Search Results (496)

This article represents a full estimation of helicopter rotor aerodynamic characteristics in ground effect conditions through the application of a coupled empirical blade element–momentum theory algorithm. The main focus of this research includes the evaluation of the required weighted power coefficients $\left({\displaystyle \raisebox{1ex}{$C_P$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ for a hovering state in close proximity to obstacles and their relation to the weighted thrust force coefficients’ values $\left({\displaystyle \raisebox{1ex}{$C_T$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right),$ varying the relative distance from the helicopter rotational plane to the ground surface $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$ and the rotor’s collective pitch angle (θ). The represented numerical and experimental results show that an increase in the collective pitch angles (θ) leads to a rise in the generated weighted thrust force coefficients $\left({\displaystyle \raisebox{1ex}{$C_T$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ and in the weighted power coefficients $\left({\displaystyle \raisebox{1ex}{$C_P$}\!\left/ \!\raisebox{-1ex}{$\sigma $}\right.}\right)$ for every individual fixed normalized distance from the ground surface $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$. Moreover, a decline in the relative distance from the ground $\left({\displaystyle \raisebox{1ex}{$H$}\!\left/ \!\raisebox{-1ex}{$R$}\right.}\right)$ requires less power to keep the rotation going in hover. The dependencies indicate that the ground effect zone covers a distance of up to 2R from the rotational plane to the ground surface. Full article

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards. Full article

Ecological ethics is gaining traction. Can this new attitude towards our ecosystems help to motivate a change in our relationship with land and nature? Can we move towards a legal system that supports the legal personality of land, devoid of human ownership? There are substantial amounts of international environmental laws that have been hovering on the fringes of defining and then redefining our relationship with land, with more emphasis on respecting the land as itself rather than as a vehicle with which humanity can gain wealth. This article briefly explores the conjunction of international environmental law history and ecological ethics in the hope that it will encourage a segue in our approach to conservation, ecology, and being. Full article

The flight capability of drones expands the surveillance area and allows drones to be mobile platforms. Therefore, it is important to estimate the kinematic state of drones. In this paper, the kinematic state of a mini drone in flight is estimated based on the video captured by its camera. A novel frame-to-frame template-matching technique is proposed. The instantaneous velocity of the drone is measured through image-to-position conversion and frame-to-frame template matching using optimal windows. Multiple templates are defined by their corresponding windows in a frame. The size and location of the windows are obtained by minimizing the sum of the least square errors between the piecewise linear regression model and the nonlinear image-to-position conversion function. The displacement between two consecutive frames is obtained via frame-to-frame template matching that minimizes the sum of normalized squared differences. The kinematic state of the drone is estimated by a Kalman filter based on the velocity computed from the displacement. The Kalman filter is augmented to simultaneously estimate the state and velocity bias of the drone. For faster processing, a zero-order hold scheme is adopted to reuse the measurement. In the experiments, two 150 m long roadways were tested; one road is in an urban environment and the other in a suburban environment. A mini drone starts from a hovering state, reaches top speed, and then continues to fly at a nearly constant speed. The drone captures video 10 times on each road from a height of 40 m at a 60-degree camera tilt angle. It will be shown that the proposed method achieves average distance errors at low meter levels after the flight. Full article

Certifying novel VTOL aircraft handling qualities (HQs) may be challenging, relying on costly and high-risk flight testing. This paper presents a methodology to establish the credibility of flight simulation models for certification by simulation, aiming to bridge the gap between the model input uncertainty and certification confidence. The core objective is to assess if a model, despite its inherent uncertainties, can reliably predict the handling quality compliance for specific flight tasks. This is achieved by quantifying the impact of input parameter uncertainties on predicted handling qualities and, crucially, by evaluating the envelope of the resulting uncertain aircraft transfer functions—scaled by a confidence ratio—against established maximum unnoticeable added dynamics boundaries. Applied to a lift + cruise VTOL model performing a deceleration-to-hover manoeuvre, the study demonstrates that while longitudinal control dynamics largely remained within MUAD limits, indicating the model’s credibility for those aspects, vertical axis dynamics coupled with longitudinal inputs for some uncertain configurations exceeded these limits, correlating with observed flight test performance variability. Readers will find a structured, quantitative approach to model validation for HQ certification by simulation, leveraging MUAD to determine if a nominal model is sufficiently representative for certification, thereby supporting safer and more efficient VTOL development. Full article

In Japan, natural disasters occur frequently. Serious disasters may cause damage to traffic networks and telecommunication infrastructures, leading to the occurrence of isolated disaster areas. In this article, unmanned aerial vehicles (UAVs) are used for data collection instead of unavailable ground-based stations in isolated disaster areas. Detailed information about the damage situation will be collected from the user equipment (UE) by a UAV through a fly–hover–fly procedure, and then will be sent to the disaster response headquarters for disaster relief. However, mission completion time minimization becomes a crucial task, considering the requirement of rapid response and the battery constraint of UAVs. Therefore, the author proposed a three-dimensional UAV flight trajectory, discussing the optimal flight altitude and placement of hovering points by transforming the original problem of K-means clustering into a location set cover problem (LSCP) that can be solved via a genetic algorithm (GA) approach. The simulation results have shown the feasibility of the proposed method to reduce the mission completion time. Full article

Unmanned tilt-rotor electric Vertical Take-Off and Landing (eVTOL) aircraft face significant control challenges during the transition from hover to forward flight, particularly when using open-source autopilot systems that rely on open-loop tilt control and static control gains. After the transition, the Total Energy Control System (TECS) becomes active in fixed-wing mode, but its default static gains often fail to correct energy imbalances, resulting in substantial altitude loss. This paper presents the Steepest Descent-based Total Energy Control System (SD-TECS), a real-time adaptive TECS framework that dynamically tunes gains using the steepest descent method to enhance post-transition altitude and airspeed regulation in unmanned tilt-rotor eVTOLs. The proposed method integrates gain adaptation directly into the TECS loop, optimizing control actions based on instantaneous flight states such as altitude and energy-rate errors. This enables improved responsiveness to nonlinear dynamics during the critical post-transition phase. Simulation results demonstrate that the SD-TECS approach significantly improves control performance compared to the default PX4 TECS, achieving a 35.5% reduction in the altitude settling time, a 57.3% improvement in the airspeed settling time, and a 66.1% decrease in the integrated altitude error. These improvements highlight the effectiveness of SD-TECS in enhancing the stability and reliability of unmanned tilt-rotor eVTOLs operating under autonomous control. Full article

Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and computers are limited. These problems affect their flying performance and stability, which is very important for their missions. In this study, we evaluated the accuracy of mini drones in indoor environments. During hovering, the drones showed an average deviation of 77.9 cm, with a standard deviation of 26.4 cm, indicating moderate stability while stationary. In simple forward flights over 3 m, the average deviation increased to 92.6 cm, which showed slight drop in accuracy during movement. For more complex flight paths, such as L-shaped and square trajectories, the deviations increased to 141 cm and 245 cm, respectively. Full article

Real-time command and data transfer are essential for autonomous underwater vehicle (AUV) motion control in underwater missions. Due to the limitations of underwater acoustic communication, which has a low data rate, this paper introduces a new control structure using underwater wireless optical communication (UWOC) to enable effective real-time command and data transfer. In this control structure, control inputs for the AUV attitude from outside of the water are transferred to the AUV for motion control, while its orientation data and visual images from the AUV camera are sent to the control station outside the water via the UWOC system. For demonstrating the performance of control action and data monitoring, an AUV is built with a constructed UWOC system, two vertical thrusters, and two horizontal thrusters. For attitude control of the AUV, an attitude heading reference system (AHRS) and a depth sensor are installed. Bi-directional communication in the UWOC system is achieved using a return-to-zero (RZ) modulation scheme for faster, longer-range data transfer. A signal processor converts sensor data received from the transmitted data. Finally, the hovering control performance of the AUV equipped with the UWOC system was experimentally evaluated in a water tank, achieving average root mean square errors (RMSEs) of 4.82° in roll, 2.49° in pitch, and 1.99 mm in depth, while simultaneously transmitting real-time motion data at 21.2 FPS with VGA-resolution images (640 × 480 pixels) at a communication rate of 1 Mbps. Full article

Traditional track-driven deep-sea nodule mining solutions significantly disrupt seabed ecosystems, making them unsuitable for commercial application. In contrast, ROV-like alternatives, such as the hovering mining vehicle, or HMV, offer substantial improvement in this regard and are deemed to be a viable way forward. This paper proposes an adaptive neural network fuzzy sliding mode controller architecture for the underwater trajectory tracking of HMV. The algorithm, named the Adaptive Radial Basis Function Neural Network Fuzzy Sliding Mode Controller (ARFSMC), replaces modeled vehicle dynamics with a radial basis function neural network (RBFNN). To enhance disturbance rejection, an adaptive mechanism is applied to the RBFNN output weighting matrix. Additionally, a fuzzy inference system (FIS) is implemented as the switching term, replacing the traditional signum function, to reduce high-frequency oscillations in the control signal. The stability of the algorithm under unknown external disturbance was confirmed via Lyapunov stability analysis. To validate the ARFSMC’s performance, three numerical simulation cases were conducted, each designed to reflect an expected operation scenario of the HMV, through which the tracking performance of the ARFSMC under time-varying system inertia is validated and benchmarked against conventional sliding mode control (CSMC) and double-loop sliding mode control (DSMC). The simulation results confirm that comparing the above two controllers, the root mean square error (RMSE) of the ARFSMC is reduced by 15.0% and 11.4%, respectively. And when comparing the CSMC, the chattering is reduced by 97.8%. Both indicate their high robustness and superior performance in tracking control. The controller development and numerical validation in this work are aimed at the trajectory tracking challenge of the HMV in deep-sea mining operation. The dynamical modeling of the vehicle is based on parameters of the HaiMa ROV. External disturbance from currents were considered as sinusoidal functions modified with random noise. Full article

Servo-free tilt-rotor UAVs decouple position and attitude control without using servos, which cuts structural weight and removes the travel limits of traditional designs. In many applications—such as aerial platform operations and airborne photogrammetry—large attitude changes are required during hover. Conventional control-allocation schemes tend to distribute thrust unevenly, making actuators prone to saturation. To overcome these challenges, we propose a thrust-balancing control-allocation strategy specifically for passive-hinge tilt-rotor octocopters. The presented method integrates min–max optimization with the force decomposition (FD) algorithm, effectively handling actuator saturation while maintaining low computational complexity. Additionally, an offline-trained neural network is employed to replace the online optimization process, enabling the complete controller to operate on the flight control board without relying on an onboard computer. Simulation and experiment results confirm the effectiveness of the proposed strategy, demonstrating enhanced control performance and its practical feasibility for real-world UAV applications. Full article

The effect of corrugated wings on the aerodynamic characteristics of a dragonfly-like hovering flapping wing is investigated using two-dimensional numerical simulations. Two types of pitch motion profiles, namely ‘sinusoidal’ and ‘trapezoidal’, are employed. The results obtained from the corrugated wings at Reynolds number Re = 2150 are then compared with the flat plate geometries to analyze the aerodynamic benefits of wing corrugation. The aerodynamic characteristics of corrugated wings are investigated quantitatively using cycle-averaged vertical force coefficient. For the qualitative investigation, time histories of vertical force coefficient, vorticity, and surface pressure distribution are used. The results reveal that the corrugated wings perform better than the flat plates in all three flapping configurations for both sinusoidal and trapezoidal pitch profiles. For a tandem wing with a sinusoidal pitch profile, the corrugated wings yield a vertical force generation nearly 14%, 22%, and 12%, higher than the flat plate geometries for ψ = 0°, 90°, and 180°, respectively. The corrugated wing sheds a relatively stronger detached counter clockwise vortex (CCWV) on the lower surface as compared to the flat plate, and hence, the vertical force is much higher for the corrugated wing. For a tandem wing with a trapezoidal pitch profile, the corrugated wings yield a vertical force generation nearly 27%, 22%, and 57%, higher than the flat plate geometries for ψ = 0°, 90°, and 180°, respectively. In corrugated wing geometry, the delayed stall mechanism is slightly postponed due to the corrugation shape’s ability to trap the vortex structures, leading to a positive effect on vertical force production. Full article

Hematoxylin and eosin (HE) staining is widely used in medical diagnosis. Stained slides provide crucial information to diagnose or monitor the progress of many diseases. Due to the large size of scanned images of whole tissues, a JPEG algorithm is commonly used for compression. This lossy compression method introduces artifacts visible as 8 × 8 pixel blocks and reduces overall quality, which may negatively impact further analysis. We propose a fully unsupervised Edge-Preserving method Increasing nucleI Clarity (EPIIC) for removing compression artifacts from whole-slide HE-stained images. The method is introduced in two versions, EPIIC and EPIIC Sobel, composed of stain deconvolution, gradient-based edge map estimation, and weighted smoothing. The performance of the method was evaluated using two image quality measures, $PSNR$ and $SSIM$, and various datasets, including BreCaHAD with HE-stained histopathological images and five other natural image datasets, and compared with other edge-preserving filtering methods and a deep learning-based solution. The impact of compression artifacts removal on the nuclei segmentation task was tested using Hover-Net and STARDIST models. The proposed methods led to improved image quality in histopathological and natural images and better segmentation of cell nuclei compared to other edge-preserving filtering methods. The biggest improvement was observed for images compressed with a low compression quality factor. Compared to the method using neural networks, the developed algorithms have slightly worse performance in image enhancement, but they are superior in nuclei segmentation. EPIIC and EPIIC Sobel can efficiently remove compression artifacts, positively impacting the segmentation results of cell nuclei and overall image quality. Full article

Unmanned aerial vehicles, particularly drones, have been increasingly deployed for different tasks in the community. They have become an important part of the economic and social benefits that society is exploiting from modern technology development. However, efforts are still required to further develop technologies which can mitigate the negative impacts. Among them, drone noise is considered a major health concern for the community. The present study undertakes an experimental investigation of the effectiveness of blade modifications on drone noise in an aeroacoustic wind tunnel facility. A quadcopter drone is programmed to operate in both hover and forward flights. Three modified blade configurations, including trailing-edge serrations combined serration–finlets, and an unmodified (baseline) blade, are manufactured. The far-field noise signals are recorded by two polar microphone arrays to quantify both the magnitude and directivity. The results show that all modified blades are able to reduce the drone noise at mid-to-high frequencies in both hover and forward flights, and this leads to a noticeable reduction in the overall sound pressure level. More importantly, the combined serration–finlet configuration outperforms all the other blades. Psychoacoustic analysis is also performed using the far-field acoustic time series. Interestingly, only the serration–finlet combination demonstrates a consistent reduction in the psychoacoustic annoyance levels, suggesting that it is important to use metrics from both acoustic and psychoacoustic analysis when developing noise mitigation strategies in the socio-economic context. Full article

This study addresses the premature convergence issue of the Black-Winged Kite Algorithm (BKA) in high-dimensional optimization problems by proposing an enhanced hybrid algorithm (BKAPI). First of all, BKA provides dynamic global exploration through its hovering and dive attack strategies, while Particle Swarm Optimization (PSO) enhances local exploitation via its velocity-based search mechanism. Then, PSO enables efficient local refinement, and Differential Evolution (DE) introduces a differential mutation strategy to maintain population diversity and prevent premature convergence. Finally, the integration ensures a balanced exploration–exploitation trade-off, overcoming BKA’s sensitivity to parameter settings and insufficient local search capabilities. By combining these mechanisms, BKAPI achieves a robust balance, significantly improving convergence speed and computational accuracy. To validate its effectiveness, the performance of the enhanced hybrid algorithm is rigorously evaluated against seven other intelligent optimization algorithms using the CEC 2017 and CEC 2022 benchmark test functions. Experimental results demonstrate that the proposed integrated strategy surpasses other advanced algorithms, highlighting its superiority and strong application potential. Additionally, the algorithm’s practical utility is further confirmed through its successful application to three real-world engineering problems: welding beam design, the Himmelblau function, and visible light positioning, underscoring the effectiveness and versatility of the proposed approach. Full article