Search Results (160)

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in fields such as autonomous missions, reconnaissance, surveillance, and various industrial applications. These vehicles can perform desired tasks without human intervention in challenging environmental conditions. However, UAV control can be difficult due to environmental factors, wind disturbances, and uncertainties in system parameters. Therefore, developing reliable control strategies for UAVs is a significant challenge for researchers and engineers. This study presents a comprehensive review of rotary-wing UAVs, focusing on quadcopter and helicopter systems. Approximately 77 studies were selected from the Web of Science (WOS) database and analyzed, with an emphasis on Sliding Mode Control (SMC) and Fractional-Order SMC (FOSMC) applications in these systems. The review addresses key topics such as degrees of freedom, proposed control methods, adjustment techniques, comparative methods, fractional-order definitions, simulation tools, and explanations. The literature analysis highlights current research trends by showing the performance advantages and limitations of SMC and FOSMC methods. Furthermore, future research directions and existing knowledge gaps are discussed in detail. This review was prepared to provide the control engineering community with a comprehensive understanding of SMC and FOSMC applications in rotary wing systems and to contribute to the development of innovative and effective control strategies. Full article

This paper investigates the impact of tubed propeller design on the energy efficiency and acoustic emissions of sub-250 g quadcopters. This study was motivated by the growing popularity of ultralight UAVs and the lack of experimental data addressing the trade-offs between noise, efficiency, and mass. Ten drone configurations with varying tube geometries and tip clearances were constructed using 3D-printed PLA+ frames and identical propulsion components. Experimental tests were conducted in a reverberation room to measure sound pressure levels and onboard energy consumption during hover. The results show that tubed configurations are 3–6.5 dB louder than untubed ones, with a noticeable shift toward higher frequencies. While tubes increased total power demand by 18–37% compared to the lightest design, they also reduced it by 3–17% relative to untubed drones of the same mass. The findings demonstrate that tubing improves aerodynamic efficiency only under same mass constraints and is most beneficial when mechanical protection is prioritized over noise and endurance. Full article

The foldable quadcopter unmanned aerial vehicle (UAV), transported by an autonomous underwater vehicle (AUV) and launched subaquatically, represents cutting-edge technology for expanding ocean-sensing capabilities. However, its launch stability is severely challenged by complex cross-media flow fields. To address this, this paper employs a high-fidelity CFD method validated by experimental data, combined with dynamic overlapping mesh technology. Within a high-precision numerical wave tank, it systematically investigates the evolution of unsteady hydrodynamic characteristics throughout the entire launch process—from the drone’s emergence from the launch tube to its crossing of the water-air interface. Findings reveal that elevated initial launch velocities substantially alter surface flow patterns, inducing shear stress imbalances and complex flow separation on the trailing surface. This significantly amplifies lateral disturbance forces and yawing moments, constituting primary sources of motion instability. More critically, this study first uncovers and quantifies the hydrodynamic interference mechanism during the synchronous launch of dual vehicles: the wake field generated by the lead vehicle imposes a significant flow-shielding effect on the trailing vehicle. This effect alters its longitudinal forces while introducing an asymmetric pressure distribution, thereby generating substantial lateral interference. This study’s profound elucidation of these core hydrodynamic mechanisms provides crucial theoretical foundations for developing safe launch strategies, trajectory prediction, and anti-interference controller design for future AUV-UAV cooperative systems. Full article

Certain types of unmanned aerial vehicles (UAVs) represent convenient platforms for remote sensing observation as well as low-altitude targets that are themselves monitored by other devices. In order to study remote sensing grayscale and radar cross-section (RCS) in an example drone, we present a fusion framework based on remote sensing imaging and electromagnetic scattering calculations. The results indicate that the quadcopter drone shows weak visual effects in remote sensing grayscale images while exhibiting strong dynamic electromagnetic scattering features that can exceed 29.6815 dBm² fluctuations. The average and peak RCS of the example UAV are higher than those of the quadcopter in the given cases. The example freighter exhibits the most intuitive grayscale features and the largest RCS mean under the given observation conditions, with a peak of 51.6186 dBm². Compared to the UAV, the small boat with a sharp bow design has similar dimensions while exhibiting lower RCS features and intuitive remote sensing grayscale. Under cross-scale conditions, grayscale imaging is beneficial for monitoring UAVs, freighters, and other nearby boats. Dynamic RCS features and grayscale local magnification are suitable for locating and recognizing drones. The established approach is effective in learning remote sensing grayscale and electromagnetic scattering features of drones used for observing freighters. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics. Full article

Quadcopters are attracting widespread attention due to their growing demand for use in various applications. Since wired communication would severely restrict a quadcopter’s range, maneuverability, and applications, quadcopters usually communicate via wireless networks. Although wireless communication allows the freedom of movement necessary for a wide array of quadcopter applications, it is subject to bandwidth constraints. When multiple quadcopters operate simultaneously, the bandwidth of a wireless network will not meet the requirements. To address this issue, we propose an event-triggered fuzzy-networked control system for 3-DOF quadcopters that reduces the bandwidth requirement. We utilized a fuzzy-networked controller to control a 3-DOF quadcopter. After that, we adopted an event-triggered control approach to reduce the bandwidth requirement. Using the proposed method, one only needs to translate the signals while the event-triggering condition is satisfied, thus reducing the amount of data transmitted over the network. Also, to analyze the stability of the overall system, the Lyapunov stability theorem was adopted. Finally, the proposed method was validated through a 3-DOF quadcopter simulation model. The computer simulations are presented to demonstrate that the proposed control strategy enables a 75.2% (without external disturbance) reduction in bandwidth, which is sufficient to achieve the control objective. This reflects the fact that the proposed control scheme can achieve good control performance with relatively little bandwidth resources and indicates its potential to allow scalable deployment of UAVs. Full article

Traditional geological mapping is often time-consuming, labor-intensive, and restricted by rugged terrain. This study addresses these challenges by proposing a novel methodology for automated lithological identification in the Ququleke area of the eastern Kunlun Mountains, which pioneers the integration of portable UAV oblique photogrammetry with a Coordinate Attention-enhanced DeepLabV3+ (CA-DeepLabV3+) semantic segmentation framework for geological mapping. Using a DJI Mavic 3M quadcopter, high-resolution oblique photogrammetric orthophotos were captured to build a pixel-level lithology dataset containing four classes: sandstone, diorite, marble, and Quaternary sediments. The CA-DeepLabV3+ model, adapted from the DeepLabV3+ encoder–decoder framework, integrates a lightweight MobileNetV2 backbone and a Coordinate Attention mechanism to strengthen spatial position encoding and fine-scale feature extraction, crucial for detailed lithological discrimination. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 97.95%, mean accuracy of 97.80%, and mean intersection over union of 95.71%, representing a 5.48% improvement in mean intersection over union (mIoU) over the standard DeepLabV3+. These results indicate that combining UAV oblique photogrammetry with the CA-DeepLabV3+ network enables accurate lithological mapping in complex terrains. The proposed method provides an efficient and scalable solution for geological mapping and mineral resource exploration, highlighting the potential of low-altitude UAV remote sensing for field-based geological investigations. Full article

Accurate in situ wind measurements from rotorcraft uncrewed aerial vehicles (UAVs) can be impacted by the disturbed flow generated by the rotors. However, the extent of this disturbance depends on flight mode, ambient wind, and vehicle configuration, making optimal sensor placement or devising appropriate corrections nontrivial. This study uses steady-state Reynolds-averaged Navier–Stokes (RANS) simulations with an actuator disk model to characterize the flow field around representative quadcopter, hexacopter, and octocopter UAVs under conditions representing hover, ascent, and descent, for different thrust, and with and without crosswind of different magnitude. The results show that the size and shape of the disturbance field vary strongly with flight mode, with descent producing the largest region of disturbed air around the vehicle and ascent the smallest. Crosswinds advect and distort the disturbance region and reduce its vertical extent by sweeping the rotor wash downstream. The disturbance field geometry was found to scale primarily with overall aircraft size and was largely independent of rotor configuration. The effect of differing the rotor thrust was found to approximately scale using a length scale based on the volume flow rate of air through the the rotor plane. Based on these results, to maintain measurement errors below 0.5 m/s, recommended anemometer locations are at least 2.5 aircraft radii from the UAV central axis for hovering conditions when the weight of the aircraft relative to the area swept by the rotors is near 10 kg per square meter. This recommended distance is expected to scale linearly with this ratio, and will reduce under crosswind conditions or when measurements are made during ascent. Full article

The operational range and reliability of most commercially available UAVs employed in surveillance, agriculture, and infrastructure inspection missions are limited due to the use of short-range radio frequency connections. To alleviate this issue, the present work investigates the possibility of real-time long-distance UAV control using a commercial 4G LTE network. The proposed system setup consists of a Raspberry Pi 4B as the onboard computer, connected to a Pixhawk-2.4 flight controller mounted on an F450 quadcopter platform. Flight tests were carried out in open-field conditions at altitudes up to 50 m above ground level (AGL). Communication between the UAV and the ground control station is established using TCP and UDP protocols. The flight tests demonstrated stable remote control operation, maintaining an average control delay of under 150 ms and a video quality resolution of 640×480, while the LTE bandwidth ranging from 3 Mbps to 55 Mbps. The farthest recorded test distance of around 4200 km from the UAV to the operator also indicates the capability of LTE systems for beyond-visual-line-of-sight operations. The results show that 4G LTE offers an effective method for extending UAV range at a reasonable cost, but there are limitations in terms of network performance, flight time and regulatory compliance. This study establishes essential groundwork for future UAV operations that will utilize 5G/6G and satellite communication systems. 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

Quadrotor UAVs require robust control methods to handle complex dynamics, model uncertainties, and external disturbances during trajectory tracking. This paper presents a trajectory tracking control method combining Sliding Mode Control with an Improved Extended State Observer (SMC-IESO). The control system uses a hierarchical structure with position and attitude control loops, employing a third-order Extended State Observer to estimate disturbances in real-time. The improved sliding mode control law incorporates observation error compensation to reduce the required sliding mode gain. Lyapunov stability analysis proves the asymptotic convergence of tracking errors. Simulation results demonstrate that SMC-IESO achieves better tracking accuracy and disturbance rejection than conventional sliding mode control, while significantly reducing control signal chattering, making it more suitable for practical quadrotor applications. 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

This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. Full article