Search Results (242)

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection. Full article

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article

This study investigates the tracking control of quadrotor micro aerial vehicles using nonlinear model predictive control (NMPC), with primary emphasis on the implementation of a real-time embedded control system. Apart from the limited memory size, one of the critical challenges is the limited processor speed on resource-constrained microcontroller units (MCUs). This technical issue becomes critical particularly when the maximum allowed computation time for real-time control exceeds 0.01 s, which is the typical sampling time required to ensure reliable control performance. To reduce the computational burden for NMPC, we first derive a nonlinear quadrotor model based on the quaternion number system rather than formulating nonlinear equations using conventional Euler angles. In addition, an implicit continuation generalized minimum residual optimization algorithm is designed for the fast computation of the optimal receding horizon control input. The proposed NMPC is extensively validated through rigorous simulations and experimental trials using Crazyflie 2.1^®, an open-source flying development platform. Owing to the more precise prediction of the highly nonlinear quadrotor model, the proposed NMPC demonstrates that the tracking performance outperforms that of conventional linear MPCs. This study provides a basis and comprehensive guidelines for implementing the NMPC of nonlinear quadrotors on resource-constrained MCUs, with potential extensions to applications such as autonomous flight and obstacle avoidance. Full article

This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. Full article

Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods. Full article

The field of unmanned aerial vehicles (UAVs) has experienced substantial growth, with applications expanding across diverse domains. Missions increasingly demand higher autonomy, reducing human intervention and relying more on advanced onboard systems. However, integrating hybrid power sources, especially micro-turboprop engines, into UAVs poses significant challenges due to their complexity, hindering the development of effective power management control systems. This research aims to design a control algorithm for dynamic power allocation based on UAV operational needs. A fuzzy logic-based control algorithm was implemented on the Single-Board Computer (SBC) of a micro-turbogenerator test bench, which was previously developed in an earlier study. After implementing and testing the algorithm, voltage stabilization was achieved at improved levels by tightening the membership function constraints of the fuzzy logic controller. Automating the throttle control of the Electric Ducted Fan (EDF), the test platform’s primary power consumer, enabled the electric generator’s maximum capacity to be reached. This result indicates the necessity of replacing the current electric motor with one that is capable of higher power outputs to support the system’s enhanced performance. Full article

The collapse of structures during firefighter intervention is one of the greatest risks that firefighters must face when entering buildings. To reduce these risks, situational awareness is key. Although many advances have already been developed in wildland and outdoor fires, there is still room for improvement in structure fires. The development of a heat-resistant micro-aerial vehicle for indoor fires poses a series of challenges such as component cooling, battery management, and protection from impacts. In this paper, a heat-resistant tethered micro-aerial vehicle is designed, modeled through thermal analysis, and successfully tested in real-world conditions. This platform has been equipped with a micro-sized thermal sensing camera and first-person-view (FPV) camera, optimized for thermal management, to allow for situational awareness in structure fires. Full article

Wireless power transmission has become a research hotspot in the field of energy transmission, in which laser power transmission is one of the best methods for long-distance wireless transmission. Since laser has the advantages of high directivity, high energy density and no electromagnetic interference, laser power transmission technology can be applied to the energy supply of unmanned aerial vehicles (UAVs), micro-vehicles, airships and other flight systems. Long-distance laser power transmission can enable high-altitude flight systems to operate continuously without the need to return to the base station for charging, im-proving their operational efficiency. Therefore, high-altitude flight systems have a demand for laser power transmission. However, the commonly used lasers in laser power transmission are semiconductor lasers and fiber lasers, which are only suitable for short-distance transmission of about 1 km. In this paper, taking high-flying UAVs as an example, the transmission efficiency of different lasers used for laser power transmission is analyzed theoretically, and the results show that the diode pumped alkali vapor laser (DPAL) has a high transmission efficiency in high-power long-distance laser power transmission. The transmission efficiency of rubidium lasers which is 1.5 to 4 times that of other lasers can reach 21.94%, which illustrates that DPAL is expected to become a new type of laser source in laser power transmission technology. Full article

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

In this paper, a fast and accurate simulation method for the large deformation motion of anisotropic complex models is proposed. By establishing a fluid–structure interaction (FSI) coupling model based on the Lattice Boltzmann Method (LBM) and the Central Difference Method, the effect of flexible deformation on the aerodynamic performance of anisotropic wings during flapping is analyzed. The method can provide theoretical guidance and data support for the fluid–solid coupling study and the aerodynamic optimization of Micro Aerial Vehicles (MAVs). Full article

Flapping wing micro aerial vehicles (FWMAVs) are recognized for their significant potential in military and civilian applications, such as military reconnaissance, environmental monitoring, and disaster rescue. However, the lack of takeoff and landing capabilities, particularly in landing behavior, greatly limits their adaptability to the environment during tasks. In this paper, the purple stem beetle (Sagra femorata), a natural flying insect, was chosen as the bionic research object. The three-dimensional reconstruction models of the beetle’s three thoracic legs were established, and the adhesive mechanism of the thoracic leg was analyzed. Then, a series of bionic design elements were extracted. On this basis, a hook-pad cooperation bionic deployable landing mechanism was designed, and mechanism motion, mechanical performance, and vibration performance were studied. Finally, the bionic landing mechanism model can land stably on various contact surfaces. The results of this research guide the stable landing capability of FWMAVs in challenging environments. Full article

In recent years, bioinspired insect flight has become a prominent research area, with a particular focus on beetle-inspired aerial vehicles. Studying the unique flight mechanisms and structural characteristics of beetles has significant implications for the optimization of biomimetic flying devices. Among beetles, Allomyrina dichotoma (rhinoceros beetle) exhibits a distinct wing deployment–flight–retraction sequence, whereby the interaction between the hindwings and protective elytra contributes to lift generation and maintenance. This study investigates A. dichotoma’s wing deployment, flight, and retraction behaviors through motion analysis, uncovering the critical role of the elytra in wing folding. We capture the kinematic parameters throughout the entire flight process and develop an accurate kinematic model of A. dichotoma flight. Using smoke visualization, we analyze the flow field generated during flight, revealing the formation of enhanced leading-edge vortices and attached vortices during both upstroke and downstroke phases. These findings uncover the high-lift mechanism underlying A. dichotoma’s flight dynamics, offering valuable insights for optimizing beetle-inspired micro aerial vehicles. Full article

The transition to alternative electrical energy solutions for drone propulsion systems presents several challenges, particularly in managing noise. This noise, compounded by that from the propellers, can produce spectra that are either unpleasant to humans or detrimental to mission objectives. This study explores potential solutions to mitigate noise produced by a micro turboshaft engine, focusing on the solutions’ impact on weight, power output, and acoustic level. We propose two modular, scalable designs—one for the intake and one for the exhaust—based on well-known applications in cold and hot flows. These designs aim to operate effectively across the audible frequency spectrum and incorporate various Helmholtz resonator geometries, including combinations of different lengths, perforated metal sheet parameters, and cavity-filling materials, to enhance bandwidth and noise reduction. Experimental results indicate that these designs can achieve tonal noise reductions of up to 40 dB. While the results are promising, further analysis is required to evaluate the practical applicability and comprehensive impact of these solutions on drone performance. Full article

A Flapping-Wing Rotor (FWR) is a novel bio-inspired micro aerial vehicle configuration, featuring unique wing motions which combine active flapping and passive rotation for high lift production. Power efficiency in flight has recently emerged as a critical factor in FWR development. The current study investigates an elastic flapping mechanism to improve FWRs’ power efficiency by incorporating springs into the system. The elastic force counteracts the system inertia to accelerate or decelerate the wing motion, reducing the power demand and increasing efficiency. A dynamic model was developed to simulate the unique kinematics of the FWR’s wing motions and its elastic mechanism, considering the coupling of aerodynamic and inertial forces generated by the wings, along with the elastic and driven forces from the mechanism. The effects of the spring stiffness on the aerodynamic performance and power efficiency were investigated. The model was then verified through experimental testing. When a spring stiffness close to the mechanical system resonance was applied, the power efficiency of the test model increased by 16% compared to the baseline model without springs, generating an equivalent average lift. With an optimal elastic flapping mechanism for greater lift and lower power consumption, the FWR was fully constructed with onboard power and a control receiver weighing 27.79 g, successfully achieving vertical take-off flight. The current model produces ten times greater lift and has nearly double the wing area of the first 2.6 g flyable FWR prototype. Full article