Search Results (558)

We present a novel concept of an electromagnetic mechanism for shifting the centre of gravity (CoG) in a small unmanned aerial vehicle with four rotors (quadrotor). Shifting the CoG is essential for controlling drones in which the thrust is unbalanced (e.g., upon the failure of one of the drives). The concept presented here involves using electromagnetic coils mounted under the drone and moving permanent magnets inside a cylindrical tube. Moving the positions of the masses can be controlled by means of currents in the coils. Changing the position of the magnets relative to the arms of the drone causes a shift in the CoG, allowing for controllability even when one of the four engines is not working, and making it possible for the drone to land safely. This article describes the geometrical and mechanical relationships in the proposed system, the design and numerical calculations of the electromagnetic mechanism with coils and permanent magnets, as well as the results of a simulation of the control variant. Additionally, the practical implementation of the mechanism, from CAD modelling through the manufacturing of its elements to the final structure prepared for mounting on a quadrotor, is discussed. Full article

The use of unmanned aerial vehicles (UAVs) as mobile sensor platforms has grown significantly in recent years, including applications where drones emulate radar targets or serve as dynamic measurement systems. This paper presents a novel approach to time-synchronized UAV trajectory planning for radar environment simulation. The proposed method considers a UAV equipped with a software-defined radio (SDR) capable of reproducing the radar signature of a simulated airborne object, e.g., a high-maneuverability or high-speed aerial platform. The UAV must follow a spatial trajectory that replicates the viewing geometry—specifically, the observation angles—of the reference target as seen from a ground-based radar. The problem is formulated within a factor graph framework, enabling joint optimization of the UAV trajectory, observation geometry, and temporal synchronization constraints. While factor graphs have been extensively used in robotics and sensor fusion, their application to trajectory planning under temporal and sensing constraints remains largely unexplored. The proposed approach enables unified optimization over space and time, ensuring that the UAV reproduces the target motion as perceived by the radar, both geometrically and with appropriate signal timing. Full article

In the rapidly evolving landscape of logistics and last-mile delivery, optimizing efficiency and minimizing costs are paramount. This paper introduces a novel heuristic approach designed to enhance the efficiency of a truck-and-drone delivery system. Our method addresses the complex challenge of coordinating the movements of a truck, which serves as a mobile depot, and an unmanned aerial vehicle (UAV or drone), which performs rapid, short-distance deliveries. Our system proposes a two-step heuristic. For truck routes, we utilized the Concorde Solver to determine the optimal path, based on real-world road distances between locations in Bacău County, Romania. This data was meticulously collected and processed as a Traveling Salesman Problem (TSP) instance with precise geographical information. Concurrently, a drone is deployed for specific deliveries, with routes calculated using the Haversine formula to determine accurate distances based on geographical coordinates. A crucial aspect of our model is the integration of the drone’s limited autonomy, ensuring that each mission adheres to its operational capacity. Computational experiments conducted on a real-world dataset including 93 localities from Bacău County, Romania, demonstrate the effectiveness of the proposed two-stage heuristic. Compared to the optimal truck-only route, the hybrid truck-and-drone system achieved up to 15.59% cost reduction and 38.69% delivery time savings, depending on the drone’s speed and autonomy parameters. These results confirm that the proposed approach can substantially enhance delivery efficiency in realistic distribution scenarios. Full article

Unmanned aerial vehicles (UAVs) have emerged as a pivotal technology for enhancing the agility and resilience of future wireless networks. However, conventional optimization approaches remain predominantly reactive, relying solely on current network conditions for decision making. This proves to be inadequate for handling sudden traffic surges in dynamic environments, resulting in suboptimal service quality. To address this limitation, this paper proposes a novel joint optimization framework integrating spatiotemporal traffic prediction. This equips UAVs with predictive capabilities, thereby facilitating a paradigm shift from passive response to proactive service provision. The main contributions of this work are fourfold: First, a novel closed-loop optimization framework is introduced, deeply integrating an advanced traffic-forecasting module with a communication resource optimization module to provide a systematic, forward-looking decision-making solution for UAV-assisted communications. Second, a cellular traffic predictor based on Gaussian mixture model meta-learning (GMM-ML) is designed. This model effectively captures the periodicity and heterogeneity of traffic data, enabling the precise prediction of future hotspot areas and resolving the challenge of accurate forecasting under small-sample conditions. Third, a long-term discounted mixed-integer nonlinear programming (MINLP) problem model is formulated. This innovatively incorporates a “service readiness reward” for predicted hotspots within the objective function to achieve long-term performance optimization. Fourth, an efficient and convergent predictive iterative association and location optimization (P-IALO) algorithm is developed. Utilizing block coordinate descent and continuous convex approximation techniques, this algorithm decomposes the original complex problem to alternately optimized subproblems of user association and trajectory planning, guaranteeing algorithmic convergence. To validate the effectiveness of the proposed framework, large-scale simulation experiments were conducted using real-world traffic data. The results demonstrate that compared to traditional reactive algorithms, the proposed scheme significantly enhances the overall system throughput by 12%, improves user QoS satisfaction by 9.4%, and reduces service interruptions by 34.2%. Concurrently, the algorithm exhibits favorable convergence speed and robustness, maintaining performance advantages even under predictive errors. Extensive experimentation thoroughly demonstrates the efficacy of this research in enhancing the performance of drone-assisted networks. Full article

With China’s low-altitude economy becoming a strategic emerging industry, the rapid growth of UAV applications demands higher efficiency in low-altitude airspace utilization and safety management. However, existing studies lack unified grid division standards and refined methods to evaluate capacity for complex urban low-altitude airspace. This study is devoted to developing a methodology for determining safe distances and assessing the throughput capacity of transport systems. The work is based on a multi-criteria assessment that takes into account four significant indicators. The application of the Pareto optimization principle made it possible to identify the most effective compromise solutions. A collision probability model with random UAV(Unmanned Aerial Vehicle) headings was proposed to determine safety separations, and a grid capacity simulation model with saturation judgment and convergence verification was established. The optimal grid granularity was identified as 20 m. Safety separations for DJI M300RTK, Mavic 3Pro, and Air 3S were 104 m, 86 m, and 47 m, respectively. Saturated capacity stabilized within 106–116 s, with stable values of 1.022, 0.961, and 1.023 drones/min for the three UAV models. The results of the study contain key conclusions about traffic capacity and suggest ways to optimize it. Conclusions: This study provides a theoretical framework for airspace resource optimization and UAV path planning, offering quantifiable benchmarks to evaluate and manage urban low-altitude airspace. Full article

A growing interest in aerial drone applications has led to the European regulatory proposal 2022/0196/COD, which considers aerial spraying in steep-slope vineyards safer for human health and the environment. Nevertheless, disease control in perennial crops by aerial applications remains under-investigated. This study aims to identify suitable Plant Protection Products (PPPs) for aerial application in vineyards and analytical methods to quantify PPP deposits. A standardized protocol for controlling grapevine downy mildew was developed, testing Metalaxyl-M and copper-based fungicides’ efficacy and foliar depositions. As Italian law prohibits aerial application, an Unmanned Aerial Spray System (UASS) constrained to the ground simulated aerial spray. Leaves were sampled on predetermined days after treatment application for both fungicides’ efficacy evaluation and deposit quantification. Metalaxyl-M applied from UASS showed an efficacy comparable to ground sprays at pre- and post-flowering (≈70%), while copper efficacy from UASS was lower (≈47–63%) at each stage. Aerial sprayings resulted in higher deposits in the upper canopy, potentially explaining the lower efficacy of copper fungicides, while Metalaxyl-M’s systemicity partially compensated for the uneven vertical distribution, improving disease control. This study established a methodology for aerial PPP testing in vineyards, further studies are needed to confirm these findings across different years and locations. Full article

Unmanned aerial vehicle (UAV) vision systems have been widely deployed for aerial monitoring applications, yet small-target detection in UAV imagery remains a significant challenge due to minimal pixel representation, substantial scale variations, complex background interference, and varying illumination conditions. Existing object detection algorithms struggle to maintain high accuracy when processing small targets with fewer than 32 × 32 pixels in UAV-captured scenes, particularly in complex environments where target-background confusion is prevalent. To address these limitations, this study proposes RMH-YOLO, a refined multi-scale architecture. The model incorporates four key innovations: a Refined Feature Module (RFM) that fuses channel and spatial attention mechanisms to enhance weak feature representation of small targets while maintaining contextual integrity; a Multi-scale Focus-and-Diffuse (MFFD) network that employs a focus-diffuse transmission pathway to preserve fine-grained spatial details from high-resolution layers and propagate them to semantic features; an efficient CS-Head detection architecture that utilizes parameter-sharing convolution to enable efficient processing on embedded platforms; and an optimized loss function combining Normalized Wasserstein Distance (NWD) with InnerCIoU to improve localization accuracy for small targets. Experimental validation on the VisDrone2019 dataset demonstrates that RMH-YOLO achieves a precision and recall of 53.0% and 40.4%, representing improvements of 8.8% and 7.4% over the YOLOv8n baseline. The proposed method attains mAP50 and mAP50:95 of 42.4% and 25.7%, corresponding to enhancements of 9.2% and 6.4%, respectively, while maintaining computational efficiency with only 1.3 M parameters and 16.7 G FLOPs. Experimental results confirm that RMH-YOLO effectively improves small-target detection accuracy while maintaining computational efficiency, demonstrating its broad application potential in diverse UAV aerial monitoring scenarios. Full article

Unmanned Aerial Vehicles (UAVs), particularly multirotor drones, require rigorous structural monitoring to ensure safe and reliable operation. Visual inspections are often inefficient and may miss early signs of damage. Even when faults are detected visually, effective repair requires contextual knowledge such as past repairs, part specifications, and supplier information. This study presents an implemented and experimentally validated closed-loop Product Lifecycle Management (PLM) system that integrates vibration-based structural health monitoring (SHM) with UAV maintenance workflows. A physical quadcopter platform is utilized to collect vibration data for training and testing under eight physically induced single-fault scenarios, including damaged propellers and loosened components. Deep learning models are trained on time-domain vibration data collected from onboard sensors to learn fault patterns and are then deployed in the proposed system for real-time fault classification. The GRU (Gated Recurrent Unit) model is selected for deployment due to its superior performance and lower computational cost and is integrated with a custom-developed UAV data repository within the Aras Innovator PLM platform. Experimental validation shows that the GRU model achieves 99.26% classification accuracy and a macro F1-score of 0.9917, confirming the reliability of the vibration-based fault detection approach. This end-to-end integration enables not only real-time fault detection but also lifecycle traceability, digital documentation, and data-driven maintenance decisions. Experimental validation across test runs confirms that the proposed system accurately detects structural faults and enables automated safety protocols and maintenance workflows. The system improves inspection efficiency and demonstrates how closed-loop PLM can move beyond static documentation to actively monitor, diagnose, and manage UAV health throughout its operational lifecycle. Full article

Detection of small objects in aerial images captured by Unmanned Aerial Vehicles (UAVs) is a critical task in remote sensing. It is vital for applications like urban monitoring and disaster assessment. This task, however, is challenged by unique viewpoints, diminutive target sizes, and dense scenes. To surmount these challenges, this paper introduces the Locate then Calibrate (LTC) framework. It is a deep learning architecture designed to enhance visual perception systems, specifically for the accurate and robust detection of small objects. Our model builds upon the YOLOv8 architecture and incorporates three synergistic innovations. (1) An Efficient Multi-Scale Attention (EMA) mechanism is employed to ‘Locate’ salient targets by capturing critical cross-dimensional dependencies. (2) We propose a novel Adaptive Multi-Scale (AMS) convolution module to ‘Calibrate’ features, using dynamically learned weights to optimally fuse multi-scale information. (3) An additional high-resolution P2 detection head preserves the fine-grained details essential for localizing diminutive targets. Extensive experimental evaluations demonstrate that the proposed model substantially outperforms the YOLOv8n baseline. Notably, it achieves significant performance gains on the challenging VisDrone aerial dataset. On this dataset, the model achieves a remarkable 11.7% relative increase in mean Average Precision (mAP50). The framework also shows strong generalization. Considerable improvements are recorded on ground-level autonomous driving benchmarks such as KITTI and TT100K_mini. This validated effectiveness proves that LTC is a robust solution for high-accuracy detection: it achieves significant accuracy gains at the cost of a deliberate increase in computational GFLOPs, while maintaining a lightweight parameter count. This design choice positions LTC as a solution for edge applications where accuracy is prioritized over minimal computational cost. Full article

Resilient autonomous inter-Unmanned Aerial Vehicle (UAV) communication is critical for applications like drone swarms. While conventional Global Navigation Satellite System (GNSS)-based beamforming is effective at long ranges, it suffers from significant pointing errors at close range due to latency, low update rates and the inherent GNSS positioning error. To overcome these limitations, this paper proposes a novel hybrid beamforming system that enhances resilience by adaptively switching between two methods. For short-range operations, our system leverages Light Detection and Ranging (LiDAR)–camera sensor fusion for high-accuracy, low-latency UAV tracking, enabling precise millimeter-wave (mmWave) beamforming. For long-range scenarios beyond the camera’s detection limit, it intelligently switches to a GNSS-based method. The switching threshold is determined by considering both the sensor’s effective range and the pointing errors caused by GNSS latency and a UAV velocity. Simulations conducted in a realistic urban model demonstrate that our hybrid approach compensates for the weaknesses of each individual method. It maintains a stable, high-throughput link across a wide range of distances, achieving superior performance and resilience compared to systems relying on a single tracking method. This paves the way for advanced autonomous drone network operations in dynamic environments. Full article

In this paper, we propose a cooperative security method for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to address the scenario of unauthorized rogue drones (RDs) intruding into an airport’s restricted airspace. The proposed method integrates artificial intelligence techniques with engineering solutions to enhance the autonomy and effectiveness of air–ground cooperation in airport security. Specifically, the MADDPG algorithm enables the Security Interception UAVs (SI-UAVs) to autonomously detect and counteract RDs by optimizing their decision-making processes in a multi-agent environment. Additionally, Particle Swarm Optimization (PSO) is employed for distance-based target assignment, allowing each SI-UAV to autonomously select intruder targets based on proximity. To address the challenge of limited SI-UAV flight range, a power replenishment mechanism is introduced, where each SI-UAV automatically returns to the nearest UGV for recharging after reaching a predetermined distance. Meanwhile, UGVs perform ground patrols across different airport critical zones (e.g., runways and terminal perimeters) according to pre-designed patrol paths. The simulation results demonstrate the feasibility and effectiveness of the proposed security strategy, showing improvements in the reward function and the number of successful interceptions. This approach effectively solves the problems of target allocation and limited SI-UAV range in multi-SI-UAV-to-multi-RD scenarios, further enhancing the autonomy and efficiency of air–ground cooperation in ensuring airport security. Full article

Virtual reality (VR) creates immersive environments that allow users to interact with digital content, fostering a sense of presence and engagement comparable to real-world experiences. VR360 technology, combined with affordable head-mounted displays such as Google Cardboard, enhances accessibility and provides an intuitive learning experience. Drones, or unmanned aerial vehicles (UAVs), are operated through remote control systems and have diverse applications in civilian, commercial, and scientific domains. Taiwan’s Indigenous cultures emphasize environmental conservation, and integrating this knowledge into education supports both biodiversity and cultural preservation. The Amis people, who primarily reside along Taiwan’s eastern coast and central mountain regions, face educational challenges due to geographic isolation and socioeconomic disadvantage. This study integrates VR360 and drone technologies to develop a VR learning system for elementary science education that incorporates Amis culture and local environments. A teaching experiment was conducted to evaluate its impact on learning effectiveness and student responses. Results show that students using the VR system outperformed the control group in cultural and scientific knowledge, experienced reduced cognitive load, and reported greater learning motivation. These findings highlight the potential of VR and drone technologies to improve learning outcomes, promote environmental and cultural awareness, and reduce educational barriers for Indigenous students in remote or socioeconomically disadvantaged communities. Full article

Unmanned Aerial Vehicles are being applied in an increasing number of fields; however, their autonomous operation is associated with significant regulatory challenges. In this study, the performance of a PID and a Model Predictive Controller is compared based on the transfer function of the BLDC motor of a quadcopter using MATLAB simulations in the presence of white noise. The simulation results are used as reference values for measurements conducted on a cost-effective, custom-developed prototype drone. The prototype has been designed for short-duration hovering, allowing for an initial evaluation, but a more thorough analysis requires prolonged hovering tests to be carried out in an industrial environment. Based on the results, a recommendation is formulated for improving the PID controller to achieve performance closer to that of the MPC. The research is aimed at enhancing the energy efficiency of UAV systems and optimizing battery capacity, enabling longer autonomous flight time and more reliable control. Full article

The growing demand for fast and sustainable urban deliveries has accelerated exploration of the use of Unmanned Aerial Vehicles as viable logistics solutions for the last mile. This study investigates the integration of a distributed multi-agent system with a structured three-dimensional Urban Aerial Network (3D-UAN) for drone delivery operations. The proposed architecture models each drone as an autonomous agent operating within predefined air corridors and communication protocols. Unlike traditional approaches, which rely on simplified 2D models or centralized control systems, this research exploits a multi-layered 3D network structure combined with decentralized decision-making for improving scalability, safety, and responsiveness in complex environments. Through agent-based simulations, this study evaluates the operational performance of the proposed system under varying fleet size conditions, focusing on travel times and system scalability. Preliminary results demonstrate that the potential of this approach in supporting efficient, adaptive, resilient logistics within Urban Air Mobility frameworks depends on both the size of the fleet operating in the 3D-UAN and constraints linked to the current regulations and technological properties, such as the maximum allowed operational height. These findings contribute to ongoing efforts to define robust operational architectures and simulation methodologies for next-generation urban freight transport systems. 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