat-based observations (p < 0.001 for both comparisons). Whereas foraging was recorded 58.3% and resting 15.1% less frequently by the drone than by boat-based surveys, respectively (p = 0.014 and 0.024). A considerable number of underwater behaviours ranging from individual play activities to intra- and inter-species interactions (including those with humans) were observed from the drone that could not be detected from the boat. Our findings demonstrate that drone surveys can improve the accuracy of population counts and behavioural data for small cetaceans and the magnitude of the discrepancies between the two methods highlights the need for cautious interpretation of studies that have relied on boat-derived data. Full article

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; $R^2$ = 0.92, $RMSE$ = 3.25 dt ha${}^{-1}$), nitrogen (N) content in % of DM ($R^2$ = 0.58, $RMSE$ = 0.27%) and N-uptake ($R^2$ = 0.91, $RMSE$ = 6.50 kg ha${}^{-1}$). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, $RMSE$ values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets. Full article

Drones have become popular with the general public for viewing and filming marine life. One amateur enthusiast platform, DroneSharkApp, films marine life in the waters off Sydney, Australia year-round and posts their observations on social media. The drone observations include the behaviours of a variety of coastal marine wildlife species, including sharks, rays, fur seals, dolphins and fish, as well as migratory species such as migrating humpback whales. Given the extensive effort and multiple recordings of the presence, behaviour and interactions of various species with humans provided by DroneSharkApp, we explored its utility for providing biologically meaningful observations of marine wildlife. Using social media posts from the DroneSharkApp Instagram page, a total of 678 wildlife videos were assessed from 432 days of observation collected by a single observer. This included 94 feeding behaviours or events for fur seals (n = 58) and dolphins (n = 33), two feeding events for white sharks and one feeding event for a humpback whale. DroneSharkApp documented 101 interactions with sharks and humans (swimmers and surfers), demonstrating the frequent, mainly innocuous human–shark overlap off some of Australia’s busiest beaches. Finally, DroneSharkApp provided multiple observations of humpback and dwarf minke whales with calves travelling north, indicating calving occurring well south of traditional northern Queensland breeding waters. Collaboration between scientists and citizen scientists such as those involved with DroneSharkApp can greatly and quantitatively increase the biological understanding of marine wildlife data. Full article

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones. Full article

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements. Full article

The use of unmanned aerial vehicles (UAVs, also known as drones or RPA) in archaeology has expanded significantly over the last twenty years. Improvements in terms of the reliability, size, and manageability of these aircraft have been largely complemented by the high resolution and spectral bands provided by the sensors of the different cameras that can be incorporated into their structure. If we add to this the functionalities and improvements that photogrammetry programs have been experiencing in recent years, we can conclude that there has been a qualitative leap in the possibilities, not only of geometric documentation and in the presentation of the archaeological data, but in the incorporation of non-intrusive high-resolution analytics. The work that we present here gives a sample of the possibilities of both geometric documentation, creation of 3D models, their subsequent printing with different materials, and techniques to finally show a series of analytics from images with NGB (Nir + Green + Blue), Red Edge, and Thermographic cameras applied to various archaeological sites in which our team has been working since 2013, such as Clunia (Peñalba de Castro, Burgos), Puig Rom (Roses), Vilanera (L’Escala, Girona), and Cosa (Ansedonia, Italy). All of them correspond to different chronological periods as well as to varied geographical and morphological environments, which will lead us to propose the search for adequate solutions for each of the environments. In the discussions, we will propose the lines of research to be followed in a project of these characteristics, as well as some results that can already be viewed. Full article

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation. Full article

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m²), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m² and 2300 m²) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R², Bias, and residual charts. The basal area values ranged from 7.40 m² ha⁻¹ (C1380) to 8.14 m² ha⁻¹ 281 (C2300), about −5.9% less than the real value (8.65 m² ha⁻¹). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m³) to the real total value (683.88 m³) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m² is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time. Full article

This work presents an algorithm for an Aerial Sensor Network (ASN) composed of fixed-wing Unmanned Aerial Vehicles (UAVs) that performs surveillance and detects the early signs of a wildfire in a given territory. The main goal is to cover a given area while prioritizing areas of higher fire hazard risk. The proposed algorithm is scalable to any number of aircraft and can use any kind of fire hazard risk map as long as it contains bounded and nonnegative values. Two different dynamical models associated with the movement of fixed-wing UAVs are proposed, tested, and compared through simulations. Lastly, we propose a workflow to size the ASN in order to maximize the probability of detection of wildfires for a particular risk profile. Full article

During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work. Full article

The scope and scientific purpose of this paper focuses on multiscale (aerial and terrestrial) photogrammetry as a support to investigations and interpretations in a multi-component archaeological site located in the Argentinian Cordillera (Calchaquí, Salta), known as Tacuil. Due to its scarce accessibility, as well as long-term problems associated with the interpretation of the visibility of this type of settlement, the use of aerial surveying was combined with the reconstruction of structures and complex soil morphologies by resorting to modern photogrammetric approaches (3D models and orthophotos). This dataset was complemented by a terrestrial survey to obtain extremely high resolution and detailed representations of archaeological features that were integrated in a GIS database. The outcome of photogrammetric surveying was fundamental in supporting the debate on the functionality of the site and his integration in a complex, socially constructed, ancient landscape. Finally, the present paper introduces the first complete map of Tacuil. Full article

This paper proposes a novel robotic animal herding system based on a network of autonomous barking drones. The objective of such a system is to replace traditional herding methods (e.g., dogs) so that a large number (e.g., thousands) of farm animals such as sheep can be quickly collected from a sparse status and then driven to a designated location (e.g., a sheepfold). In this paper, we particularly focus on the motion control of the barking drones. To this end, a computationally efficient sliding mode based control algorithm is developed, which navigates the drones to track the moving boundary of the animals’ footprint and enables the drones to avoid collisions with others. Extensive computer simulations, where the dynamics of the animals follow Reynolds’ rules, show the effectiveness of the proposed approach. Full article

The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space. Full article

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows. Full article

Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems. Full article

Of the recent epidemics, the impact of the COVID-19 pandemic has been particularly severe, not only putting our health at risk, but also negatively affecting our daily lives. As there are no developed algorithms for the use of drones in epidemiological situations, it is ideal to analyze the experience gained on drones so far and outline the effective methods for future good practice. The author relies on a method of analyzing widely available open information, such as images and videos available on the Internet, reports from drone users, announcements by drone manufacturers and the contents of newspaper articles. Furthermore, the author has relied on the results of the relevant literature, as well as previous experience as a drone user and fire commander. The study reveals numerous possibilities associated with drone usage in epidemic related situations, but previous applications are based on previous experience gained during a non-epidemic situation, without developed algorithms. Applications can be divided into different types of groups: drones can collect data for management and provide information to the public, perform general or special logistical tasks to support health care and disinfect to reduce the risk of spreading the epidemic. Full article

As there has been an advancement in avionic systems in recent years, the enactment of unmanned aerial vehicles (UAV) has upgraded. As compared to a single UAV system, multiple UAV systems can perform operations more inexpensively and efficiently. As a result, new technologies between user/control station and UAVs have been developed. FANET (Flying Ad-Hoc Network) is a subset of the MANET (Mobile Ad-Hoc Network) that includes UAVs. UAVs, simply called drones, are used for collecting sensitive data in real time. The security and privacy of these data are of priority importance. Therefore, to overcome the privacy and security threats problem and to make communication between the UAV and the user effective, a competent anonymous mutual authentication scheme is proposed in this work. There are several methodologies addressed in this work such as anonymous batch authentication in FANET which helps to authenticate a large group of drones at the same time, thus reducing the computational overhead. In addition, the integrity preservation technique helps to avoid message alteration during transmission. Moreover, the security investigation section discusses the resistance of the proposed work against different types of possible attacks. Finally, the proposed work is related to the prevailing schemes in terms of communication and computational cost and proves to be more efficient. Full article

This paper aims to provide an overview of the capabilities of unmanned systems to monitor and manage aquaculture farms that support precision aquaculture using the Internet of Things. The locations of aquaculture farms are diverse, which is a big challenge on accessibility. For offshore fish cages, there is a difficulty and risk in the continuous monitoring considering the presence of waves, water currents, and other underwater environmental factors. Aquaculture farm management and surveillance operations require collecting data on water quality, water pollutants, water temperature, fish behavior, and current/wave velocity, which requires tremendous labor cost, and effort. Unmanned vehicle technologies provide greater efficiency and accuracy to execute these functions. They are even capable of cage detection and illegal fishing surveillance when equipped with sensors and other technologies. Additionally, to provide a more large-scale scope, this document explores the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity. The capabilities of existing commercial systems, the Internet of Things, and artificial intelligence combined with drones are also presented to provide a precise aquaculture framework. Full article

Unmanned aerial vehicles (UAVs) have become a research hotspot in the field of magnetic exploration because of their unique advantages, e.g., low cost, high safety, and easy to operate. However, the lack of effective data processing and interpretation method limits their further deployment. In view of this situation, a complete workflow of UAV magnetic data processing and interpretation is proposed in this paper, which can be divided into two steps: (1) the improved variational mode decomposition (VMD) is applied to the original data to improve its signal-to-noise ratio as much as possible, and the decomposition modes number K is determined adaptively according to the mode characteristics; (2) the parameters of target position and magnetic moment are obtained by Euler deconvolution first, and then used as the prior information of the Levenberg–Marquardt (LM) algorithm to further improve its accuracy. Experiments are carried out to verify the effectiveness of the proposed method. Results show that the proposed method can significantly improve the quality of the original data; by combining the Euler deconvolution and LM algorithm, the horizontal positioning error can be reduced from 15.31 cm to 4.05 cm, and the depth estimation error can be reduced from 16.2 cm to 5.4 cm. Moreover, the proposed method can be used not only for the detection and location of near-surface targets, but also for the follow-up work, such as the clearance of targets (e.g., the unexploded ordnance). Full article

At present, the great progress made by the Internet of Things ($IoT$) has led to the emergence of the Internet of Drones ($IoD$). $IoD$ is an extension of the $IoT$, which is used to control and manipulate drones entering the flight area. Now, the fifth-generation mobile communication technology ($5G$) has been introduced into the $IoD$; it can transmit ultra-high-definition data, make the drones respond to ground commands faster and provide more secure data transmission in the $IoD$. However, because the drones communicate on the public channel, they are vulnerable to security attacks; furthermore, drones can be easily captured by attackers. Therefore, to solve the security problem of the $IoD$, Hussain et al. recently proposed a three-party authentication protocol in an $IoD$ environment. The protocol is applied to the supervision of smart cities and collects real-time data about the smart city through drones. However, we find that the protocol is vulnerable to drone capture attacks, privileged insider attacks and session key disclosure attacks. Based on the security of the above protocol, we designed an improved protocol. Through informal analysis, we proved that the protocol could resist known security attacks. In addition, we used the real-oracle random model and ProVerif tool to prove the security and effectiveness of the protocol. Finally, through comparison, we conclude that the protocol is secure compared with recent protocols. Full article

Telecommunications among unmanned aerial vehicles (UAVs) have emerged recently due to rapid improvements in wireless technology, low-cost equipment, advancement in networking communication techniques, and demand from various industries that seek to leverage aerial data to improve their business and operations. As such, UAVs have started to become extremely prevalent for a variety of civilian, commercial, and military uses over the past few years. UAVs form a flying ad hoc network (FANET) as they communicate and collaborate wirelessly. FANETs may be utilized to quickly complete complex operations. FANETs are frequently deployed in three dimensions, with a mobility model determined by the work they are to do, and hence differ between vehicular ad hoc networks (VANETs) and mobile ad hoc networks (MANETs) in terms of features and attributes. Furthermore, different flight constraints and the high dynamic topology of FANETs make the design of routing protocols difficult. This paper presents a comprehensive review covering the UAV network, the several communication links, the routing protocols, the mobility models, the important research issues, and simulation software dedicated to FANETs. A topology-based routing protocol specialized to FANETs is discussed in-depth, with detailed categorization, descriptions, and qualitatively compared analyses. In addition, the paper demonstrates open research topics and future challenge issues that need to be resolved by the researchers, before UAVs communications are expected to become a reality and practical in the industry. Full article

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems. Full article

Biodiversity monitoring is crucial in tackling defaunation in the Anthropocene, particularly in tropical ecosystems. However, field surveys are often limited by habitat complexity, logistical constraints, financing and detectability. Hence, leveraging drones technology for species monitoring is required to overcome the caveats of conventional surveys. We investigated prospective methods for wildlife monitoring using drones in four ecosystems. We surveyed waterbird populations in Pulau Rambut, a community of ungulates in Baluran and endemic non-human primates in Gunung Halimun-Salak, Indonesia in 2021 using a DJI Matrice 300 RTK and DJI Mavic 2 Enterprise Dual with additional thermal sensors. We then, consecutively, implemented two survey methods at three sites to compare the efficacy of drones against traditional ground survey methods for each species. The results show that drone surveys provide advantages over ground surveys, including precise size estimation, less disturbance and broader area coverage. Moreover, heat signatures helped to detect species which were not easily spotted in the radiometric imagery, while the detailed radiometric imagery allowed for species identification. Our research also demonstrates that machine learning approaches show a relatively high performance in species detection. Our approaches prove promising for wildlife surveys using drones in different ecosystems in tropical forests. Full article

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions. Full article

In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars. Full article

Drones are now widely used to study wildlife, but their application in the study of bioacoustics is limited. Drones can be used to collect data on bird vocalizations, but an ongoing concern is that noise from drones could change bird vocalization behavior. To test for behavioral impact, we conducted an experiment using 30 sound localization arrays to track the song output of 7 songbird species before, during, and after a 3 min flight of a small quadcopter drone hovering 48 m above ground level. We analyzed 8303 song bouts, of which 2285, from 184 individual birds were within 50 m of the array centers. We used linear mixed effect models to assess whether patterns in bird song output could be attributed to the drone’s presence. We found no evidence of any effect of the drone on five species: American Robin Turdus migratorius, Common Yellowthroat Geothlypis trichas, Field Sparrow Spizella pusilla, Song Sparrow Melospiza melodia, and Indigo Bunting Passerina cyanea. However, we found a substantial decrease in Yellow Warbler Setophaga petechia song detections during the 3 min drone hover; there was an 81% drop in detections in the third minute (Wald test, p < 0.001) compared with before the drone’s introduction. By contrast, the number of singing Northern Cardinal Cardinalis cardinalis increased when the drone was overhead and remained almost five-fold higher for 4 min after the drone departed (p < 0.001). Further, we found an increase in cardinal contact/alarm calls when the drone was overhead, with the elevated calling rate lasting for 2 min after the drone departed (p < 0.001). Our study suggests that the responses of songbirds to drones may be species-specific, an important consideration when proposing the use of drones in avian studies. We note that recent advances in drone technology have resulted in much quieter drones, which makes us hopeful that the impact that we detected could be greatly reduced. Full article

The use of drones in mining and geological exploration is under rapid development, especially in the field of magnetic field prospection. In part, this is related to the advantages presented for over ground surveys, allowing for high-density data acquisition with low loss of resolution, while being particularly useful in scenarios where vegetation, topography, and access are limiting factors. This work analyzes results of a drone magnetic survey acquired across the old mines of Don Jacobo, where Copper-Cobalt-Nickel stratabound mineralizations were exploited in the Estancias mountain range of the Betic Cordillera, Spain. The survey carried out used a vapor magnetometer installed on a Matrice 600 Pro Hexacopter. Twenty-four parallel survey lines were flown with a speed of 5 m/s, orthogonal to the regional strike of the geological structure, and mineralization with 50 m line separation and 20 m flight height over the ground was studied. The interpretation of the magnetic data allows us to reveal and model two high magnetic susceptibility bodies with residual magnetization, close to the old mines and surface mineral shows. These bodies could be related to potential unexploited mineralized areas whose formation may be related to a normal fault placed to the south of the survey area. Our geophysical survey provides essential data to improve the geological and mining potential of the area, allowing to design future research activities. Full article

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI. Full article

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided. Full article

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable. Full article

This paper considers the self-localization of a tethered drone without using a cable-tension force sensor in GPS-denied environments. The original problem is converted to a state-estimation problem, where the cable-tension force and the three-dimensional position of the drone with respect to a ground platform are estimated using an extended Kalman filter (EKF). The proposed approach uses the data reported by the onboard electric motors (i.e., the pulse width modulation (PWM) signals), accelerometers, gyroscopes, and altimeter, embedded in the commercial-of-the-shelf (COTS) inertial measurement units (IMU). A system-identification experiment was conducted to determine the model that computes the drone thrust force using the PWM signals. The proposed approach was compared with an existing work that assumes known cable-tension force. Simulation results show that the proposed approach produces estimates with less than 0.3-m errors when the actual cable-tension force is greater than 1 N. Full article

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%. Full article

Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R² > 0.99) in the temperature range 5–40 °C as well as a small (<2 °C) temperature bias that is less than the stated accuracy of the cameras. Field validation was accomplished by imaging vegetation and concrete targets (outdoors and at night), that were instrumented with surface temperature sensors. Environmental parameters (air temperature, humidity, pressure and, wind and gusting) were measured for several hours prior to imaging data collection and found to either not be a factor, or were constant, during the ~30 min data collection period. In-field results from imagery at five heights between 10 m and 50 m show absolute temperature retrievals of the concrete and two vegetation sites were within the specifications of the cameras. The methodology has been developed with consideration of active RPAS operational requirements. Full article

Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) measurement and the Digital Elevation Model (DEM) of the surveilled area. The proposed system employs a waypoint scheduling logic, derived from a dynamic Voronoi Tessellation of the area, that combines characteristics of the territory (e.g., vegetation density) with real-time measurements (e.g., wind speed and direction). The system is validated on a case study in Italy, in the municipality of the city of L’Aquila, on three different fire scenarios. In normal situations, the designed waypoint-based navigation system provided an effective monitoring of the area, enabling the early detection of starting fires. The developed solution also demonstrated good performance in tracking and anticipating the fire front advance, potentially providing a better situational awareness to emergency operators and support their response policies. Both the test environment and the simulator have been made open-source. Full article

This study justifies the prospect of using aerial imagery from unmanned aerial vehicles (UAVs) for technological monitoring and operational control of municipal solid waste landfills. It presents the results of surveys (aerial imagery) of a number of Russian landfills, which were carried out using low-cost drones equipped with standard RGB cameras. In the processing of aerial photographs, both photogrammetric data processing algorithms (for constructing orthophotoplans of objects and 3D modeling) and procedures for thematic interpretation of photo images were used. Thematic interpretation was carried out based on lists of requirements for the operating landfills (the lists were compiled on the basis of current legislative acts). Thus, this article proposes framework guidelines for the complex technological monitoring of landfills using relatively simple means of remote control. It shows that compliance with most of the basic requirements for landfill operations, which are listed in both Russian and foreign regulation, can be controlled by unmanned aerial imagery. Thus, all of the main technological operations involving waste at landfills (placement, compaction, intermediate isolation) are able to be controlled remotely; as well as compliance with most of the design and planning requirements associated with the presence and serviceability of certain engineering systems and structures (collection systems for leachate and surface wastewater, etc.); and the state of the landfill body. Cases where the compliance with operating standards cannot be monitored remotely are also considered. It discusses the advantages of air imagery in comparison with space imagery (detail of images, operational efficiency), as well as in comparison with ground inspections (speed, personnel safety). It is shown that in many cases, interpreting the obtained aerial photographs for technological monitoring tasks does not require special image processing and can be performed visually. Based on the analysis of the available world experience, as well as the results of the study, it was concluded that unmanned aerial imagery has great potential for solving problems of waste landfill management. Full article

Uncooled thermal infrared sensors are increasingly being deployed on unmanned aerial systems (UAS) for agriculture, forestry, wildlife surveys, and surveillance. The acquisition of thermal data requires accurate and uniform testing of equipment to ensure precise temperature measurements. We modified an uncooled thermal infrared sensor, specifically designed for UAS remote sensing, with a proprietary external heated shutter as a calibration source. The performance of the modified thermal sensor and a standard thermal sensor (i.e., without a heated shutter) was compared under both field and temperature modulated laboratory conditions. During laboratory trials with a blackbody source at 35 °C over a 150 min testing period, the modified and unmodified thermal sensor produced temperature ranges of 34.3–35.6 °C and 33.5–36.4 °C, respectively. A laboratory experiment also included the simulation of flight conditions by introducing airflow over the thermal sensor at a rate of 4 m/s. With the blackbody source held at a constant temperature of 25 °C, the introduction of 2 min air flow resulted in a ’shock cooling’ event in both the modified and unmodified sensors, oscillating between 19–30 °C and -15–65 °C, respectively. Following the initial ‘shock cooling’ event, the modified and unmodified thermal sensor oscillated between 22–27 °C and 5–45 °C, respectively. During field trials conducted over a pine plantation, the modified thermal sensor also outperformed the unmodified sensor in a side-by-side comparison. We found that the use of a mounted heated shutter improved thermal measurements, producing more consistent accurate temperature data for thermal mapping projects. Full article

Localization and mapping technologies are of great importance for all varieties of Unmanned Aerial Vehicles (UAVs) to perform their operations. In the near future, it is planned to increase the use of micro/nano-size UAVs. Such vehicles are sometimes expendable platforms, and reuse may not be possible. Compact, mounted and low-cost cameras are preferred in these UAVs due to weight, cost and size limitations. Visual simultaneous localization and mapping (vSLAM) methods are used for providing situational awareness of micro/nano-size UAVs. Fast rotational movements that occur during flight with gimbal-free, mounted cameras cause motion blur. Above a certain level of motion blur, tracking losses exist, which causes vSLAM algorithms not to operate effectively. In this study, a novel vSLAM framework is proposed that prevents the occurrence of tracking losses in micro/nano-UAVs due to the motion blur. In the proposed framework, the blur level of the frames obtained from the platform camera is determined and the frames whose focus measure score is below the threshold are restored by specific motion-deblurring methods. The major reasons of tracking losses have been analyzed with experimental studies, and vSLAM algorithms have been made durable by our studied framework. It has been observed that our framework can prevent tracking losses at 5, 10 and 20 fps processing speeds. vSLAM algorithms continue to normal operations at those processing speeds that have not been succeeded before using standard vSLAM algorithms, which can be considered as a superiority of our study. Full article

Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods. Full article

UAV-based photogrammetry has many applications today. Measuring of snow depth using Structure-from-Motion (SfM) techniques is one of them. Determining the depth of snow is very important for a wide range of scientific research activities. In the alpine environment, this information is crucial, especially in the sphere of risk management (snow avalanches). The main aim of this study is to test the applicability of fixed-wing UAV with RTK technology in real alpine conditions to determine snow depth. The territory in West Tatras as a part of Tatra Mountains (Western Carpathians) in the northern part of Slovakia was analyzed. The study area covers more than 1.2 km² with an elevation of almost 900 m and it is characterized by frequent occurrence of snow avalanches. It was found that the use of different filtering modes (at the level point cloud generation) had no distinct (statistically significant) effect on the result. On the other hand, the significant influence of vegetation characteristics was confirmed. Determination of snow depth based on seasonal digital surface model subtraction can be affected by the process of vegetation compression. The results also point on the importance of RTK methods when mapping areas where it is not possible to place ground control points. Full article

LiDAR technology has been proven to be an effective remote sensing technique for forest inventory and management. Among existing remote sensing platforms, unmanned aerial vehicles (UAV) are rapidly gaining popularity for their capability to provide high-resolution and accurate point clouds. However, the ability of a UAV LiDAR survey to map under canopy features is determined by the degree of penetration, which in turn depends on the percentage of canopy cover. In this study, a custom-built UAV-based mobile mapping system is used for simultaneously collecting LiDAR and imagery data under different leaf cover scenarios in a forest plantation. Bare earth point cloud, digital terrain model (DTM), normalized height point cloud, and quantitative measures for single-tree inventory are derived from UAV LiDAR data. The impact of different leaf cover scenarios (leaf-off, partial leaf cover, and full leaf cover) on the quality of the products from UAV surveys is investigated. Moreover, a bottom-up individual tree localization and segmentation approach based on 2D peak detection and Voronoi diagram is proposed and compared against an existing density-based clustering algorithm. Experimental results show that point clouds from different leaf cover scenarios are in good agreement within a 1-to-10 cm range. Despite the point density of bare earth point cloud under leaf-on conditions being substantially lower than that under leaf-off conditions, the terrain models derived from the three scenarios are comparable. Once the quality of the DTMs is verified, normalized height point clouds that characterize the vertical forest structure can be generated by removing the terrain effect. Individual tree detection with an overall accuracy of 0.98 and 0.88 is achieved under leaf-off and partial leaf cover conditions, respectively. Both the proposed tree localization approach and the density-based clustering algorithm cannot detect tree trunks under full leaf cover conditions. Overall, the proposed approach outperforms the existing clustering algorithm owing to its low false positive rate, especially under leaf-on conditions. These findings suggest that the high-quality data from UAV LiDAR can effectively map the terrain and derive forest structural measures for single-tree inventories even under a partial leaf cover scenario. Full article

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring. Full article

This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results. Full article

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented. Full article