Unmanned Aircraft Systems Research Articles

The Challenges of Blood Sample Delivery via Drones in Urban Environment: A Feasibility Study through Specific Operation Risk Assessment Methodology

In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply with technological constraints such as range, speed and capacity, while minimizing potential risks. In this paper, the feasibility of a drone operation dedicated to the transport of blood samples in an urban area is studied through a safety risk analysis. The assessment utilizes the Specific Operation Risk Assessment (SORA) framework, in line with current European regulations, and extends it to define flight trajectories with minimal risk. A case study in the Helsinki urban area is used as a reference, with an exemplary case of commercial drone transportation of blood samples between the Töölö and Malmi Hospitals. By leveraging the drone performance capabilities and minimizing the risk for people on the ground, this approach demonstrates that medical delivery using drones in densely populated urban environments remains challenging. Nonetheless, it argues that the proposed method can enhance risk awareness and support the planning of feasible operations.

Probabilistic Higher-Order Velocity Obstacle for Dynamic Collision Avoidance in Uncertain Environments

Autonomous systems like unmanned aircraft systems are touted as front-runners in terms of efficiency and agility in challenging operations like emergency response. The ability of autonomous agents in such systems to avoid collisions with dynamic obstacles while navigating through an obstacle-cluttered environment is critical for success in any mission. In addition to that, further challenges are posed by environments with uncertainties in obstacles’ motion, sensing, and occluded regions. To this end, a higher-order velocity obstacle–based novel motion planner is presented in this paper in a probabilistic setup for smooth, collision-free navigation of the agent with acceleration constraints in uncertain, unstructured environments with an element of anticipation for the future environment. The effectiveness of the developed algorithm for safe, collision-free, and smooth navigation of the agent is investigated in simulation studies in two different kinds of environments, one with known trajectories of obstacles and the other with unknown maneuvering trajectories of obstacles. Extensive simulation studies are performed in the presence of dynamic obstacles to elucidate the performance of the proposed algorithm on four crucial parameters—mission time, computational time, minimum obstacle distance, and an overall control effort under varied obstacle densities. With satisfactory performance in all these aspects, the developed algorithm possesses strong potential for real-time implementation.

THE IMPACT OF STRESS-ASSOCIATED DISEASES OF THE DIGESTIVE ORGANS ON THE PSYCHOPHYSIOLOGICAL STATE OF OPERATORS OF UNMANNED AIRCRAFT SYSTEMS

Introduction. In the conditions of martial law, an important role belongs to modern military equipment, which includes unmanned aircraft systems (UAS). Оperators of UAS in the course of combat missions are significantly affected by the stress factor. It provokes the occurrence of stress-associated diseases in them, therefore, studying the specifics of the impact of stress-associated diseases on the psychophysiological state of operators determines the relevance of this study. The purpose of the study. To establish the patterns of influence of stress-associated diseases of the digestive organs on the psychophysiological state of the operators of unmanned aircraft systems. Materials and methods. In the course of the research, 94 operators of UAS under the age of 40 were examined, who were then divided into groups based on their health status. The examination was carried out using modern modified methods implemented on the basis of the software and hardware complex for psychophysiological research «PFI-2». The results. It has been proven that stress-associated diseases of the digestive organs significantly worsen (p<0,05) the psychophysiological state of operators of UAS in a sufficiently large number of professionally important psychophysiological qualities. The reliable influence of the «state of health» factor on the indicators of simple and complex visual-motor reaction of operators of UAS is shown. The analysis of correlation constellations according to cognitive parameters showed that 6 of them are observed in healthy operators, 2 in mildly ill patients, and 1 in severely ill patients, that is, the system of connections between cognitive functions breaks down quite strongly with the development of stress-associated diseases of the digestive organs. There is a natural decrease in the number of connections in the series «healthy (13) – mildly ill (10) – severely ill (5)». It is shown that the stress that causes the disease fundamentally destroys the system of ensuring professional activity. Conclusions. It was established that stress-associated diseases of the digestive organs negatively affect the psychophysiological state of operators. The degree of degradation of the structure of correlation relations (р=0,0491) as a result of the influence of the stress factor on the operators was revealed. A hypothesis regarding the destruction of the system of connections between cognitive psychophysiological parameters depending on the «severity of the disease» is proposed. The need to develop a set of measures to prevent the development of stress-related diseases among operators of UAS has been established.

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models – Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) – under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond

Implementation of a UAV-aided calibration method for a mobile dual-polarization weather radar

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.

UAV Detect and Avoid from UTM-Dependent Surveillance

A hierarchical unmanned aircraft system (UAS) traffic management (UTM) system has deployed 45 ground transceiver stations (GTS) for UAS services in Taiwan. This UTM system covers most areas for UAV-dependent surveillance using ADS-B Like technology. UTM Controller can monitor all UAV flights under transparent surveillance in low airspace. Controller-initiated UAV “detect and avoid” (DAA) mechanism assists UAV separation to ensure flight safety on UTM for small multirotor UAVs. From similar concept to traffic alert and collision avoidance system (TCAS) for the manned aircraft system, the UTM software executes DAA functions to generate approach alerts to UTM Controller. Conflict is detected by heading arrow extrapolation from multiple approaching UAVs by their time to conflict (TTC) on icons. Traffic advisory (TA) and resolution advisory (RA) are pronounced on UTM console to controllers. The less priority UAV pilot will receive the controller-pilot communication (CPC) to perform avoidance resolution. In UTM, the surveillance data period is broadcasting at 5~8 seconds on LoRa (long-range wide-area network) chip. Referring to TA=48 seconds and RA=24 seconds, the signal delay in ADS-B Like system to UTM server is about 0.5 seconds and CPC response is measured about 3~5 seconds. From real flight tests, the RA is enough for the less priority pilot to maneuver UAV for avoidance. From real flight tests, the proposed DAA mechanism based on UTM-dependent surveillance is feasible to resolve multiple approaches. The developing UTM system using ADS-B Like technology is also examined of high availability with redundant reliability and performance stability for flight safety.

Evidence of the potential benefits of digital technology integration in Asian agronomy and forestry: A systematic review

CONTEXTThe agronomy–livestock–forestry nexus is critical as it influences ecosystem services, food production, and land use. Asia, a diverse and technologically dynamic region, faces disparities in agronomy tech adoption. Studies emphasize agronomy and forestry's pivotal role in determining livelihoods in the region. Challenges like rising food demand, land scarcity, climate change, and biodiversity loss require innovative solutions. Digital technologies offer enhanced productivity, sustainability, and resource management opportunities, defining the era of smart agriculture and forestry. OBJECTIVESThis work presents a Systematic Literature Review (SLR), which examines the potential for using digital technology in agronomy and forestry across Asian countries and evaluates evidence of the potential benefits for practitioners and the environment. METHODSThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method guided the choice of relevant publications. Of the 375 papers located in Scopus and Web of Science (WoS) databases, only 24 were considered relevant to the research questions. RESULTS AND CONCLUSIONSIn the agronomy and forestry sectors in Asia, the adoption of digital technologies has the potential to contribute toward the promotion of biodiversity, the preservation of ecosystem services, improved labor efficiency, risk reduction, and the promotion of climate change resilience. Geospatial tools, modeling tools, decision support systems, Unmanned Aircraft Systems (UAS), and the Internet of Things (IoT) have emerged as prominent technologies driving these positive outcomes. The study lists evidence from various articles supporting these benefits, although most demonstrate indirect causation. This underscores the need for more direct experiments to establish the broader contribution of digital technologies as well as provide evidence showing farmer uptake and the potential negative impacts of implementation. SIGNIFICANCEThe findings from this study offer a comprehensive view of the potential use of digital technologies for agronomy and forestry in Asia, as well as evidence of their potential benefits. It gives stakeholders valuable information on digital technologies and provides a springboard for future studies regarding the application of digital technology in agronomy, animal husbandry, and forestry.

NAVIGATOR PREPARATION DURING SPRAYING BY UAVS

The article presents the results of research on the implementation of navigator preparation duringagricultural spraying, conditioning the safe and precise performance of the task. This assumptionwas confirmed during spraying with unmanned aerial vehicles carried out in 2021–2023. Theexperience gained and conclusions enabled the development of a concept of initial and directnavigational preparation for unmanned aircraft systems performing spraying. It is also possibleto use this algorithm to perform deactivation by drones. The results obtained from the conductedaerial tests (within two scenarios: agricultural spraying and decontamination) have also shown thatthe risk analysis methodology recommended by EASA (SORA) entails a number of limitations andcannot be applied during spraying as it causes gaps in safety management. Therefore, a proprietaryEPEP risk analysis method has been developed specifically for spraying, which allows the pilot,the operator of an unmanned aircraft, to perform a risk analysis and manage it accordinglyimmediately before performing the task, compensating or minimizing the hazards that should beat an acceptable level before spraying.

New Concept of Smart UAS-GCP: A Tool for Precise Positioning in Remote-Sensing Applications

Today, ground control points (GCPs) represent indispensable tools for products’ georeferencing in all the techniques concerning remote sensing (RS), particularly in monitoring activities from unmanned aircraft system (UAS) platforms. This work introduces an innovative tool, smart GCPs, which combines different georeferencing procedures, offering a range of advantages. It can serve three fundamental purposes concurrently: (1) as a drone takeoff platform; (2) as a base station, allowing the acquisition of raw global navigation satellite system (GNSS) data for post-processed kinematic (PPK) surveys or by providing real-time GNSS corrections for precision positioning; (3) as a rover in the network real-time kinematic (NRTK) mode, establishing its position in real time with centimetric precision. The prototype has undergone testing in a dedicated study area, yielding good results for all three geodetic correction techniques: PPK, RTK, and GCP, achieving centimeter-level accuracy. Nowadays, this versatile prototype represents a unique external instrument, which is also easily transportable and able to connect to the GNSS RING network, obtaining real-time positioning corrections for a wide range of applications that require precise positioning. This capability is essential for environmental applications that require a multitemporal UAS-based study. When the real-time RING data are accessible to the scientific community operating in RS surveying, this work could be a helpful guide for researchers approaching such investigations.

Design methodology and simulation analysis of hybrid fuel cell and battery systems for powering unmanned aircraft systems

Hybrid fuel cell and battery systems are currently among the most promising options for increasing the flight autonomy of unmanned aircraft systems. The ratio between the size of the battery and the fuel cell is a delicate issue in power system design. Both devices are necessary; the battery, with a quick response, to respond to demand peaks, and the fuel cell, with a slow dynamic response, but greater energy density, to increase its autonomy. One of the most important tasks in designing the hybrid system is selecting the appropriate size of each component so that it can respond to peak demands, extend its autonomy as far as possible and reduce its weight.The purpose of this research was to establish a design procedure for hybrid power systems for unmanned aerial systems, which means selecting the best size for each component. The hybrid system's modeling and the dynamic simulation of its response to demand curves are the foundation of this procedure. The simulation results include the consumption of the fuel cell as a function of time, the battery’s charge status and an estimate of its autonomy for each demand curve. The analysis of the simulation results guides the choice of component size, adapting each one to achieve minimum fuel consumption for the characteristic demand curves for which the unmanned aircraft system is designed. After modeling the components of the hybrid system and describing the simulation procedure, this contribution proposes an example of a design for an unmanned aircraft system with a maximum takeoff weight of 20 kg. In this case, the most appropriate size for the fuel cell was 2000 W and a 3000 mAh 10S Lithium-Polymer battery. Fitted with a 12-liter hydrogen tank, stored at a maximum pressure of 300 bar, an estimated autonomy of 1.5 h can be expected.

Enhancing sustainable urban air transportation: Low-noise UAS flight planning using noise assessment simulator

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation.

Live Location Sharing System (L2S2): Empowering Management of Unmanned Aircraft Systems (UAS) Operations

Transparency and confidence-building measures in Unmanned Aircraft Systems (UAS) operations continue to be important topics of discussion in various contexts. Transparency helps build trust and alleviate concerns about the use of UAS, especially in sensitive areas or situations where privacy and security are paramount. Meanwhile, confidence-building measures aim to establish trust and mitigate potential risks associated with UAS operations, particularly in complex UAS operations involving multiple teams. These measures include implementing systems that enable the identification and tracking of UAS operations activities that can be achieved through technologies such as global positioning system (GPS)-based tracking systems. Although there are several UAS operations GPS-based management systems available in the market, it requires a certain initial investment cost that creates a barrier for some parties, particularly smaller organisations or those with limited budgets. Therefore, in this study, we have developed a web-based application called as Live Location Sharing System (L2S2) by leveraging the capabilities of Traccar, a free and open-source GPS tracking system. Through this system, the movement activities of the Malaysian Space Agency (MYSA) UAS operation teams can be monitored in real-time by the mission commander, which has finally empowered the management of UAS operations as a whole.

Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies

This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting, utilizing a UAS to capture high-resolution imagery, which was subsequently processed to generate detailed orthomosaics of road surfaces. This study critically analyzed the discrepancies between traditional field measurements and UAS-derived data in pavement condition assessment. The study findings demonstrate that photogrammetry-derived data from UAS offer at least similar or, in some cases, improved information on the collection of a comprehensive state of roadways, particularly in local and collector roads. Furthermore, this study proposed key modifications to the existing methodologies, including dividing the road network into segments for more precise and relevant data collection. These enhancements aim to address the limitations of current practices in capturing the diverse and dynamic conditions of urban infrastructure. Integrating UAS technology improves the measurement of pavement condition assessments and offers a more efficient, cost-effective, and scalable approach to urban infrastructure management. The implications of this study are significant for urban planners and policymakers, providing a robust framework for future infrastructure assessment and maintenance strategies.

Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV’s performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter’s presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.

Brandt's vole hole detection and counting method based on deep learning and unmanned aircraft system.

Rodents are essential to the balance of the grassland ecosystem, but their population outbreak can cause major economic and ecological damage. Rodent monitoring is crucial for its scientific management, but traditional methods heavily depend on manual labor and are difficult to be carried out on a large scale. In this study, we used UAS to collect high-resolution RGB images of steppes in Inner Mongolia, China in the spring, and used various object detection algorithms to identify the holes of Brandt's vole (Lasiopodomys brandtii). Optimizing the model by adjusting evaluation metrics, specifically, replacing classification strategy metrics such as precision, recall, and F1 score with regression strategy-related metrics FPPI, MR, and MAPE to determine the optimal threshold parameters for IOU and confidence. Then, we mapped the distribution of vole holes in the study area using position data derived from the optimized model. Results showed that the best resolution of UAS acquisition was 0.4cm pixel-1, and the improved labeling method improved the detection accuracy of the model. The FCOS model had the highest comprehensive evaluation, and an R2 of 0.9106, RMSE of 5.5909, and MAPE of 8.27%. The final accuracy of vole hole counting in the stitched orthophoto was 90.20%. Our work has demonstrated that UAS was able to accurately estimate the population of grassland rodents at an appropriate resolution. Given that the population distribution we focus on is important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring rodent damage across broad landscapes for studies of grassland ecological balance, vegetation conservation, and land management.

Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

Current approaches in UAV Operational Risk Assessment and Practical Considerations

The expansion of Unmanned Aircraft Systems (UAS) is creating new markets, particularly in Urban Air Mobility (UAM), which presents unique challenges. Beyond the risk of mid-air collisions, UAM services in urban areas introduce ground risks to buildings, traffic routes, and pedestrians. This research explores trends in Ground Risk models for different operation planning stages: strategic, pre-tactical, and tactical. It offers a logical pipeline for UAS operators, emphasizing detailed analysis and data source considerations, along with the importance of model interoperability. Using a Naples case study, the research provides practical steps for national authorities to streamline the UAS authorization process.

Collision Avoidance Capabilities in High-Density Airspace Using the Universal Access Transceiver ADS-B Messages

The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.

Sustainable Urban Drone Operations: FF2020 view

Experts worldwide agree that development of operations of Unmanned Aircraft Systems (UAS, commonly known as drones) above populated areas, to remain socially acceptable, must satisfy a set of crucial criteria: safety, security, environmental sustainability (including noise and pollution reduction) and compliance with legal requirements (encompassing liability, privacy and alignment with urban land use regulations). However, for both commercial organisations and entities carrying flights in the public interest, these operations need to also be economically sustainable. This article considers two illustrative scenarios: (1) the urgent transport of medical equipment (e.g., defibrillators) on an ad-hoc basis, precisely when and where required, rather than on a routine schedule; (2) security or plant safety applications, where a drone remains on standby, recharging its batteries in a designated ‘nest,’ ready to take-off for capturing imagery or sensory data when triggered by security personnel or by automatic alerts (e.g., a camera detecting an individual penetrating a perimeter). Deploying one remote pilot for each ‘sleeping’ drone 24/7 would lead to enormous service costs, difficult to be sustained by any organisation. The drone hence needs to be completely autonomous, meaning that a remote pilot would no longer be necessary. The concept, often referred to as ‘Drones in the Box,’ is technically feasible and currently exists. Organisation of the operations, however, goes beyond technical feasibility. According to the Automation Concept by the Joint Authorities for Rulemaking on Unmanned Systems (JARUS), this falls under level 5 of automation. In this highest level, there is neither involvement of a remote pilot in aircraft flight functions, both on the ground and in the air, nor any human awareness of dynamic operational parameters. In essence, to ensure economic sustainability, the 24/7 operations must entirely eliminate the need for remote pilots. Nonetheless, a UAS operator would still exist as the legal entity responsible for organising and overseeing operations, securing necessary approvals and being accountable for the process. Therefore, a professional job profile would still be necessary, to possibly trigger the flight, receive the collected information and act upon or activate the Emergency Response Plan (ERP) when required. This professional would neither need to be trained nor licenced as Remote Pilot but the position should be 24/7. In scenarios such as the two mentioned examples, security personnel may already be on duty 24/7 to safeguard certain facilities (e.g., hospitals) or provide emergency medical services (e.g., 118 in Italy). These operations could thus become economically viable if these personnel were trained by the operator for this on-demand duty. The Flying Forward 2020 (FF2020) project has coined this role as the ‘Fleet Manager’ and intends to standardise it through ISO (International Organisation for Standardisation), for global harmonisation.

The Mapping of Alpha-Emitting Radionuclides in the Environment Using an Unmanned Aircraft System

The protection of first responders from radioactive contamination with alpha emitters that may result from a radiological accident is of great complexity due to the short range of alpha particles in the air of a few centimeters. To overcome this issue, for the first time, a system mounted on a UAS for the near-real-time remote measurement of alpha particles has been developed, tested, and calibrated. The new system, based on an optical system adapted to be installed on a UAS in order to measure the UV-C fluorescence emitted by alpha particles in the air, has been tested and calibrated, carried out in the laboratory and in field experiments using UV-C LEDs and 241Am sources. In experimental flights, the probability of detecting a point source was determined to be approximately 60%. In the case of a surface extended source, a detection efficiency per unit surface activity of 10 counts per second per MBq cm−2 was calculated. A background count rate of UV-C of around 26 ± 28 s−1 for an integration time of 0.1 s was measured during flights, which led to a decision threshold surface activity of 5 MBq cm−2.