2022 Advances in Science and Engineering Technology International Conferences (ASET), Год журнала: 2024, Номер unknown, С. 1 - 5
Опубликована: Июнь 3, 2024
Язык: Английский
Sensors, Год журнала: 2024, Номер 24(10), С. 3064 - 3064
Опубликована: Май 11, 2024
The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, collision prevention. structures of are generally complex, thorough hierarchies intricate connections exist between. For a comprehensive understanding design, this paper aims assess critically review the purpose-classified electronics hardware inside UAVs, each with corresponding performance metrics thoroughly analyzed. This includes an exploration different algorithms used for data processing, flight control, protection, communication. Consequently, enriches knowledge base offering informative background on various design processes, particularly those related applications. As future work recommendation, actual relevant project is openly discussed.
Язык: Английский
Процитировано
9
International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)
Опубликована: Янв. 8, 2025
Visual servoing using image registration is a method employed in robotics to control the movement of system visual information. In this context, we propose new intensity-based algorithm (IBIR) that uses information derived from images acquired at different times or views determine parameters geometric transformations needed align these images. The Arithmetic Optimization Algorithm (AOA) used optimize parameters, minimizing difference between be aligned. proposed algorithm, Intensity-Based Image Registration via Optimisation (IBIRAOA), robust data fluctuations and perturbations can avoid local optima. Simulation results prove importance efficiency terms computation time similarity aligned compared other methods based on various metaheuristics. addition, our confirm significant improvement trajectory wheeled mobile robot, thus reinforcing overall effectiveness practical navigation robotic applications.
Язык: Английский
Процитировано
1
Thin-Walled Structures, Год журнала: 2024, Номер unknown, С. 112899 - 112899
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
7
International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 137, С. 104424 - 104424
Опубликована: Фев. 15, 2025
Язык: Английский
Процитировано
0
Humanities and Social Sciences Communications, Год журнала: 2025, Номер 12(1)
Опубликована: Март 4, 2025
Язык: Английский
Процитировано
0
Engineering Reports, Год журнала: 2025, Номер 7(3)
Опубликована: Март 1, 2025
ABSTRACT This study develops and evaluates a deep learning based visual servoing (DLBVS) control system for guiding industrial robots during aircraft refueling, aiming to enhance operational efficiency precision. The employs monocular camera mounted on the robot's end effector capture images of target objects—the refueling nozzle bottom loading adapter—eliminating need prior calibration simplifying real‐world implementation. Using learning, identifies feature points these objects estimate their pose estimation, providing essential data precise manipulation. proposed method integrates two‐stage neural networks with Efficient Perspective‐n‐Point (EPnP) principle determine orientation rotation angles, while an approximation point errors calculates linear positions. DLBVS effectively commands robot arm approach interact targets, demonstrating reliable performance even under positional deviations. Quantitative results show translational below 0.5 mm rotational 1.5° both adapter, showcasing system's capability intricate operations. work contributes practical, calibration‐free solution enhancing automation in aerospace applications. videos sets from research are publicly accessible at https://tinyurl.com/CiRAxDLBVS .
Язык: Английский
Процитировано
0
Drones, Год журнала: 2025, Номер 9(4), С. 250 - 250
Опубликована: Март 26, 2025
This paper presents a methodology for training Deep Learning model aimed at flight management tasks in fixed-wing unmanned aerial vehicle (UAV), specifically autopilot control and GPS prediction. In this formulation, sensor data the most recent signal are first processed by an LSTM to produce initial coordinate preliminary estimate is then merged with additional inputs passed MLP, which replaces conventional algorithm generating commands real-time navigation. The approach particularly valuable scenarios where aircraft must follow predetermined route—such as surveillance operations—or maintain remote ground link under varying availability. study focuses on Class I UAVs; however, proposed can be adapted larger classes (II III) adjusting configurations network parameters. To collect data, small was instrumented record kinematic inputs, served neural network. Despite limited suite use of open-source controller (SpeedyBee), flexibility allows easy adaptation more complex UAVs equipped sensors, potentially improving prediction accuracy. performance network, implemented PyTorch, evaluated comparing its predicted actual logs. addition, method has been shown robust both short prolonged outages, it relies waypoint-based navigation along previously explored routes, ensuring reliable known operational contexts.
Язык: Английский
Процитировано
0
International Journal of Dynamics and Control, Год журнала: 2025, Номер 13(4)
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
International Journal of Intelligent Robotics and Applications, Год журнала: 2024, Номер unknown
Опубликована: Июнь 28, 2024
Язык: Английский
Процитировано
1
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0