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EN
This paper presents a modified algorithm for determining the positioning accuracy of a UAV based on a joint GPS/EGNOS+GPS/SDCM (Global Positioning System/European Geostationary Navigation Overlay Service+Global Positioning System/ System for Differential Corrections and Monitoring) solution. Firstly, the average weighted model for determining the position of the UAV (Unmanned Aerial Vehicle) was developed. The algorithm takes into account the coordinates from the individual GPS/EGNOS and GPS/SDCM solution as well as correction coefficients that are a function of the inverse of the ionospheric VTEC (Vertical TEC) delay. Next the accuracy term was estimated in the form of the position errors and RMS (Root Mean Square) errors. Finally the Kalman filter algorithm was used for improved the position errors and RMS errors. The developed algorithm is concerned with determining the positioning accuracy of the UAV for BLh (B-Latitude, L-Longitude, h-ellipsoidal height) ellipsoidal coordinates. The algorithm was tested on kinematic GPS/SBAS (Global Positioning System/Satellite Based Augmentation System) data recorded by a GNSS (Global Navigation Satellite System) receiver placed on a DJI Matrice 300RTK type unmanned platform. As part of the research test, two flights of the UAV were performed on 16 March 2022 in Olsztyn. In the first flight, the proposed algorithm enabled an increase in UAV positioning accuracy from 4% to 57% after Kalman filter process. In the second flight, on the other hand, UAV positioning accuracy was increased from 6% to 42%. The developed algorithm enabled an increase in UAV positioning accuracy and was successfully tested in two independent flight experiments. Ultimately, further research is planned to modify the algorithm with other correction coefficients.
EN
SBAS systems are applied in precise positioning of UAV. The paper presents the results of studies on the improvement of UAV positioning with the use of the EGNOS+SDCM solutions. In particular, the article focuses on the application of the model of totaling the SBAS positioning accuracy to improve the accuracy of determining the coordinates of UAVs during the realisation of a test flight. The developed algorithm takes into account the position errors determined from the EGNOS and SDCM solutions. as well as the linear coefficients that are used in the linear combination model. The research was based on data from GPS observations and SBAS corrections from the AsteRx-m2 UAS receiver installed on a Tailsitter platform. The tests were conducted in September 2020 in northern Poland. The application of the proposed algorithm that sums up the positioning accuracy of EGNOS and SDCM allowed for the improvement of the accuracy of determining the position of the UAV by 82-87% in comparison to the application of either only EGNOS or SDCM. Apart from that, another important result of the application of the proposed algorithm was the reduction of outlier positioning errors that reduced the accuracy of the positioning of UAV when a single SBAS solution (EGNOS or SDCM) was used. The study also presents the effectiveness of the proposed algorithm in terms of calculating the accuracy of EGNOS+SDCM positioning for the weighted average model. The developed algorithm may be used in research conducted on other SBAS supporting systems.
EN
This article proposes a methodology for assessing the adequacy of mathematical models for the discharge characteristics of lithium-polymer batteries (LPABs) used in unmanned aerial vehicles (UAVs). The methodology is based on ISO 5725 standards, focusing on the evaluation of trueness and precision under repeatability and reproducibility conditions. Experimental studies were conducted to collect data on LPAB discharge behavior and surface temperature dynamics across a range of environmental conditions (-20°C to +50°C). Key analyses included systematic error evaluation, statistical variance assessment, and the formulation of precision criteria using tools such as pairwise differences, mean square deviation, and statistical tests. The results validate the developed regression and simulation models, confirming their reliability for predicting battery performance under dynamic operational conditions. The study provides a robust framework for UAV battery monitoring, enabling accurate prediction of flight duration and ensuring safe and efficient UAV operation across diverse environmental scenarios.
EN
This study explores the legal framework governing cross-border operations of Unmanned Aircraft Systems (UAS), highlighting the pressing need for a unified regulatory environment to match the rapid advancements in UAS technology. As UAS operations increasingly align with international air services, this study draws parallels between UAS governance and the regulation of airlines, which are currently authorized to provide international air services under air transport agreements. The analysis focuses on the applicability of both international law and EU regulations to UAS operations, particularly in the context of scheduled, non-scheduled, and cabotage services. It examines certification and licensing requirements under EU regulations, identifying their potential to serve as models for future international standards. Overall, this study contributes to the understanding of regulatory challenges and opportunities, emphasizing the importance of harmonized global standards for the effective integration of UAS into international aviation.
5
Content available remote Unmanned Aerial Vehicles in the road safety system
EN
The article presents the potential uses of drones in road safety systems, as predicted based on the ongoing evolution of the unmanned technologies. Requirements pertaining to users (operators) of Unmanned Aerial Vehicles (UAV) in the context of road safety are presented. The research problem was formulated based on experience and research on UAVs operating above roads, intersection and in traffic collision/accident areas. An analysis of opportunities and threats of usage of UAVs in road safety was performed. An attempt was made to identify the means of shaping legal and criminal awareness of users, along with an analysis of remedial measures that had been introduced. A concept of safe flow of UAVs in the airspace was described. An attempt was made to evaluate the potential for support of the road safety system by using specialized UAVs. The performed research indicates that usage of drones in road safety systems will enable real-time identification of traffic with very high precision (high-resolution cameras), including identification of vehicles, pedestrians and cyclists, verification of vehicle speed, determination of queue lengths at intersections, determination of traffic volume, detection of traffic violations, identification of high-risk locations and warning drivers about potential threats.
PL
W artykule przedstawiono potencjalne możliwości zastosowania dronów w systemie bezpieczeństwa ruchu drogowego (BRD), przewidywane na podstawie ewolucji technologii bezzałogowej. Zaprezentowano wymagania stawiane użytkownikom (operatorom) technologii bezzałogowych statków powietrznych (BSP) w odniesieniu do BRD. Problem badawczy sformułowano na podstawie doświadczeń oraz badań nad BSP operującymi nad drogami, skrzyżowaniami oraz w środowisku kolizji i wypadków w ruchu drogowym. Dokonano bilansu szans i zagrożeń w odniesieniu do użycia BSP w BRD. Zbadano nastroje społeczne w odniesieniu do kwestii świadomości prawnej operatów dronów. Podjęto próbę wskazania możliwości kształtowania świadomości prawnej oraz odpowiedzialności karnej wraz z analizą zaimplementowanych dotychczas środków zaradczych. Wskazano koncepcję bezpiecznego przepływu ruchu statków bezzałogowych w przestrzeni powietrznej. Podjęto próbę oceny potencjału wsparcia systemu bezpieczeństwa ruchu drogowego przez użycie wyspecjalizowanych BSP. Z przeprowadzonych badań wynika, że zastosowanie dronów w BRD umożliwi identyfikację ruchu drogowego w czasie rzeczywistym z bardzo dobrą dokładnością (kamery o wysokiej rozdzielczości), w tym identyfikację pojazdów, pieszych oraz rowerzystów, a także sprawdzanie prędkości pojazdów, określanie długości kolejek na skrzyżowaniach, pomiar natężenia ruchu, wykrywanie naruszeń przepisów ruchu drogowego oraz identyfikację miejsc niebezpiecznych czy ostrzeganie kierowców o potencjalnych zagrożeniach.
PL
Jednym z istotnych elementów eksploatacji farmy wiatrowej jest monitorowanie stanu technicznego turbin. Obecnie istnieje wiele metod pozwalających na prowadzenie ciągłego monitoringu umożliwiającego ocenę stanu technicznego turbiny, jednakże najczęściej bez pełnej informacji o powstałych uszkodzeniach. Przedstawiono prace dotyczące wyznaczenia bezpiecznej strefy operowania BSP w bezpośrednim otoczeniu turbiny wiatrowej w trakcie jej pracy. Wyznaczenie takiej strefy stanowi element przygotowania systemu monitorowania bezpośredniego stanu technicznego łopat turbiny wiatrowej w trakcie jej pracy z wykorzystaniem drona.
EN
Numerical simulations of air flow around the rotating rotor of a working wind turbine were carried out, as well as unmanned aerial vehicle (UAV) flights with recording of flight parameters for a real object. A safe operating zone for the UAV in the immediate vicinity of the wind turbine during its operation was detd. Defining such a zone was part of the prepn. of a system for monitoring the direct technical condition of wind turbine blades during operation using a drone.
PL
Rynek platform i aplikacyjnych zastosowań bezzałogowych statków powietrznych (BSP) jest obecnie jednym z najbardziej dynamicznie rozwijających się gałęzi przemysłu i usług, zarówno w odniesieniu do segmentu cywilnego jak i wojskowego. W tym artykule przedstawiamy testy jakości usług dla radiowego modułu DTC SOL8SDR wykorzystywanego na BSP do transmisji danych. Moduł ten będzie wykorzystany w systemie rozpoznania radioelektronicznego na grupie BSP.
EN
The market for platforms and applications of un- manned aerial vehicles (UAVs) is currently one of the most dynamically developing industries and services, both in the civil and military segments. In this paper, we present quality of service (QoS) tests for the SOL8SDR DTC radio module used on a UAV for data transmission. This module is planned to be used in the radioelectronic reconnaissance system of a UAV group.
PL
Niniejsza publikacja prezentuje przegląd metod detekcji oraz klasyfikacji sygnałów radiowych wykorzystywanych do komunikacji z bezzałogowymi statkami powietrznymi. Artykuł przedstawia zarówno techniki wstępnego przetwarzania sygnału, algorytmy detekcyjne, jak również wybrane sposoby klasyfikacji sygnałów, bazujące na technikach uczenia maszynowego oraz sieciach neuronowych. Dodatkowo prezentowane są architektury wybranych algorytmów detekcyjnych w ujęciu złożoności obliczeniowej oraz skuteczności detekcji pożądanych sygnałów.
EN
This publication presents an overview of detection and classification methods of radio signals used to communicate with unmanned aerial vehicle. The article presents signal pre-processing techniques, detection algorithms, as well as selected signal classification problems based on ma- chine learning techniques and neural networks. Additionally, the architectures of selected detection algorithms are presented in terms of computational complexity and effectiveness of detecting the desired signals.
EN
This paper addresses the deployment of a drone equipped with a reconfigurable intelligent surface (RIS), creating a drone relay station (DRS) to enhance the connectivity of vehicle-to-vehicle (V2V) pairs on the ground. The trajectory of the DRS is optimized to quickly reach the best location for maximizing throughput. Additionally, the presence of an interfering node is considered, and an analytical solution is derived to determine the optimal orientation of the DRS at each time step, minimizing interference to the receiver. Simulation results confirm the effectiveness of the proposed framework.
PL
Niniejszy artykuł omawia wdrożenie drona wyposażonego w rekonfigurowalną inteligentną powierzchnię (Reconfigurable Intelligent Surface, RIS) celem stworzenia dronowej stacji przekaźnikowej (Drone Relay Station, DRS), aby zwiększyć łączność między pojazdami (Vehicle-To-Vehicle, V2V) na ziemi. Trajektoria DRS jest optymalizowana w taki sposób, aby możliwie najszybciej osiągnąć położenie maksymalizujące przepustowość. Dodatkowo uwzględniana jest obecność węzła zakłócającego, a rozwiązanie analityczne jest wyprowadzone w celu określenia optymalnej orientacji DRS w każdym kroku czasowym, minimalizując zakłócenia dla odbiornika. Wyniki symulacji potwierdzają skuteczność proponowanego modelu.
EN
This research at the Wilanów Palace, Warsaw, assesses urban greenery’s cooling impacts in a cultural heritage site using remote sensing and on-site measurements, highlighting vegetation’s importance in urban climate control. The study combines soil temperature data, UAV thermal imagery, leaf area index (LAI), LiDAR, and NDVI analyses. Findings demonstrate a strong link between vegetation density and temperature: UAV land surface temperature (LST) ranged from 26.8° to 47.5°C, peaking at 72°C, while ground-based temperatures were between 19.5° and 29.2°C, lowest in dense vegetation areas. The statistical analysis confirmed significant temperature differences across vegetation types, with higher LAI areas showing lower temperatures. These results validate the cooling effect of dense vegetation, emphasizing green spaces’ significance in urban climate regulation within cultural heritage sites. The study informs sustainable urban design and conservation, underlining the critical role of vegetation in improving urban microclimates.
PL
Niniejszy artykuł przedstawia możliwości wykorzystania oteksturowanego modelu mesh utworzonego przy użyciu bezzałogowego statku powietrznego wraz z pięcioobiektywową kamerą ukośną w pomiarach sytuacyjno – wysokościowych. Metodyka polegała na bezpośrednim pomiarze wybranych elementów baz EGiB, BDOT500, GESUT, pozyskaniu współrzędnych poprzez ich wskazanie na modelu 3D i porównaniu uzyskanych wyników z uwzględnieniem podziału na grupy dokładnościowe szczegółów terenowych.
EN
This article presents the possibilities of using a textured mesh model created using an unmanned aerial vehicle with a five-lens oblique camera in situational and altitude measurements. The methodology consisted in direct measurement of selected elements of the EGiB, BDOT500, GESUT databases, obtaining coordinates by mark them on a 3D model and comparing the obtained results, taking into account the division into accuracy groups of field details.
PL
Niniejszy artykuł przedstawia możliwość zastosowania oteksturowanego modelu mesh w procesie realizacji scalenia gruntów, skupiając się na dokładności pomiaru z jego wykorzystaniem oraz możliwości jego implementacji na różnych etapach scalenia. Badania zostały przeprowadzone na czterech obrębach ewidencyjnych znajdujących się na obszarze województwa podkarpackiego które, zostały objęte postępowaniem scaleniowym. Metodyka polegała na porównaniu wyników pomiaru bezpośredniego wykonanego za pomocą odbiornika GNSS do wskazań wykonanych na modelu 3D. Pomiary zostały przeanalizowane z uwzględnieniem podziału na grupy dokładnościowe szczegółów sytuacyjnych.
EN
This article presents the possibility of using a textured mesh model in the process of land consolidation implementation, focusing on the accuracy of measurement with its use and the possibility of its implementation at different stages of consolidation. The research was carried out on four registration areas located in the area of Podkarpackie voivodeship which were included in the land consolidation proceedings. The methodology consisted in comparing the results of direct measurement made with the help of a GNSS receiver to the indications made on a 3D model. The measurements were analyzed taking into account the division into accuracy groups of situational details.
PL
Niniejszy artykuł przedstawia porównanie danych otrzymanych z trzech niezależnych nalotów, z wykorzystaniem bezzałogowych statków powietrznych. W wyniku opracowania danych utworzono dwa oteksturowane modele mesh wykonane kamerami ukośnymi oraz chmurę punktów pozyskaną za pomocą skanera LiDAR. Metodyka polegała na porównaniu współrzędnych z pomiaru bezpośredniego oraz pozyskanych z analizowanych opracowań z uwzględnieniem podziału na grupy dokładnościowe szczegółów terenowych.
EN
This article presents a comparison of data obtained from three independent raids using Unmanned Aerial Vehicles. As a result of data processing, two textured mesh models were created with oblique cameras and a point cloud acquired using a LiDAR scanner. The methodology consisted in comparing coordinates from direct measurement and those obtained from the analyzed studies, taking into account the division into accuracy groups of field details.
EN
The article presents the results of identifying dynamic models for an unmanned multi-rotor platform. Due to such an object’s highly complex mathematical model, it was decided to identify dynamic models based on experimental data. The identification concerns vertical movement parameters, but also energy consumption when performing maneuvers, which is a key factor for autonomous aircraft.
PL
W artykule zaprezentowano wyniki identyfikacji modeli dynamicznych dla bezzałogowej platformy wielowirnikowej. Ze względu na wysoce skomplikowany model matematyczny takiego obiektu zdecydowano się na zidentyfikowanie modeli dynamicznych na podstawie danych eksperymentalnych. Identyfikacja dotyczy parametrów ruchu pionowego, ale również zużycia energii podczas wykonywania manewrów, które jest kluczowym czynnikiem dla autonomicznych statków powietrznych.
EN
As a part of the marine ecosystem, seagrass plays a significant role in the coastal environment. However, due to increased threats from natural causes and anthropogenic pressures, seagrass decline will likely begin in many areas of the world. Therefore, several studies have been carried out to observe seagrass distribution to help resolve the issue. Remote sensing is often used due to its ability to achieve high accuracy when distinguishing seagrass distribution. Still, this method lacks in species classification because not all satellites and similar aerial vehicles have fine spatial resolution to distinguish distinct species of seagrass. In this study, we aim to address the issue by utilizing unmanned aerial vehicles (UAV), which are known for providing finer resolution and better imagery. Samuh Beach at Tanjung Benoa, Bali, Indonesia, was chosen as the study site location because it experiences high levels of marine tourism and anthropogenic activities. From the UAV flight mission, the images obtained were processed. The result’s accuracy was also tested with an error matrix. The species found in this study are Enhalus acoroides, Halodule pinifolia, Thalassia hemprichii, Cymodocea rotundata, and Syringodium isoetifolium, with 65% overall accuracy of the species classification map. This result indicates that UAVs can be a strong option for similar studies in the future. In addition to that, this study was able to observe the scars on the seagrass beds left by boat propeller activities from marine tourism. However, further research is needed to gain a better understanding of these objects.
EN
Timely detection of fires in the natural environment (including fires on agricultural land) is an urgent task, as their uncontrolled development can cause significant damage. Today, the main approaches to fire detection are human visual analysis of real-time video stream from unmanned aerial vehicles or satellite image analysis. The first approach does not allow automating the fire detection process and contains a human factor, and the second approach does not allow detect the fire in real time. The article is devoted to the issue of the relevance of using neural networks to recognize and detect seat of the fire based on the analysis of images obtained in real time from the cameras of small unmanned aerial vehicles. This ensures the automation of fire detection, increases the efficiency of this process, and provides a rapid response to fires occurrence, which reduces their destructive consequences. In this paper, we propose to use the convolutional neural network ResNet-152. In order to test the performance of the trained neural network model, we specifically used a limited test dataset with characteristics that differ significantly from the training and validation dataset. Thus, the trained neural network was placed in deliberately difficult working conditions. At the same time, we achieved a Precision of 84.6%, Accuracy of 91% and Recall of 97.8%.
PL
Wczesne wykrycie pożarów w środowisku naturalnym (w tym pożarów na gruntach rolnych) jest zadaniem pilnym, gdyż ich niekontrolowany rozwój może spowodować znaczne szkody. Obecnie głównymi podejściami do wykrywania pożarów jest wizualna analiza przez człowieka strumienia wideo w czasie rzeczywistym z bezzałogowych statków powietrznych lub analiza obrazu satelitarnego. Pierwsze podejście nie pozwala na automatyzację procesu wykrywania pożaru i uwzględnia czynnik ludzki, natomiast drugie podejście nie pozwala na wykrycie pożaru w czasie rzeczywistym. Artykuł poświęcony jest zagadnieniu przydatności wykorzystania sieci neuronowych do rozpoznawania i wykrywania źródła pożaru na podstawie analizy obrazów uzyskiwanych w czasie rzeczywistym z kamer małych bezzałogowych statków powietrznych. Zapewnia to automatyzację wykrywania pożaru, zwiększa efektywność tego procesu oraz zapewnia szybką reakcję na wystąpienie pożarów, co ogranicza ich niszczycielskie skutki. W artykule proponujemy wykorzystanie splotowej sieci neuronowej ResNet-152. Aby przetestować wydajność wyszkolonego modelu sieci neuronowej wykorzystaliśmy ograniczony testowy zbiór danych, którego charakterystyka znacznie różni się od zbiorów danych treningowych i walidacyjnych. Tym samym wytrenowana sieć neuronowa została poddana celowo trudnym warunkom operacyjnym. Jednocześnie uzyskano parametry "Precision" – 84.6%, "Accuracy" – 91% i "Recall" – 97.8%.
EN
Measurements using drones have enabled significant changes in the inventorying and monitoring of mining areas. Drone-based measurements can be faster and more accurate [Mazurek 2018]. Aerial photographs taken with drones allow the surveying department in mines to accurately represent the photographed terrain and make precise measurements, which can be used, among other things, to calculate the volume of mass. The aim of the article is to present the results of research on the automated process of acquiring and processing photogrammetric data for the purpose of calculating mass volumes. As part of the research, an algorithm based on classical methods and deep learning was developed. In collaboration with the Silesian University of Technology and 3D Format company from Gliwice, the AGH University of Krakow has developed a system for automated volumetric measurements based on low-altitude photogrammetry using non-metric photos and artificial intelligence (AI) algorithms to provide cyclical volume measurement services on the Polish market. The idea of the system is to acquire data automatically, then provide the data in the cloud, maximize measurement automation, and provide results in near real-time. The entire process is to be conducted using software available through the website. The project was divided into several stages. This particular publication focuses on the automation of the measurement of surveying points.
PL
W pracy przedstawiono przegląd oprogramowania dedykowanego dla autonomicznych platform mobilnych takich jak bezzałogowe systemy powietrzne (BSP). Praca obejmuje porównanie najpopularniejszych produktów dostępnych na rynku autonomicznych platform mobilnych. Dokonano analizy oprogramowania pod kątem zastosowania, posiadanych możliwości, obsługi, poziomu personalizacji i kompatybilności z innymi systemami.
EN
The paper presents an overview of software dedicated to autonomous mobile platforms such as unmanned aerial systems (UAVs). The paper includes a comparison of the most popular products available on the market of autonomous mobile platforms. The software was analyzed in terms of application, capabilities, operation, level of personalization and compatibility with other systems.
EN
The increasing use of drone technology to produce high-resolution digital imagery and elevation models has been associated with a growing interest in developing quantitative morphometric analysis (QMA). QMA analysis is an invaluable part of creating detailed topographic models in landslide scars that are still highly unstable and prone to erosion. This paper presents the results of a research that aims to create a topographic model in a landslide scarred area where the slope configuration is still varied. The study area was located in the landscape of the Cretaceous-Tertiary volcanic transition where many landslides have occurred. Three landslides were selected on the basis of different soil material characteristics that affect the topographic condition of the landslide scar. Aerial photography was recorded using a UAV with a flying height of 80 m, with an orthomosaic resolution of 1 cm. In detail, three morphometric variables (slope, plan curvature, topographic position index) were selected and calculated with the output evaluated based on visual-spatial interpretation. The results showed that morphometric variables performed well in modeling land surface topography. Steep slopes and surfaces with convex curvature are abundant at the ledges and landslide heads that allow water runoff to disperse as the initiation of gully erosion. The multidimensional gully erosion network is concentrated at relatively low elevations and surfaces with concave curvature. The undulating micro-relief of the land surface as a result of the process of material disposition builds up on each other to a gentle slope. Finally, the topographic model of the landslide surface can be used as a base material in implementation of both physical and vegetative land conservation strategies.
EN
Macroplastics are a global threat to the aquatic environment and will degrade into microplastics over time. Its presence in canal causes pollution and inhibits water flow, causing flooding in urban areas; therefore, it is essential to identify and monitor its presence. Addressing knowledge gaps is critical in determining solutions for mitigation purposes. In visual object detection studies, aerial mapping is developed with advanced technology, such as unmanned aerial vehicles (UAV). This research aims to conduct aerial mapping experiments to find the right formula or technical reference for detecting macroplastic waste objects floating on the surface of the canal, including flight altitude, exposure to sunlight, and the influence of season on object detection. Aerial mapping will be done in densely populated urban canals in Southeast Asia, Indonesia, and Makassar City. The aerial mapping survey method will be used, and then the data will be processed in the digitization process and object detection with GIS. The analysis kernel in GIS tools will be used to see the distribution density of macroplastics. The research results show that autoblock occurs at heights of 5m and 10m, but this autoblock can be minimized at a flight height of 15 m. Apart from that, height also affects flight duration. The lower flying height will result in better visual accuracy and better resolution. However, at a height of 15m, macroplastic objects were still detected on a moderate scale. This research successfully distinguished various plastic waste materials, the most found being the soft polyolefin category in plastic bags. Monitoring results detected 321 items of macroplastics in the dry season and 1,163 in the rainy season, or a threefold increase with conditions spread thinly in the dry season. In the rainy season, they gather densely on one side of the canal.
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