Search Results (277)

Recent advancements in Unmanned Aerial Vehicle (UAV) physics simulators, flight control firmware, and network virtualization have been substantial. However, operating these systems independently fails to capture the complex dynamics of real-world multi-UAV networks, thereby compromising simulation reliability. To address this, we propose the Multi-UAV Network-in-the-Loop Simulation (MUNILS) platform, which seamlessly integrates the Gazebo physics engine, the PX4 flight controller, and the ns-3 network simulator via Robot Operating System 2 (ROS2) middleware. Specifically, MUNILS leverages Micro eXtremely Resource Constrained Environments–Data Distribution Service (XRCE-DDS) for high-speed data bridging and employs Linux network namespaces to enforce traffic isolation and routing exclusively through ns-3. Crucially, we introduce a precise cross-layer time synchronization mechanism spanning the physical, control, and network domains to resolve inherent clock discrepancies among these heterogeneous simulators. Experimental evaluations confirm that MUNILS achieves strict traffic isolation, scalable closed-loop flight control, and highly accurate time synchronization across all integrated modules (Gazebo, ns-3, ROS2, and PX4) without cumulative clock drift, thereby providing a highly reliable verification environment for large-scale swarm operations on a single machine. Full article

The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. To address this issue, this paper proposes Area-to-Point Matching Visual–Inertial SLAM (A2PM-VINS), a visual–inertial SLAM method based on Area-to-Point matching. The method introduces Area-to-Point hierarchical matching and a kinematic temporal inheritance mechanism to improve matching reliability and track continuity, and further designs an Anchor–Explorer feature selection strategy to retain features with higher geometric value for back-end optimization. In addition, a Sub-Window Consistency (SWC) weighting strategy is incorporated into the back end to suppress geometrically deceptive observations with poor temporal continuity and geometric consistency. Experiments on the European Robotics Challenge Micro Aerial Vehicle (EuRoC MAV) dataset show that A2PM-VINS achieves superior or competitive localization accuracy on multiple challenging sequences. The absolute trajectory errors on MH_04 and MH_05 are 0.0983 m and 0.1191 m, respectively, and stable tracking is maintained on V2_02, where VINS-Fusion fails. These results show that the proposed method effectively improves the robustness of visual–inertial state estimation in complex degraded environments. Full article

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

Heterogeneous unmanned ground vehicle-unmanned aerial vehicle (UGV-UAV) collaborative systems offer clear advantages for field exploration. However, when tethered unmanned aerial vehicles (TUAVs) are introduced to extend mission capability, a major compatibility gap emerges for small and highly maneuverable UGVs: existing industrial tethered ground stations are generally too heavy and bulky to be carried by such platforms. In addition, on unstructured ground, residual station tilt can significantly complicate UAV launch and recovery. To address these issues, this paper develops an ultralight vehicle-mounted tethered ground station for micro unmanned aerial vehicles (micro-UAVs) that can be integrated directly with small UGVs. Through co-design of a 2-degree-of-freedom (2-DOF) self-leveling launch platform and a passive tether-assisted recovery scheme without visual fiducials, in which a customized UAV flight-control loop is coordinated with the state transitions of the ground tether-management system, the proposed system achieves practical tether-assisted recovery. Experiments show that the complete platform weighs only 4.1 kg and that the self-leveling mechanism compensates for ground inclinations over a total range of 24 degrees. Repeated passive-landing tests further demonstrate the feasibility of the proposed recovery scheme and its tolerance to moderate bay tilt and terminal off-axis activation. System-level flight validation confirms practical tether-assisted recovery without visual fiducials. In addition, we conduct a simplified exploratory simulation of tether-based ground-anchor localization under the proposed system architecture. Overall, these results establish a lightweight and low-cost hardware design and a practically viable recovery strategy for multimodal micro air-ground robotic systems. Full article

This paper presents the application of a micro unmanned aerial vehicle (UAV) that acts as a pollination agent in a controlled environment simulating greenhouse conditions. The micro-UAV system was integrated with a convolutional neural network (CNN) for autonomous flower detection and navigation. The custom Sequential CNN architecture was used on board to perform real-time binary classification, accurately distinguishing flowers from non-flower objects. The fusion of this deep learning-based detection with precise micro-UAV navigation enables efficient identification and approaches to target flowers within optimal operational distances. Experimental evaluations revealed that the micro-UAV’s onboard camera, combined with CNN processing, outperformed standard webcams in terms of detection speed and accuracy, demonstrating the benefits of specialized hardware. Within the experiment, the micro-UAV was pre-programmed to follow a ‘cross’-shaped flight pattern. Experimental results show that the proposed system successfully detects multiple flowers autonomously between distances of 30.5 cm and 91.5 cm within 149.1 s. Overall, this study validated the integration of neural network capabilities with micro-UAV navigation. These findings are crucial for highlighting the potential of neural network-enabled micro-UAVs as effective pollinators in enclosed agricultural environments and for addressing the challenges faced by natural pollinators in greenhouses. Full article

The large-scale operation of multiple fixed-wing unmanned aerial vehicles (UAVs) in shared airspace requires efficient flight conflict detection and resolution to ensure aviation safety. However, existing research predominantly lacks collaborative optimization of multi-dimensional maneuver recommendations and struggles with dynamic priority allocation in complex multi-UAV scenarios, leaving a critical gap in the field. To bridge this gap, this paper proposes a Complex Network-Based Multi-UAV Conflict Resolution (NCR) method, which first constructs a three-dimensional (3D) flight conflict detection and resolution model for fixed-wing UAVs. The core innovation lies in mapping dynamic multi-UAV conflict scenarios into a flight conflict network, where UAVs serve as nodes and conflict urgencies act as edge weights. By calculating network and node robustness, the method accurately identifies key UAVs requiring immediate maneuver. Subsequently, taking the minimum variation in the velocity vector as the core objective, NCR iteratively searches for optimal resolution recommendations for these key UAVs using an improved fitness function until the conflict network collapses. Simulation and comparative experiments in 3D airspace, including evaluations against serial-based resolution, random-recommendation resolution, and a classical reactive baseline, demonstrate that NCR efficiently resolves multi-UAV conflicts with minimal trajectory deviations and fewer maneuvering UAVs. Furthermore, a macro-micro bi-level validation architecture based on a six-degree-of-freedom (6-DOF) aerodynamic platform is introduced to verify the physical executability of the proposed strategies. Results demonstrate that by incorporating a dynamic aerodynamic compensation margin, the inevitable trajectory tracking deviations caused by system inertia are enveloped within the safety threshold, ensuring absolute flight safety in engineering practice. Notably, as conflict complexity increases, NCR exhibits prominent advantages in reducing velocity variation costs, minimizing the number of maneuvering UAVs, and avoiding unnecessary trajectory deviations. Full article

A model predictive control (MPC) strategy is proposed based on state observation and updating for image-based visual servoing (IBVS) tasks of micro aerial vehicles (MAVs). This control strategy enables precise pose adjustment of MAVs without relying on the global positioning system (GPS). Specifically, image features are first defined on a virtual image plane to decouple the translational motion of the MAV. Subsequently, a linear velocity observer is developed to provide high-quality real-time velocity information for the MAV during IBVS execution. Furthermore, the image dynamics on the virtual image plane are linearized using a first-order Taylor expansion, and a linear MPC controller is formulated to efficiently compute the optimal control inputs. Moreover, the state inputs to the MPC controller are updated at each control cycle to eliminate errors accumulated during the rolling optimization based on the linearized dynamics, thereby ensuring the precision of IBVS. Simulation and experimental results demonstrate the performance of the proposed observer and control strategy. Full article

This paper presents an experimental investigation of the level flight speed and endurance characteristics of a micro-class unmanned aerial vehicle as a function of operating weight. Wind tunnel experiments were conducted to determine the aerodynamic performance and power requirements of the UAV over a range of operating weight configurations. The tested vehicle, a fixed-wing micro UAV, was examined under steady, level flight conditions, with particular emphasis on identifying variations in the minimum power required to sustain level flight. Measured aerodynamic forces and moments were used to derive drag polars and the corresponding power curves for each mass configuration. Based on these results, endurance estimates were obtained by coupling the experimentally derived power requirements with the characteristics of the onboard electric propulsion system. The study demonstrates a clear shift in flight speeds with increasing operating weight, as well as a reduction in achievable endurance, highlighting the sensitivity of micro-class UAV performance to mass variations, and therefore energy consumption. Full article

The increasing need to reduce reliance on fossil-derived aviation fuels and mitigate environmental impacts has intensified research into renewable alternatives for aviation energy systems. The growing interest in ethanol-based fuels is primarily driven by their simple oxygen-rich molecular structure and advantageous physicochemical characteristics, yet experimental studies examining their application in hybrid power architectures, including micro-turboprop engine-based power sources, are still limited. This study presents an experimental investigation of ethanol–Jet A1 fuel blends used in a micro-turboprop engine operating as a power generation unit for unmanned aerial vehicle applications. Ethanol was blended with Jet A1 at volumetric fractions of 10%, 20% and 30% and the engine was tested under multiple operating regimes corresponding to different electrical power outputs. Exhaust gas temperature, electrical power output and gaseous emissions (CO and NO_x) were measured for each operating condition. The results indicate that low ethanol fractions (E10) provide performance comparable to neat kerosene, while higher ethanol fractions lead to a reduction in exhaust gas temperature at low-power regimes due to the lower heating value and high latent heat of vaporization of ethanol. Emission measurements showed a decrease in NO_x emissions with increasing ethanol content, associated with lower combustion temperatures, while CO emissions increased at low-power regimes due to incomplete combustion under lean conditions. Additionally, combustion instability was observed during rapid transitions from maximum to idle regime operation for higher ethanol blends, attributed to transient ultra-lean mixtures, evaporative cooling, and reduced reaction rates. The results demonstrate that ethanol–kerosene blends can be used in micro-turboprop systems at low blend ratios without major performance penalties, but transient operating conditions impose stability limits that must be considered in practical UAV power system applications. Full article

To address the challenge of accurately distinguishing UAVs and birds in a low-altitude detection field, this paper proposes a classification algorithm of UAVs and birds based on L/K dual-band micro-Doppler spectrograms and Mamba. We establish a dual-band radar detection model for unmanned aerial vehicles (UAVs) and birds, provide a method for characterizing the Doppler parameters of the echo signals, and research a UAV and bird target classification network model that integrates micro-Doppler and Mamba. Based on a dual-branch encoding framework, we use Patch block decomposition to design a classification model to serialize the two-dimensional spectrogram of the echo signal, and introduce the Mamba state-space backbone network to extract the long-term sequence features of the target’s micro-motion. The main breakthrough of the proposed classification algorithm lies in the feature fusion stage, where a late fusion strategy is adopted to integrate the dual-path high-level representation measures, fully leveraging the sensitivity of the K-band to high-frequency textures and the scale complementarity of the L-band. Then, according to the joint loss function of mutual learning and contrastive learning, we improve the model’s prediction consistency and representation discriminability. Through experimental testing, the results show that the proposed method can effectively classify UAVs and birds, and compared with other algorithms, the accuracy rate reaches 97.5%. Full article

Micro air vehicles (MAVs) operating at low Reynolds numbers face aerodynamic and structural challenges that differ significantly from those encountered by conventional aircrafts. Nature provides effective solutions to these constraints, as insects, birds, and bats demonstrate highly efficient flight through integrated interactions between morphology, kinematics, and unsteady aerodynamic mechanisms. This review examines how biological flight principles can inform the design of next-generation MAVs. The study first analyzes biological flight strategies across insects, birds, and bats, with emphasis on scaling laws and physiological adaptations relevant to small-scale flight. It then reviews key unsteady aerodynamic phenomena governing low-Reynolds-number flight, including leading-edge vortex stability, wing–wake interactions, tandem-wing effects, and ground influence, as well as current modeling approaches ranging from quasi-steady methods to high-fidelity Navier–Stokes simulations. Building on these principles, the paper discusses biomimetic design strategies for MAV wings, structural–aerodynamic coupling, and actuation technologies used to replicate flapping flight. Existing MAV demonstrators inspired by biological flyers are analyzed, including concepts relevant to planetary exploration environments. Finally, the review identifies current technological limitations and research gaps in materials, actuation, aerodynamic modeling, and system integration. By synthesizing insights from biology and engineering, this work highlights key directions for the development of efficient, adaptable biomimetic MAV platforms capable of operating in complex environments. Full article

In UAV (unmanned aerial vehicle)-enabled precision agriculture, achieving high-accuracy disease diagnosis and yield estimation simultaneously on resource-constrained edge devices remains a significant challenge. Existing solutions are commonly hindered by conflicts in visual feature scales, the absence of explicit agronomic causal logic, and the trade-off between lightweight design and global modeling capability. To address these challenges, a heterogeneous dual-stream state-space framework termed SymbioMamba is proposed. The proposed framework incorporates three key innovations: first, a heterogeneous dual-stream encoder is constructed, in which a micro-texture stream captures high-frequency disease details while a macro-context-scan stream models field-scale biomass continuity; second, a pathology–biomass collaborative interaction (PBCI) module is designed to explicitly inject the biological prior—disease stress leading to yield reduction—into the feature space. Third, a topology-aligning cross-architecture distillation (TACAD) paradigm is introduced to transfer global knowledge from a heavyweight teacher to a lightweight student. Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an $R^2$ of 0.915. Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines. Furthermore, yield prediction error is significantly reduced to an RMSE of 485.6 kg/ha. With a compact model size of 6.2 M parameters and 2.4 G FLOPs, SymbioMamba attains an inference speed of 38.2 FPS on the NVIDIA Jetson AGX Orin platform, providing a high-performance, real-time solution for intelligent agricultural phenotypic analysis. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

The aerodynamics of avian flight provides critical inspiration for the design of bioinspired aerial vehicles, yet the quantitative characterization of free-flight wing kinematics remains challenging. This study employs a neural-network-based motion tracking approach (DeepLabCut) to analyze wingbeat kinematics in free-flying birds from video data. We automatically digitize key wing points and reconstruct three-dimensional trajectories to quantify asymmetric flapping patterns. Our analysis reveals that while wing oscillations approximate sinusoidal motion, they exhibit statistically significant velocity differences between upstroke and downstroke phases, confirming the stroke asymmetry of avian flapping. Furthermore, using video of a flying frigatebird (Fregata ariel), we quantify the changes in the effective wing area throughout the wingbeat cycle, showing a ~19% variation that significantly impacts lift generation efficiency. These findings provide quantitative benchmarks for avian-inspired wing design and offer insights for optimizing flapping kinematics in bioinspired aerial systems, particularly for enhancing takeoff and landing capabilities in micro air vehicles. Full article

Apple snail infestation poses a persistent threat to rice production in open-field environments, where long-term coexistence with this species is unavoidable. This study presents a drone-based precision control approach that integrates high-resolution micro-topographic mapping with site-specific pesticide application. A lightweight mapping unmanned aerial vehicle was deployed to produce centimeter-level microtopographic data across paddy fields, facilitating the identification of deep-water areas preferred by apple snails. From these elevation-derived water risk patterns, prescription maps were generated to guide downstream management decisions, and agricultural drones equipped for granular application subsequently performed targeted pesticide delivery only in these high-risk areas. Over 2 years of field experiments, the proposed method achieved rice yields comparable to those under conventional management while reducing pesticide use by 44.1–63.0%, with lower estimated crop damage in regions with high apple snail occurrence. Designed with robustness and scalability in mind, the system demonstrated considerable potential for practical implementation in general farming households and broader applications in precision pest management. Full article