Search Results (133)

Future planetary exploration missions are expected to employ increasingly sophisticated aerial, ground, and hybrid robotic systems that must interact directly with extraterrestrial regolith during landing, takeoff, mobility, anchoring, sampling, and subsurface investigation activities. Consequently, understanding the mechanical behavior of lunar and Martian regolith is essential for the design and reliable operation of autonomous exploration platforms. This review examines drone–regolith interaction from a system-level perspective by integrating knowledge of regolith mechanical properties with findings from penetration mechanics, anchoring technologies, mobility studies, numerical modelling, and in situ mission observations. Key differences between lunar and Martian regolith are identified, highlighting the predominantly friction-driven behavior of lunar soils and the combined frictional–cohesive response frequently observed in Martian regolith. Lessons learned from planetary missions, particularly the Apollo and Mars InSight programs, demonstrate how system–soil mismatch can significantly affect penetration, stabilization, and surface-operation performance. The review further discusses the implications of regolith mechanics for landing stability, rotor–surface interaction, anchoring efficiency, subsurface access, and future drone-assisted exploration concepts. Finally, current challenges in experimental validation and numerical modelling are assessed, emphasizing the need for integrated approaches that combine soil mechanics, robotic system design, and environmental constraints to enable reliable autonomous operations on the Moon and Mars. Full article

As unmanned aerial vehicles (UAVs) are increasingly deployed in various fields, their flight safety has become a critical issue. However, limited onboard sensing and computing resources make it difficult to perform intelligent fault monitoring and diagnosis directly on UAVs. To explore an offboard alternative, this paper investigates a drone nest vibration analysis based fault diagnosis framework for a multirotor UAV rotor system using vibration signals measured from a laboratory-scale simulated drone nest. A simplified coupled dynamic model of the UAV–drone nest system is established to analyze the transmission mechanism of rotor fault-induced vibration and to explain the observability of fault-related frequency components under the tested configuration. Considering the weak and attenuated characteristics of the nest-side vibration signals, a multi-domain feature fusion and multi-task learning network is developed to integrate time-domain, frequency-domain, and envelope-spectrum information while jointly learning fault type and rotational speed. Comparative experiments on the constructed quadrotor–drone nest test platform are conducted to validate the feasibility and effectiveness of the proposed method under the tested operating conditions. Full article

Outer rotor permanent magnet synchronous motors (ORPMSMs) are widely used in drone and aircraft propulsion due to their high power density. However, conventional arc-shaped designs involve an inherent trade-off between efficiency and torque ripple. Increasing the arc curvature improves the sinusoidal air gap flux density and reduces torque ripple, but it also increases rotor eddy current loss due to larger flux variations, thereby degrading efficiency. This paper investigates the effects of stator and rotor geometries on rotor eddy current loss and torque ripple in ORPMSMs. To address this trade-off, arc- and rectangular-shaped rotor and stator pole shoes are combined to form four design candidates. Their electromagnetic performance is evaluated using finite element analysis. Based on this comparison, a configuration with rectangular rotor and stator pole shoes is selected as the initial design and further optimized using a multi-objective genetic algorithm to simultaneously improve efficiency and torque ripple. The optimized design demonstrates significant improvements, achieving reductions of 56.67% in peak-to-peak torque ripple and 46.89% in rotor eddy current loss compared to the initial design. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone. Full article

Rotor blades are the primary cause of drone failure. To assess the health status of drone blades, vibration monitoring is required; however, this is challenging due to noisy signals and limited labeled datasets. This study investigates a data loading strategy called selective embedding (SE), which is shown to improve data diagnosis across engineering fields. The hypothesis is that this strategy can improve the classification accuracy of drone blade conditions with multi-axis vibration data. Accelerometer signals are collected under different blade health conditions; the signals are then processed and fed into a deep learning model for multi class condition classification. An ablation study is conducted with different data loading strategies, including traditional single channel, parallel channel, and SE. The results show that SE improves classification accuracy, reduces performance variance, and achieves higher generalization performance across multiple blade fault conditions. These improvements are observed consistently across domain evaluations, where traditional data loading strategies have difficulty generalizing to unseen temporal segments. The findings demonstrate that SE can effectively support vibration fault diagnostics for aerospace applications, offering a reliable way to improve safety in drone monitoring. Full article

The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates the aerodynamic characteristics of a quadrotor drone with a vertically offset rear-rotor configuration through computational fluid dynamics (CFD) simulations. By varying the vertical spacing ratio between the front and rear rotors (H/R), quantitative analyses were conducted of key performance metrics, including rotor thrust and power loading, with explanations provided from the perspective of the flow field structure. Furthermore, the underlying physical mechanisms influencing the observed performance variations are explored. The results indicate that, under the operating conditions investigated in this study, which include a single rotor RPM, a 10° inflow angle, and a specific forward-flight speed, the vertically offset configuration demonstrates superior aerodynamic performance at H/R = 1. At this spacing ratio, the rear rotor disk avoids most of the downwash-induced velocity generated by the front rotor, allowing partial recovery of the effective angle of attack. Consequently, vortex-propeller interaction (PVI) is significantly weakened, turbulent kinetic energy (TKE) levels in the interference region are reduced, and premature flow separation on the rear rotor blades is suppressed. These combined effects enhance overall aerodynamic efficiency. This study clarifies the role of vertical rotor spacing in regulating aerodynamic interference in multi-rotor drones, offering valuable insights for the aerodynamic design of compact rotorcraft. Full article

To address flight safety risks from rotor defects in rotorcraft drones operating in complex low-altitude environments, this study proposes a high-precision diagnostic model based on the Multimodal Data Input and Spatio-Temporal Feature Fusion Network (MDI-STFFNet). The model uses a dual-modality coupling mechanism that integrates vibration and air pressure signals, forming a “single-path temporal, dual-path representational” framework. The one-dimensional vibration signal and the five-channel pressure array are mapped into a texture space via phase space reconstruction and color-coded recurrence plots, followed by extraction of transient spatial features using a pre-trained ResNet-18 model. Parallel LSTM networks capture long-term temporal dependencies, while a parameter-free 1D max-pooling layer compresses redundant pressure data, reducing LSTM parameter growth. The CSW-FM module enables adaptive fusion across modal scales via shared-weight mapping and learnable query vectors that dynamically assign spatiotemporal weights. Experiments on a self-built dataset with seven defect types show that the model achieves 99.01% accuracy, improving by 4.46% and 1.98% over single-modality vibration and pressure inputs. Ablation studies confirm the benefits of spatiotemporal fusion and soft weighting in accuracy and robustness. The model provides a scalable, lightweight solution for UAV power system fault diagnosis under high-noise and varying conditions. Full article

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

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. 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 increase in production of small unmanned rotorcrafts (SURs) has made real-time drone surveillance critical for airspace security. Effective SUR detection is essential for maintaining aviation safety, protecting privacy, and ensuring public security. However, conventional radar systems struggle to detect hovering SURs due to their low velocity and small radar cross-section (RCS), which make them nearly indistinguishable from stationary clutter. To address this issue, this paper proposes a hovering SUR detection method through identifying the micro-Doppler signal (MDS). By applying the multiple reassignment squeeze processing and exhaustive Hough transform, the proposed approach effectively enhances the accumulation of micro-Doppler signal generated by the rotor blades, which enables the separation of hovering SUR signals from stationary clutter. Numerical simulations and field experiments validate the effectiveness of the proposed method, demonstrating its potential for micro-Doppler signal detection using a UHF-band horizontally co-polarized radar system. Full article

With the growing demand for high-efficiency and high-performance electric motors in applications such as electric vehicles, drones, and industrial drive systems, Axial Flux Motors (AFMs) have gained significant attention due to their high torque density and compact structure. However, low-pole AFMs are prone to performance degradation and noise issues caused by magnetic saturation in the rotor back yoke and increased torque ripple. In this study, a conventional 6-pole, 9-slot Radial Flux Motor (RFM) was redesigned as an AFM within the same external volume. To minimize losses, the stator inner diameter and slot thickness were co-optimized. In addition, tapering techniques were applied to both the stator and magnets to reduce torque ripple, and a parametric analysis of magnet tapering was conducted to identify optimal design conditions. A rolling core fabrication method was adopted to ensure both electromagnetic performance and manufacturability. The final AFM design demonstrated a 1.4 percentage point improvement in efficiency. Additionally, torque ripple was reduced by 69.44%, thereby validating the effectiveness of the AFM redesign and ripple reduction strategy. Full article

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings. Full article

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone. Full article