Search Results (306)

Flying remotely requires accurate perception of the environment to ensure safe operation. While remotely piloted aircraft (RPA) bring unique opportunities, they also present new challenges for the pilot, including exercising accurate depth perception. The impact of a structured training program on the improvement of depth perception skills of ab initio RPA pilots was measured. Importantly, it should be noted that such training programs are not specifically designed or intended to improve depth perception skills. Students were pre-tested prior to undergoing a training program by flying a drone away from themselves. They were required to stop and hover when they estimated the drone was over markers at three distances of 20 m, 40 m, and 60 m. After completing the 2 days of flight training, they were re-tested with the same exercise. While there was no significant improvement in distance estimation at the 20 m marker, there was significant improvement at the 40 and 60 m markers. These findings indicate that a standard, syllabus-constrained ab initio training course yields measurable gains in egocentric distance estimation beyond the action space, supporting the sufficiency of current training to transfer non-technical perceptual skills to longer VLOS ranges. Full article

It has become common to apply multiple drones to conduct plant-protection in large-scale farms, where dual-UAV synchronisation is representative. However, current studies are mainly dedicated to the spray quality of a single UAV, and it remains unclear whether synchronous operation affects spray effectiveness. This paper focuses on the spray efficacy and coupling effects of dual-UAV collaboration. Five-factor orthogonal four-level tests were conducted using the developed UAV collaboration system, and the results were compared with those of asynchronous and ideal linear superposition. It is indicated that (1) spray uniformity was impacted by the relative height between the UAVs and the flight speed of the UAVs (all the p-values < 0.02), whilst the deposition amount was affected by the relative horizontal spacing between the UAVs and the height of the left UAV relative to the forward flight direction (all the p-values < 0.04); (2) the proportion of high-quality spray in the coupling areas had a negative relation with the relative horizontal distance of the two UAVs, and the threshold of the effective coupling distance was 5 m; and (3) synchronous coupling should be avoided. If it is not, the left-side UAV (referring to the forward direction of flight) should be at a higher altitude (5 m or 6.5 m), be 0.5 m higher than the right and fly with a low or medium flight speed (3.5 m/s–4.5 m/s). The research can give a reference to the real spray operation by multiple UAVs. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

The evolution of modern warfare and civil exploration requires platforms that can operate seamlessly across the air–water interface. The folding-wing Hybrid Air and Underwater Vehicle (FUD) has emerged as a transformative solution, combining the high-speed cruising capabilities of fixed-wing aircraft with the stealth characteristics of underwater navigation. This review thoroughly analyzes the advancements and challenges in folding-wing FUD technology. The discussion is framed around four interconnected pillars: the overall design driven by morphing technology, adaptation of the propulsion system, multi-phase dynamic modeling and control, and experimental verification. The paper systematically compares existing technical pathways, including lateral and longitudinal folding mechanisms, as well as dual-use and hybrid propulsion strategies. The analysis indicates that, although significant progress has been made with prototypes demonstrating the ability to transition between air and water, core challenges persist. These challenges include underwater endurance, structural reliability under impact loads, and effective integration of the power system. Additionally, this paper explores promising application scenarios in both military and civilian domains, discussing future development trends that focus on intelligence, integration, and clustering. This review not only consolidates the current state of technology but also emphasizes the necessity for interdisciplinary approaches. By combining advanced materials, computational intelligence, and robust control systems, we can overcome existing barriers to progress. In conclusion, FUD technology is moving from conceptual validation to practical engineering applications, positioning itself to become a crucial asset in future cross-domain operations. Full article

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed. Full article

Drones are increasingly vital for disaster management, yet emergency fleets often consist of heterogeneous platforms, complicating task allocation. Efficient deployment requires rapid assignment based on vehicle and payload characteristics. This work proposes a three-step method composed of fleet analysis, area decomposition and trajectory generation for multi-rotor drone surveillance, aiming to achieve complete area coverage in minimal time while respecting no-fly zones. The three-step method generates optimized trajectories for all drones in less than 2 min, ensuring uniform precision and reduced flight distance compared to state-of-the-art methods, achieving mean distance gains of up to 9.31% with a homogeneous fleet of 10 drones. Additionally, a comparative analysis of area partitioning algorithms reveals that simplifying the geometry of the surveillance region can lead to more effective divisions and less complex trajectories. This simplification results in approximately 8.4% fewer turns, even if it slightly increases the total area to be covered. Full article

The rapid expansion of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) poses significant challenges for the integration of Unmanned Aircraft Systems (UAS) into dense urban environments, where safety risks and population exposure are particularly high. This study proposes and applies a methodology based on probabilistic assessment of both ground and air risk, grounded in the principles of safety management and the use of geospatial data from OpenStreetMap (OSM), official aeronautical charts, and digital urban models. The urban area is discretized into a spatial grid on which independent risks are calculated per cell and later combined through a cumulative probabilistic fusion model. The resulting risk estimates enable the construction of cost matrices compatible with path-search algorithms. The methodology is applied to a case study in Medellín, Colombia, connecting the Oviedo and San Diego shopping centers through Beyond Visual Line of Sight (BVLOS) operations of a DJI FlyCart 30 drone. Results show that planning with the A* algorithm produces safe routes that minimize exposure to critical areas such as hospitals and restricted air corridors, while maintaining operational efficiency metrics. This approach demonstrates a practical bridge between regulatory theory and operational practice in UAM corridor design, offering a replicable solution for risk management in urban scenarios. Full article

With the recent increase in the threat posed by unmanned aerial vehicles (UAVs) operating in environments where conventional detection systems such as radar, optical, or acoustic detection are impractical, attention is paid to methods for detecting low-flying UAVs with small radar cross-section (RCS). The most commonly used detection methods are radar detection, which is susceptible to electromagnetic (EM) interference, and optical detection, which is susceptible to weather conditions and line-of-sight. This research aims to demonstrate the possibility of using passive optical fiber Bragg grating (FBG) as a sensitive element array for low-flying UAV detection and localization. The principle is as follows: an optical signal that propagates through an optical fiber can be modulated due to the FBG reaction on the air pressure caused by a low-flying (even hovering) UAV. As a result, a small target—the DJI Avata drone can be detected and tracked via intensity surge determination. In this paper, the experimental setup of the proposed FBG-based UAV detection system, measurement results, as well as methods for analyzing UAV-caused downwash are presented. High-speed data reading and processing were achieved for low-flying drones with the possible presence of EM clutter. The proposed system has shown the ability to, on average, detect an overpassing UAV’s flight height around 85 percent and the location around 87 percent of the time. The key advantage of the proposed approach is the comparatively straightforward implementation and the ability to detect low-flying targets in the presence of EM clutter. Full article

This study advances the unmanned aerial vehicle (UAV) localization technology within the framework of a low-altitude economy, with particular emphasis on the accurate and real-time identification and tracking of unauthorized (“black-flying”) drones. Conventional YOLOv8s-based target detection algorithms often suffer from missed detections due to their reliance on single-frame features. To address this limitation, this paper proposes an improved detection algorithm that integrates a long-short-term memory (LSTM) network into the YOLOv8s framework. By incorporating time-series modeling, the LSTM module enables the retention of historical features and dynamic prediction of UAV trajectories. The loss function combines bounding box regression loss with binary cross-entropy and is optimized using the Adam algorithm to enhance training convergence. The training data distribution is validated through Monte Carlo random sampling, which improves the model’s generalization to complex scenes. Simulation results demonstrate that the proposed method significantly enhances UAV detection performance. In addition, when deployed on the RK3588-based embedded system, the method achieves a low false negative rate and exhibits robust detection capabilities, indicating strong potential for practical applications in airspace management and counter-UAV operations. Full article

Despite flies regularly visiting flowers, limited research has gone into their pollination ability on commercial crops. A national project in Australia aimed to identify fly species as potential managed pollinators for the horticultural industry and, in particular, avocado. This study investigated the ability of two calliphorids (Calliphora dubia and Calliphora vicina) and a syrphid (Eristalis tenax) fly species to pollinate Hass avocados in southwestern Australia. Four (4) field trials over three (3) years showed that each fly species (all found across Australia) was capable of pollinating Hass avocados when released into netted enclosures around multiple trees (12–26) during flowering. Trees enclosed with Eristalis tenax produced the highest fruit yield (18.0 kg/tree) outperforming trees pollinated by either C. dubia (11.6), managed honey bees in the open orchard (10.5) or C. vicina (6.8). Increasing fly numbers from 10,000 to 15,000 in the enclosures provided no additional pollination benefit. These results suggest that either E. tenax or C. dubia could be valuable managed pollinators for the avocado industry either with or without honey bees. Calliphora dubia was a significant pollinator during warmer flowering seasons and C. vicina was a useful pollinator during cold and wet flowering seasons. Full article

A primary objective of this research entails the development of an innovative algorithm capable of tracking a drone in real-time. This objective serves as a fundamental requirement across various applications, including collision avoidance, formation flying, and the interception of moving targets. Nonetheless, regardless of the efficacy of any detection algorithm, achieving 100% performance remains unattainable. Deep neural networks (DNNs) were employed to enhance this performance. To facilitate real-time operation, the DNN must be executed within a Deep Learning Processing Unit (DPU), Neural Processing Unit (NPU), Tensor Processing Unit (TPU), or Graphics Processing Unit (GPU) system on board the UAV. Given the constraints of these processing units, it may be necessary to quantify the DNN or utilize a less complex variant, resulting in an additional reduction in performance. However, precise target detection at each control step is imperative for effective flight path control. By integrating multiple algorithms, the developed system can effectively track UAVs with improved detection performance. Furthermore, this paper aims to establish a versatile Unmanned Aerial System (UAS) development platform constructed using open-source components and possessing the capability to adapt and evolve seamlessly throughout the development and post-production phases. Full article

In this paper, a hybrid flying robot that utilizes water thrust and aerial propeller actuation is proposed and analyzed, with the aim of applications in hazardous tasks in the marine field, such as firefighting, ship inspections, and search and rescue missions. For such tasks, existing solutions like drones and water-powered robots inherited fundamental limitations, making their use ineffective. For instance, drones are constrained by limited flight endurance, while water-powered robots struggle with horizontal motion due to the couplings between translational motions. The proposed hydro-aerodynamic hybrid actuation in this study addresses these significant drawbacks by utilizing water thrust for sustainable vertical propulsion and propeller-based actuation for more controllable horizontal motion. The characteristics and mathematical models of the proposed flying robots are presented in detail. A state feedback controller and a proportional–integral–derivative (PID) controller are designed and implemented in order to govern the proposed robot’s motion. In particular, a linear matrix inequality approach is also proposed for the former design so that a robust performance is ensured. Simulation studies are conducted where a purely water-powered flying robot using a nozzle rotation mechanism is deployed for comparison, to evaluate and validate the feasibility of the flying robot. Results demonstrate that the proposed system exhibits superior performance in terms of stability and tracking, even in the presence of external disturbances. Full article

The advent of large language models (LLMs), characterized by their immense scale, deep understanding of language nuances, and remarkable generative capabilities, has sparked a revolution across numerous industries and reshaped the way of machines’ comprehension of human languages. In this context, the low-altitude economy, an emerging domain that encompasses a wide spectrum of activities and services leveraging unmanned aerial vehicles (UAVs), drones, and other low-flying platforms, benefits significantly from the integration of LLMs. We developed a novel framework to explore the applications of LLMs in the low-altitude economy, outlining how these advanced models enhance aerial operations, optimize service delivery, and foster innovation in a rapidly evolving industry. Full article

With the increased proliferation of drone use for many purposes, counter drone technology has become crucial. This rapid expansion has inherently introduced significant opportunities and applications. This creates applications such as aerial surveillance, delivery services, agriculture monitoring, and, most importantly, security operations. Due to the relative simplicity of learning and operating a small-scale UAV, malicious organizations can field and use UAVs (drones) to form substantial threats. Their interception may then be hindered by evasive manoeuvres performed by the malicious UAV (mUAV). Novice operators may also unintentionally fly UAVs into restricted airspace such as civilian airports, posing a hazard to other air operations. This paper explores predictive trajectory code and methods for the neutralisation of mUAVs by following drones, using state estimation techniques such as the extended Kalman filter (EKF) and particle filter (PF). Interception strategies and optimization techniques are analysed to improve interception efficiency and robustness. The novelty introduced by this paper is the implementation of adaptive time horizon (ATH) and velocity control (VC) in the predictive process. Simulations in MATLAB were used to evaluate the effectiveness of trajectory prediction models and interception strategies against evasive manoeuvres. The tests discussed in this paper then demonstrated the following: the EKF predictive method achieved a significantly higher neutralisation rate (41%) compared to the PF method (30%) in linear trajectory scenarios, and a similar neutralisation rate of 5% in stochastic trajectory scenarios. Later, after incorporating adaptive time horizon (ATH) and 20 velocity control (VC) measures, the EKF method achieved a 98% neutralization rate, demonstrating significant improvement in performance. Full article

Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation. Full article