UAV Technology Research Articles

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

Research on UAV formation signal location based on two-dimensional plane analysis

In recent years, the UAV technology has developed rapidly and is widely used in military, civil and scientific research fields. In order to avoid external electromagnetic interference, the bearings-only passive positioning method is usually used to adjust the position of the UAV to maintain the formation.In this study, according to the circular formation of Uavs, the positioning model of the received signal of Uavs on the excellent arc and the positioning model of the received signal of Uavs on the inferior arc are established. The Uavs with slightly deviated positions in the circular formation are effectively adjusted, and the positioning model of Uavs with the passive receiving signal is established. In this paper, the double-station cross-positioning method is used to calculate that in the case of circular formation, four Uavs are needed to transmit signals, among which one is located in the center of the circle and the other three are distributed on the circumference to realize the effective positioning of Uavs.

Optimization of Multitask Scheduling for Swarm UAV System with Charging Platform

Swarm UAV technology have potential application in a wide range because of its ability to utilize large number, low cost and unified scheduled UAVs. Unified scheduling is tasks and resources allocation through UAV scheduling optimization, which is the key problem for swarm UAV research. Existing scheduling research mainly focuses on scheduling analysis for small-scale, short-time tasks of swarm UAVs, and rarely consider the application of charging platform. Scheduling optimization must consider the influence of charging and other factors for future UAV multi-task and long-time applications . An improved swarm UAV task scheduling method based on a unified scheduling model and improved genetic algorithms was proposed. First, the wireless charging platform resources are incorporated into the UAV working environment, and the working scenario is modeled systematically. Then, the genetic algorithm is used to optimize the task and charging platform resource assignment. Finally, the proposed method is tested and validated using simulated scenarios. Test results show that the proposed method can better adapt to changes in tasks, environment and resources, and still shows good results with relatively large swarms.

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.

Application of integrated geodetic and UAV technologies for monitoring environmental changes due to the mining activities in Solotvyno salt mine, Ukraine

This paper covers the spatio-temporal evolution of land surface deformation in the Tisza River basin within the Transcarpathian region, analyzing geodetic observations obtained over the last decade. In the town of Solotvyno, in the western part of Ukraine, near the Romanian border, there is an abandoned salt deposit where salt was extracted in several mining operations. In 2010, the mine was closed, and mine chambers 7 and 8 were flooded with water, causing the collapse of this region. Since then, the extent of the damage has been increasing, with sinkholes steadily growing larger and larger, threatening not only the entire surface area around the quarry but especially the groundwater that connects the deposit to the Tisza River. Several research institutions have been involved in monitoring the shifts over time. We have monitored the karst by identifying the most dangerous areas of the earth's surface subject to vertical shifts - trench sinkholes by UAV survey and terrestrial measurements using precise levelling in 4 stages. The situation on the site is an emergency with a major ecological impact of salt contamination in the whole Upper Tisza Basin region and requires not only continuous geodetic, geological, and chemical monitoring but especially an urgent solution to reverse the state of damage to the region.

Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm

UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG–VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG–VSC algorithm’s implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.

Assessment of New Techniques for Measuring Volume in Large Wood Chip Piles

Our work aimed to compare the chip pile volumes calculated by laser ground scanning, UAV technology, and laser ground measurement and also to determine the accuracy, speed, and economic efficiency of each method. The large chip pile was measured in seven different ways: band measurement, laser measurement with Vertex, global navigation satellite system, handheld mobile laser scanner, terrestrial laser scanner, drone, and smartphone with a light detection and ranging sensor. All the methods were compared in terms of accuracy, price, user-friendliness, and time required to obtain results. The calculated pile volume, depending on the method, varied from 2588 to 3362 m3. The most accurate results were provided by the terrestrial laser scanning method, which, however, was the most expensive and the most demanding in terms of collecting and evaluating the results. From a time and economic point of view, the most effective methods were UAVs and smartphones with LiDAR.

Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.

Wireless Online Rotation Monitoring System for UAV Motors Based on a Soft-Contact Triboelectric Nanogenerator.

Real-time monitoring of UAV motor speed is crucial for enhancing control performance and ensuring flight safety. However, this task faces challenges such as difficult sensor installation and high costs. This study introduces a wireless rotational speed sensing system based on a UAV-rotary triboelectric nanogenerator (UR-TENG). By employing a carefully designed structure with soft contact and a freestanding-triboelectric-layer mode, UR-TENG exhibits characteristics like low friction, affordability, ease of production, and self-powering capability. This eliminates the need for an external power source and addresses the complexity of installation in the limited space of UAVs. Experimental findings demonstrate that UR-TENG possesses high sensitivity and stability, maintaining the structural integrity of the UAV. The goodness of fit is notably high at 0.99959, with a maximum error rate of only 0.014 within a range of 6270 rpm. Moreover, UR-TENG integrates with a microcontroller unit (MCU) and external circuitry to form a monitoring system. This system transmits electrical signals to a PC via a Wi-Fi module, facilitating real-time rotational speed sensing and anomaly detection. Finally, a practical application demonstration on a UAV validates the adaptability of UR-TENG to complex operational environments. This study presents a promising approach for online rotation monitoring of UAV motors, with potential for commercialization, and introduces new avenues for TENG application in UAV technology.

Energy Absorption and Damage Analysis of Fragments in High-Speed Impact on Composite Honeycomb Sandwich Structures

With the continuous innovation of UAV technology, composite honeycomb sandwich structures are used more and more in the fuselage structure of UAVs, and their anti-high-speed fragment impact performance has become a concern. In order to study the influence of fragments on the damage degree of composite honeycomb structures and analyze the damage difference of different initial velocity fragments on different composite honeycomb structures, based on the numerical simulation results of high-speed impact, the energy absorption characteristics of different honeycomb sandwich structures are analyzed, and the honeycomb structure with better energy absorption is selected for 24 impact experiments. The velocity range of fragment impact is 141–368 m/s. The typical damage of the upper core layer and the lower core layer in each structure is analyzed, and the energy absorption theory is used to compare the conditions of each group. The results show that ST-3-3 has the best energy absorption characteristics. Under different velocity impacts, the energy absorption per unit volume of the ST-3-3 structure reaches 521.6~659.6 × 103 J/m3, which is about 30.6% higher than that of the same design structure. The four groups of composite honeycomb sandwich structures designed in the experiment have obvious deformation in the process of impact, except that the deformation of the bottom skin of the ST-6-6-1 structure is not obvious, and the deformation of the other three groups is more obvious with the increase in structural resistance. This research shows that the reasonable arrangement of the structure and material of the composite honeycomb sandwich can better cope with the impact of high-speed fragments and reduce the damage to the structure.

A low-cost UAV survey can unravel Baird's Tapir (Tapirus bairdii) trail network dynamics in a neotropical highland forest

UAV technology has transformed spatial data collection and high-spatial resolution analysis of diverse environments around the world. Its role within geosciences, ecology, engineering, and other sciences has been increasing withing the last decade. In ecological and geomorphological aspects, it offers a powerful tool for studying landforms, surface processes, vegetation, and animal behavior globally. Our study employs high-resolution UAV surveys to analyze Tapirus bairdii trail networks and their connection to the landscape in a Costa Rican highland paramo-forest national park. Using a DJI Phantom 4 V.2 UAV, we conducted a photogrammetric flight over 3.19 ha, producing datasets with a spatial resolution of 2.99 cm/pixel. These datasets facilitated geostatistical analysis of Tapirus bairdii trails, revealing their complexity and hotspots near lagoons. We investigated lagoon characteristics, finding that finer sediments spatially correspond with higher trail activity. This suggests that sediment types in lagoons influence Tapirus bairdii navigation, impacting trail network composition. Our findings highlight UAV technology's role in advancing ecological and geomorphological research in protected areas, providing insights into Tapirus bairdii behavior within highland ecosystems. Overall, to the date this is the first high-resolution UAV survey of Tapirus bairdii within these types of highland tropical environments.

CFD Analysis of Aerodynamic Characteristics in a Square-Shaped Swarm Formation of Four Quadcopter UAVs

The aerodynamic behavior of a square-shaped formation of four quadcopter UAVs flying in a swarm is investigated in detail through three-dimensional computer simulations utilizing Computational Fluid Dynamics (CFD) methodology. The swarm configuration comprises four UAVs positioned with two in the upper row and two in the lower row along the same propeller axes. The flow profile generated by the UAV propellers rotating at 10,000 revolutions per minute is analyzed parametrically using the Multiple Reference Frame (MRF) technique. UAVs within the swarm are positioned at 75 cm from the motion centers of adjacent propellers. This distance, the effects of horizontally and vertically positioned UAVs on each other, and the collective behavior of the swarm are thoroughly examined. Pressure, velocity, and turbulent kinetic energy values are meticulously analyzed. This research represents a milestone in understanding the aerodynamic characteristics of UAV swarms and the optimization of swarm performance. The findings highlight effective factors in swarm flights and their consequences for UAVs. Additionally, the article describes the “near-UAV phenomenon”. Furthermore, the methodology developed for CFD simulations provides an approach to analyzing close flight scenarios and evaluating their performance in various swarm configurations. These achievements contribute to the future development of UAV technology.

3D LoD2 modelling of Halong City based on UAV point cloud

3D urban building models are crucial for linking, converging, and integrating economic and social urban data. They are extensively utilized in numerous domains such as smart city development, comprehensive social management, and emergency decision-making. Advanced technologies like affordable UAV (Unmanned Aerial Systems) imagery enable a greater level of automation in data acquisition compared to traditional digital photogrammetry methods. The main objective of this research is to build a 3D LoD2 CityGML model with dense point clouds from UAV images. This study presents a completed workflow for generating 3D CityGML models of the city at LoD2 using UAV data. The use of dense point cloud data from UAV technology in the experimental area has been performed using a DJI Phantom 4 Pro. The original point clouds should be denoised using the statistical outlier remover (SOR), the main goal is to reduce noise while preserving the building's geometry. After that, the point clouds of object features were vectored for the level of detail 2 (LoD2) of the object's 3D volume corresponding to its actual height, and objects belonging to the Feature Class 3D layer will represent LoD2 on SketchUp Pro 2021 software to generate a highly accurate 3D model. The evaluation results show that the square errors calculated from the test points for the three axes X, Y, Z are 1.4 cm, 1.6 cm, 1.7 cm, respectively. Conducting research to choose the UAV photography method aims to offer an efficient and cost-effective solution, saving time and human resources, to address the 3D mapping challenges in urban areas across Vietnam.

Spatial data collection in cadastral updating in Turkey

In the cadastral studies carried out in Turkey in the last century, cadastral maps were produced using different surveying methods. It is also constantly replacing or diversifying its surveying methods with new ones. Finally, it has started to produce spatial data for cadastral parcels with the photogrammetric method using UAV technology. The expected accuracy in spatial data generation is ±7 cm. This article includes all surveying methods used in cadastral studies to date. In addition, the contribution of photogrammetric studies to the cadastral updating activities was investigated, carried out using UAV in a study area of 18 thousand ha.

Large dump critical zone of interest identification and slope failure prediction using realistic 3D numerical modelling

Dump failure is one of the most catastrophic hazards in the opencast mining industry and involves sudden and violent failures. Therefore, susceptible region identification is essential for slope failure mitigation in the large dump. This study proposed a sustainable approach for large dump critical zone of interest (CZI) identification, stability monitoring and slope failure prediction. UAV close-range photogrammetry survey and structure-from-motion (SfM) approach were used for the dump realistic 3D model reconstruction. 3D numerical modelling was employed for dump stability analysis. In this investigation, the dump critical zone, slope factor of safety (FOS), displacement and slope failure prediction were analysed using the 3D numerical modelling. The results indicate that maximum displacement was found in the critical zone I. However, the dump was stable under static conditions. Dump critical slope failure time is predicted using the inverse velocity method. The results of this investigation are reliable and establish the scope of UAV photogrammetry for realistic 3D modelling. In addition, the slope stability state and failure model were consistent with field observations. The proposed approach can be used for the mine, hill, and roadcut slopes. UAV technology and 3D modelling approaches have substantially reduced data collection time and expense.

Evolution of flight control systems and aerial photography in unmanned agricultural aircraft

The advancement of UAV technologies has enabled the automated capture of photos and videos, eliminating the need for manual intervention in flight control. (Research purpose) This research aims to conduct a retrospective analysis of the evolution offlight control systems and the development of aerial photography equipment for agricultural land, covering the period from the mid-19th century to present. (Materials and methods) A systematic literature review was conducted using the historical-analytical method. The paper examines original works by both domestic and international authors, including monographs, scientific journals, conference proceedings, museum exhibitions, photographic archives, and open-source software code. (Results and discussion) The paper identifies six distinct phases in the development of aerial photography and flight control systems. The classification is based on key innovations in camera types, control systems, and aircraft designs. Each phase highlights the predominant cameras, control systems, and aircraft utilized for agricultural applications. (Conclusions) Over the past 165 years, notable changes have occurred in aerial photography parameters, including the type of photographic material, image spatial and spectral resolution, camera weight and mounting, shutter types and their mechanisms, inertial control units, integrated GPS/GLONASS receivers, and light sensors. In terms of flight control systems for UAVs, significant developments over the last 106 years include variations in flight control types, the number offlight-stabilizing sensors, obstacle detection systems, size of the flight control units, flight modes, and takeoff/landing techniques, along with interfaces for attachments. It is anticipated that future intellectualization and miniaturization of flight control systems will not only boost UAV performance but also reduce the economic costs associated with the aerial monitoring of agricultural biological assets.

DTPBFT:A dynamic and highly trusted blockchain consensus algorithm for UAV swarm

With the continuous development of UAV technology, the application of UAV swarm in the military field is gradually becoming the focus of research around the world. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues such as a single point of failure brought by centralized management. Because of the advantages of distributed, tamper-proof, and traceability, blockchain is applied to UAV swarm to solve some of the security problems caused by centralized management. However, due to the limitations of its consensus algorithm Practical Byzantine Fault Tolerance (PBFT), its communication complexity will increase rapidly with the increase of the number of nodes, which also leads to the poor scalability of the algorithm and can only be applied to small-scale networks. To use the PBFT algorithm in large-scale networks such as UAV swarm, a dynamic and highly trusted PBFT (DTPBFT) algorithm is proposed in this paper. Firstly, a new consensus algorithm model including consensus layer and verification layer is designed. Then, a consensus node election scheme based on trust mechanism is proposed under this model. The trust degree of nodes in the blockchain network is comprehensively evaluated by selecting several representative indicators, and the weight factor of each indicator is calculated by entropy weight method. It not only reduces the communication complexity, but also ensures the reliability and dynamic update of consensus nodes. Experiments show that when the number of UAVs is 200, the consensus time of DTPBFT is 0.24 s, which indicates that this algorithm can support large-scale UAV swarm without causing communication congestion, so it has good scalability. In addition, experiments also show that DTPBFT can tolerate more than 13 malicious nodes, which improves the fault tolerance rate of PBFT.

Routeways in Sicily’s Middle Bronze Age. Remote Sensing Analysis of Internal Communication Routes through the Territory of Milena (Caltanissetta, Italy)

This research attempts to locate the pathways used during the Sicilian Bronze Age in the territory of the municipality of Milena (Caltanissetta, Italy), in a central area of the island. The study begins by analyzing settlement's superimposition from the Neolithic period through the Middle Ages. Using an interdisciplinary approach, it was possible to identify and hypothesize prehistoric routeways by combining UAV technology, remote sensing, and GIS applications. Thus, studying the ancient movement areas without new material evidence of their existence is possible. This methodological approach is a solution for identifying specific prehistoric pathways across the interior of Sicily. The settlement logic of the hinterland and mountainous areas on the island can sometimes be replicated in other parts of the island. The good results obtained allow us to use this case study as an example of best practices in identifying specific prehistoric routeways through the analysis of topographic features with combined technological methods to study other similar contexts.

Estimation of Oil Palm Tree Quantity Using Oil Palm Ecognition and SAGA GIS Based on UAV Imagery (Case Study: Dumai, Riau)

Abstract Oil palm is a plantation commodity in Indonesia that drives the growth of the national economy. The growth of oil palm is rapidly expanding every year, thus requiring efficient land monitoring strategies. Tree counting is one technique used to monitor current conditions in vast plantation areas. Utilizing UAV technology in agricultural mapping is the best option as a tool for capturing high-resolution aerial photos. The objective of this research is to automatically count the number of oil palm trees by comparing two methods and algorithms. GEOBIA (Geography-Based Image Analysis) and Deep Learning classification are methods used for automatic object counting. In this study, high-resolution aerial photo data was utilized, making object detection much more feasible. The research applied the Template Matching and Watershed Segmentation algorithm models in a sample area of 32.5 hectares divided into two blocks: Unknown-2 and E-1. It can be concluded that both methods, with their respective algorithm models, are sufficiently relevant for use in the automatic counting process of oil palm trees. This is evidenced by the accuracy test error values, which are not more than 15%, indicating that the counted oil palm tree results can be used for further data analysis.

Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs

UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.