Search Results (89)

The objective of this study is to design, develop, and analyze a tri-wheeled trolley integrated with a motor that incorporates adjustable and foldable features. The purpose of a trolley is to allow users to easily transport items from one place to another. However, problems arise when transporting objects across challenging surfaces, such as up a flight of stairs, using a conventional cart. This innovation uses multiple engineering skills to determine and develop the best possible design for a stair-climbing trolley. A tri-wheel mechanism is integrated into its motorized design, meticulously engineered for adjustability, ensuring compatibility with a wide range of staircase dimensions. The designed trolley was constructed considering elements and processes such as a literature review, conceptual design, concept screening, concept scoring, 3D modelling, engineering design calculations, and simulations. The trolley was tested, and the measured pulling force data were compared with the theoretical calculations. A graph of the pulling force vs. load was plotted, in which both datasets showed similar increasing trends; hence, the designed trolley worked as expected. The development of this stair-climbing trolley can benefit people living in rural areas or low-cost buildings that are not equipped with elevators and can reduce injuries among the elderly. The designed stair-climbing trolley will not only minimize the user’s physical effort but also enhance safety. On top of that, the adjustable and foldable features of the stair-climbing trolley would benefit users living in areas with limited space. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

As a key component of unmanned aerial vehicles (UAVs), the vibrational characteristics of the airframe critically impact flight safety and imaging quality. These vibrations, often generated by motor-propeller systems or aerodynamic forces, can lead to structural fatigue during flight or cause image blur in payloads like cameras. To analyze the dynamic performance of the six-rotor UAV frame, this paper develops a state-space model based on linear state-space theory, structural dynamics principles, and modal information. The Direct Current (DC) gain method is employed to reduce the number of modes, followed by frequency response analysis on the reduced modes to derive the frequency–domain transfer function between the excitation input and response output points. The contribution of each mode to the overall frequency response is evaluated, and the frequency response curve is subsequently plotted. The results indicate that the model achieves a 73-fold speed improvement with an error rate of less than 13%, thereby validating the accuracy of the six-rotor UAV frame state-space model. Furthermore, the computational efficiency has been significantly enhanced, meeting the requirements for vibration simulation analysis. The dynamic analysis approach grounded in state-space theory offers a novel methodology for investigating the dynamic performance of complex structures, enabling efficient and precise analysis of frequency response characteristics in complex linear systems such as electric vehicle (EV) battery modules and motor systems. By treating EV components as dynamic systems with coupled mechanical–electrical interactions, this method contributes to the reliability and safety of sustainable transportation systems, addressing vibration challenges in both UAVs and EVs through unified modeling principles. Full article

In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs. Full article

Spacecraft separation safety is the key characteristic of flight safety. Obtaining the velocity and distance curves of spacecraft and booster at the separation time is at the core of separation safety analysis. In order to solve the separation velocity measurement problem, this paper introduces the YOLOv8_n target detection algorithm and the circle fitting algorithm based on random sample consistency (RANSAC) to measure the separation velocity of space targets according to a space-based video obtained by a monocular camera installed on the spacecraft arrow-shaped body. Firstly, MobileNetV3 network is used to replace the backbone network of YOLOv8_n. Then, the circle fitting algorithm based on RANSAC is improved to improve the anti-interference performance and the adaptability to various light environments. Finally, by analyzing the imaging principle of the monocular camera and the results of circle feature detection, distance information is obtained, and then the measurement results of velocity are obtained. The experimental results based on a space-based video show that the YOLOv8_n target detection algorithm can detect the booster target quickly and accurately, and the improved circle fitting algorithm based on RANSAC can measure the separation speed in real time while maintaining the detection speed. The ground simulation results show that the error of this method is about 1.2%. Full article

To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. The full-process operation of aircraft within the Bay Area is analyzed to identify key operational spots. Safety domains are then established based on path conflicts arising from aircraft movements and safety conflicts caused by minimum separation distances and wake vortex effects. These domains are used to define corresponding safe operating spaces and construct an optimized operational model for the Bay Area. A multi-agent reinforcement learning algorithm is employed to solve the model, deriving an optimized stand allocation plan and Multi-Stand Grouped Operations strategy. To evaluate the effectiveness of the optimization, real flight data from the northwest Bay Area of Terminal 2 at Guangzhou Baiyun Airport are used for validation. Compared to the original stand allocation scheme, the optimized stand allocation and Multi-Stand Grouped Operations strategy reduce aircraft delay times by 62.45%, demonstrating that the proposed model effectively enhances operational efficiency in the Bay Area. Full article

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

Autonomous vehicles must make quick and accurate decisions to operate efficiently in complex and dynamic urban traffic environments, necessitating a reliable and stable learning mechanism. The proximal policy optimization (PPO) algorithm stands out among reinforcement learning (RL) methods for its consistent learning process, ensuring stable decisions under varying conditions while avoiding abrupt deviations during execution. However, the PPO algorithm often becomes trapped in a limited search space during policy updates, restricting its adaptability to environmental changes and alternative strategy exploration. To overcome this limitation, we integrated Lévy flight’s chaotic and comprehensive exploration capabilities into the PPO algorithm. Our method helped the algorithm explore larger solution spaces and reduce the risk of getting stuck in local minima. In this study, we collected real-time data such as speed, acceleration, traffic sign positions, vehicle locations, traffic light statuses, and distances to surrounding objects from the CARLA simulator, processed via Apache Kafka. These data were analyzed by both the standard PPO and our novel Lévy flight-enhanced PPO (LFPPO) algorithm. While the PPO algorithm offers consistency, its limited exploration hampers adaptability. The LFPPO algorithm overcomes this by combining Lévy flight’s chaotic exploration with Apache Kafka’s real-time data streaming, an advancement absent in state-of-the-art methods. Tested in CARLA, the LFPPO algorithm achieved a 99% success rate compared to the PPO algorithm’s 81%, demonstrating superior stability and rewards. These innovations enhance safety and RL exploration, with the LFPPO algorithm reducing collisions to 1% versus the PPO algorithm’s 19%, advancing autonomous driving beyond existing techniques. Full article

Enhancing the effectiveness of aviation engine borescope inspection is critical for flight safety. Statistics indicate that engine defects contribute to 20% of mechanical-related flight accidents, while existing defect detection and segmentation models for borescope images suffer from a low operational efficiency and suboptimal accuracy. To address these challenges, this study proposes a Visual State Space with Multi-directional Feature Fusion Mamba (VMmamba) model and constructs a real-world borescope defect dataset. First, a feature compensation module with symmetrical diagonal feature optimization fusion is developed to enhance the feature representation capabilities, expand the receptive fields, and improve the feature extraction of the model. Second, a content-aware upsampling module is introduced to restructure contextual information for complex scene understanding. Finally, the learning process is optimized by integrating Smooth L1 Loss with Focal Loss to strengthen defect recognition. The experimental results demonstrate that VMmamba achieves a 43.4% detection mAP and 36.4% segmentation mAP on our dataset, outperforming state-of-the-art models by 2.3% and 1.4%, respectively, while maintaining a 29.2 FPS inference speed. This framework provides an efficient and accurate solution for borescope defect analysis, offering significant practical value for aviation maintenance and safety-critical decision making. Full article

This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with x and y-axis errors not exceeding 0.01 m in close range, the x-axis relative error not exceeding 0.05 m, and the y-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system. Full article

Trajectory planning is a key task in unmanned aerial vehicle navigation systems. Although trajectory planning in the presence of obstacles is a well-understood problem, unknown and dynamic environments still present significant challenges. In this paper, we present a trajectory planning method for unknown and dynamic environments that explicitly incorporates the uncertainty about the environment. Assuming that the position of obstacles and their instantaneous movement are available, our method represents the environment uncertainty as a dynamic map that indicates the probability that a region might be occupied by an obstacle in the future. The proposed method first divides the free space into non-overlapping tetrahedral partitions using Delaunay triangulation. Then, a topo-graph that describes the topology of the free space and incorporates the uncertainty of the environment is created. Using this topo-graph, an initial path and a safe flight corridor are obtained. The initial safe flight corridor provides a sequence of control points that we use to optimize clamped B-spline trajectories by formulating a quadratic programming problem with safety and smoothness constraints. Using computer simulations, we show that our algorithm can successfully find a collision-free and uncertainty-aware trajectory in an unknown and dynamic environment. Furthermore, our method can reduce the computational burden caused by moving obstacles during trajectory replanning. Full article

Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments. Full article

To enhance the operational management capabilities of general aviation airports, this paper proposes a method for evaluating the runway capacity of general aviation airports based on the runway expansion system. Firstly, it provides a brief introduction to the flight rules of general aviation airports and arrival and departure flight procedures with symmetrical characteristics, which serve as a theoretical basis for establishing the runway expansion system. Subsequently, a runway expansion system that covers symmetrical flight activities such as departure and arrival under a visual flight rule and an instrument flight rule is proposed, providing a conceptual model for evaluating the runway capacity of general aviation airports. On this foundation, the classical space–time analysis model is improved to establish a single runway arrival, departure, and mixed operation capacity evaluation model for general aviation airports. Finally, the reliability and rationality of this method are verified through case evaluations and three sets of numerical experiments with symmetrical relationships. The experiments demonstrate that this method can better reflect the actual conditions of the runways at general aviation airports while ensuring flight safety, and it can provide a reference for related research. Full article

In this study, we explore the work of breathing (WoB) experienced by aviators during the Anti-G Straining Maneuver (AGSM) to improve pilot safety and performance. Traditional airflow models of WoB fail to adequately distinguish between breathing rate and inspiratory frequency, leading to potentially inaccurate assessments. This mismatch can have serious implications, particularly in critical flight situations where understanding the true respiratory workload is essential for maintaining performance. To address these limitations, we used a non-sinusoidal model that captures the complexities of WoB under high inspiratory frequencies and varying dead space conditions. Our findings indicate that the classical airflow model tends to underestimate WoB, particularly at elevated inspiratory frequencies ranging from 0.5 to 2 Hz, where resistive forces play a significant role and elastic forces become negligible. Additionally, we show that an increase in dead space, coupled with high-frequency breathing, elevates WoB, heightening the risk of dyspnea among pilots. Interestingly, our analysis reveals that higher breathing rates lead to a decrease in total WoB, an unexpected finding suggesting that refining breathing patterns could help pilots optimize their energy expenditure. This research highlights the importance of examining the relationship between alveolar ventilation, breathing rate, and inspiratory frequency in greater depth within realistic flight scenarios. These insights indicate the need for targeted training programs and adaptive life-support systems to better equip pilots for managing respiratory challenges in high-stress situations. Ultimately, our research lays the groundwork for enhancing respiratory support for aviators, contributing to safer and more efficient flight operations. Full article