Search Results (273)

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption. Full article

Small multi-rotor UAVs face endurance limitations during long-range missions due to high rotor energy consumption and limited battery capacity. This paper proposes a folding-wing range extender integrating a sliding-rotating two-degree-of-freedom folding wing—which, when deployed, quadruples the fuselage length yet folds within its profile—and a tail-thrust propeller. The device can be rapidly installed on host small multi-rotor UAVs. During cruise, it utilizes wing unloading and incoming horizontal airflow to reduce rotor power consumption, significantly extending range while minimally impacting portability, operational convenience, and maneuverability. To evaluate its performance, a 1-kg-class quadrotor test platform and matching folding-wing extender were developed. An energy consumption model was established using Blade Element Momentum Theory, followed by simulation analysis of three flight conditions. Results show that after installation, the required rotor power decreases substantially with increasing speed, while total system power growth slows noticeably. Although the added weight and drag increase low-speed power consumption, net range extension emerges near 15 m/s and intensifies with speed. Subsequent parametric sensitivity analysis and mission profile analysis indicate that weight reduction and aerodynamic optimization can effectively enhance the device’s performance. Furthermore, computational fluid dynamics (CFD) analysis confirms the effectiveness of the dihedral wing design in mitigating mutual interference between the rotor and the wing. Flight tests covering five conditions validated the extender’s effectiveness, demonstrating at 20 m/s cruise: 20% reduction in total power, 25% improvement in endurance/range, 34% lower specific power, and 52% higher equivalent lift-to-drag ratio compared to the baseline UAV. Full article

The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame to generate stabilizing gravitational torques by the closed-loop feedback from the inertial measurement unit (IMU). Two major experiments were conducted to evaluate the feasibility of the system. In a controlled roll test with varying payloads, the device produced a corrective torque up to 1.2375 N·m, reducing maximum roll deviations from nearly 90° without the device to less than 5° with it. In a dynamic free-fall simulation, the baseline UAV exhibited rapid tumbling and inverted impacts, whereas with the CoM system activated, the UAV maintained a near-level attitude to achieve the upright recovery and greatly reduced structural stress prior to ground contact. The CoM device, as a fail-safe stabilizer, can also enhance maneuverability by increasing control authority, enable a faster speed response and more efficient in-air braking without reliance on the rotor thrust, and achieve comprehensive energy saving, at about 7% of the total power budget. In summary, the roll stabilization and free-fall results show that the CoM device can work as a practical pathway toward the safer, more agile, and energy-efficient UAV platforms for civil, industrial, and defense applications. Full article

Variations in flight trajectory and velocity during vertical takeoff, transition, and level flight cause substantial changes in the relative inflow vector of multi-rotor unmanned aerial vehicles (UAVs). In urban environments, disturbances from complex wind fields further increase the uncertainty of inflow conditions. This study investigates the aerodynamic characteristics of a fixed-pitch small-sized UAV rotor under varying inflow angles, velocities, and rotational speeds using a subsonic return-flow wind tunnel. The experimental setup enables inflow angle control from −90° to +90° via a turntable. Results indicate that, without incoming flow, the axial thrust and torque coefficients remain nearly constant. With inflow, both coefficients become highly sensitive to velocity in the 2000–5000 rpm range, with deviations up to four times those under static conditions. The in-plane lateral force along the X-axis increases significantly with inflow velocity, reaching about half the axial force, while the Y-axis component is minor and negligible under symmetric configurations. Pitching and rolling moments increase rapidly once inflow velocity exceeds 8 m/s, surpassing the axial torque and exhibiting strong directional asymmetry around ±15° inflow angles. The results demonstrate coupled aerodynamic force and moment behavior of small rotors under complex inflow, providing experimental evidence for improved dynamic modeling, control design, and the energy optimization of UAVs operating in turbulent wind environments. 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

Multirotor Unmanned Aerial Vehicles (UAVs), characterized by their inherently symmetrical propulsion configurations, are increasingly applied across diverse domains, yet their endurance remains fundamentally constrained by the high energy demand of flight. Autonomous docking and charging systems have emerged as practical solutions, enabling UAVs to recharge or replace batteries without human intervention. This paper provides a structured review of current approaches, offering a systematic categorization of UAV docking platforms into fixed and mobile systems, followed by an analysis of positioning and landing strategies, charging mechanisms, and modular docking concepts. Advances in vision-based guidance and sensor fusion are highlighted as key enablers of precise and reliable autonomous recovery. Contact-based charging and wireless power transfer are compared, with their benefits and limitations outlined. In addition to charging solutions, the paper presents a dedicated review of mechanisms that enable automated battery swapping, increasingly recognized as a complementary pathway to extend mission duration. By synthesizing state-of-the-art research and implementations, this study identifies key technological trends, persisting challenges, and future directions toward scalable, fully autonomous ecosystems capable of long-duration operations. Full article

This paper introduces a structured methodology for bridging the gap between theoretical modeling and high-fidelity simulation of multirotor Unmanned Aerial Systems (UAS) through the construction of digital twins in PX4 v1.12 Software-in-the-Loop (SITL) environments. A key challenge addressed is the absence of standardized procedures for translating physical UAV characteristics into simulation-ready parameters, which often results in inconsistencies between virtual and real-world behavior. To overcome this, we propose a hybrid parametric identification pipeline that combines analytical modeling with experimental characterization. Critical parameters—such as inertial properties, thrust and torque coefficients, drag factors, and motor response profiles—are obtained through a combination of physical measurements and theoretical derivation. The proposed methodology is demonstrated on a custom-built heavy-lift quadrotor, and the resulting digital twin is validated by executing autonomous missions and comparing simulated outputs against flight logs from real-world tests. Full article

Polarimetric radar offers strong potential for UAV detection, but time-varying polarization induced by rotor rotation leads to unstable echoes, degrading feature consistency and recognition accuracy. This paper proposes a unified framework that combines rotor phase compensation, adaptive polarization filtering, and a multi-branch polarization aware fusion network (MPAF-Net) to enhance micro-Doppler features. The compensation scheme improves harmonic visibility through rotation-angle-based phase alignment and polarization optimization, while MPAF-Net exploits complementary information across polarimetric channels for robust classification. The framework is validated on both simulated and measured UAV radar data under varying SNR conditions. Results show an average harmonic SNR gain of approximately 1.2 dB and substantial improvements in recognition accuracy: at 0 dB, the proposed method achieves 66.7% accuracy, about 10% higher than Pauli and Sinclair decompositions, and at 20 dB, it reaches 97.2%. These findings confirm the effectiveness of the proposed approach for UAV identification in challenging radar environments. Full article

The operational performance of robotic arms for multi-rotor flying robots (MFRs) has attracted growing attention in recent years. To explore new possibilities for aerial manipulation, this study investigates a novel parallel mechanism, the TriVariant, comprising one UP limb and two identical UPS limbs (2-UPS&UP). To evaluate its potential, we analyze its dimensional and kinematic characteristics and benchmark them against the widely adopted Delta robot, which is commonly integrated with unmanned aerial vehicles (UAVs). A prototype of the TriVariant is fabricated for experimental validation. Both analytical and experimental results reveal that, within a cylindrical task workspace characterized by a large diameter and moderate height, the TriVariant offers a more compact structure than the Delta robot, despite its slightly reduced dexterity. These findings highlight that the TriVariant is especially suitable for aerial manipulation in space-constrained environments where all limbs must be mounted beneath the UAV. Full article

The integration of unmanned aerial vehicles (UAVs) and deep learning (DL) has significantly advanced crop disease detection by enabling scalable, high-resolution, and near real-time monitoring within precision agriculture. This systematic review analyzes peer-reviewed literature indexed in the Web of Science Core Collection as articles or proceeding papers through 2024. The main selection criterion was combining “unmanned aerial vehicle*” OR “UAV” OR “drone” with “deep learning”, “agriculture” and “leaf disease” OR “crop disease”. Results show a marked surge in publications after 2019, with China, the United States, and India leading research contributions. Multirotor UAVs equipped with RGB sensors are predominantly used due to their affordability and spatial resolution, while hyperspectral imaging is gaining traction for its enhanced spectral diagnostic capability. Convolutional neural networks (CNNs), along with emerging transformer-based and hybrid models, demonstrate high detection performance, often achieving F1-scores above 95%. However, critical challenges persist, including limited annotated datasets for rare diseases, high computational costs of hyperspectral data processing, and the absence of standardized evaluation frameworks. Addressing these issues will require the development of lightweight DL architectures optimized for edge computing, improved multimodal data fusion techniques, and the creation of publicly available, annotated benchmark datasets. Advancements in these areas are vital for translating current research into practical, scalable solutions that support sustainable and data-driven agricultural practices worldwide. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries due to their adaptability, efficiency, and capability to operate in diverse environments. However, conventional UAV designs face trade-offs between flight endurance and maneuverability. This study explores the design, analysis, and optimization of a biplane quadrotor UAV, integrating the vertical takeoff and landing (VTOL) capabilities of multirotors with the aerodynamic efficiency of fixed-wing aircraft to enhance flight endurance while maintaining high maneuverability. The UAV’s structural design incorporates biplane wings with different NACA airfoil configurations (NACA4415, NACA0015, and NACA0012) to assess their impact on drag reduction, stress distribution, and flight efficiency. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent 2023 R2 (Canonsburg, PA, USA).reveal that the NACA0012 airfoil achieves the highest drag reduction (75.29%), making it the most aerodynamically efficient option. Finite Element Analysis (FEA) further demonstrates that NACA4415 exhibits the lowest structural stress (95.45% reduction), ensuring greater durability and load distribution. Additionally, a hybrid flight control system, combining Backstepping Control (BSC) and Integral Terminal Sliding Mode Control (ITSMC), is implemented to optimize transition stability and trajectory tracking. The results confirm that the biplane quadrotor UAV significantly outperforms conventional quadcopters in terms of aerodynamic efficiency, structural integrity, and energy consumption, making it a promising solution for surveillance, cargo transport, and long-endurance missions. Future research will focus on material enhancements, real-world flight testing, and adaptive control strategies to further refine UAV performance in practical applications. Full article

Efficient pesticide delivery in mango orchards is hindered by tall canopies and dense foliage. This study evaluated two ultra-low-volume (ULV) nozzles—TeeJet XR and HYPRO rotary—mounted on an indigenous multirotor drone during flowering and fruit-set stages in ‘Dashehari’ mango. HYPRO achieved 14% higher lower-canopy penetration, while TeeJet provided better upper coverage. Droplet spectra differed by 58 µm. UAV-based ULV spraying reduced carrier water by 97% and CO₂-equivalent emissions by 99% compared to air-blast methods. Results underscore the importance of nozzle selection and support UAV adoption for climate-smart, resource-efficient horticulture in India. Full article

Tilt-rotor Vertical Takeoff and Landing Unmanned Aerial Vehicles (TR-VTOL UAVs) combine fixed-wing and rotary-wing configurations, offering optimized flight planning but presenting challenges due to their complex dynamics and uncertainties. This study investigates a multi-agent reinforcement learning (RL) control system utilizing Soft Actor-Critic (SAC) modules, which are designed to independently control each input with a tailored reward mechanism. By implementing a novel reward structure based on a dynamic reference response region, the multi-agent design improves learning efficiency by minimizing data redundancy. Compared to other control methods such as Actor-Critic Neural Networks (AC NN), Proximal Policy Optimization (PPO), Nonsingular Terminal Sliding Mode Control (NTSMC), and PID controllers, the proposed system shows at least a 30% improvement in transient performance metrics—including RMSE, rise time, settling time, and maximum overshoot—under both no wind and constant 20 m/s wind conditions, representing an extreme scenario to evaluate controller robustness. This approach has also reduced training time by 80% compared to single-agent systems, lowering energy consumption and environmental impact. Full article

Multirotor UAVs powered by batteries face limitations due to the low energy density of their energy source, which constitutes a significant portion of the total weight. During missions, the high battery mass remains constant, necessitating high required power. This leads to reductions in payload capacity and endurance constraints. This study developed a design tool for multirotor UAVs that sequentially detach used batteries during missions to reduce weight and extend endurance. The developed tool consists of a battery weight prediction model and a required power prediction model. It accurately predicts endurance by considering changes in weight, thrust, RPM, motor-propeller efficiency, and required power at each battery separation point. Using the developed tool, the battery separation technology was applied to a quadcopter with total weights of 7, 15, and 25 kg, and the extended endurances were quantitatively compared. The results showed endurance improvements of 127.3%, 122.0%, and 127.0% for the 7, 15, and 25 kg quadcopters, respectively, compared to using a single battery. In addition, the method was applied to the commercially available industrial UAV DJI Matrice 300 RTK. With a 2.7 kg payload, the two-stage battery configuration extended the endurance by 12.5% compared to the single-battery case. Under no-payload conditions, a three-stage configuration achieved a 16.7% improvement. These results confirm the effectiveness of staged battery detachment even in real-world UAV platforms. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article