 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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- Co-TS: Design and Implementation of a 2-UAV Cooperative Transportation
System
Authors: Qian Li Weng, Guan Jun Liu, Pei Zhou, Hao Ran Shi, Kai Wen Zhang
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
UAV application research has attracted more and more attention of researchers. However, in the field of UAV transportation, especially in the case of multi-UAV cooperation, there is little research or design on the overall system of multi-UAV cooperative transportation due to the complexity of the task and many factors need to be considered. In this paper, we first introduce a 2-UAV cooperative transportation task scenario and we call it Co-TS. Then according to the scenario, we design a task planning system, which describes the entire process of 2-UAV performing transportation tasks; in order to solve the design complexity of this UAV upper-layer software, we propose a model architecture of 2-UAV transportation application, design the functions of each module and analyze the relationship between modules in the model architecture; in addition, to realize the correct execution of the 2-UAV cooperative transportation task, the task execution is divided into four states which include preparation, rising, forwarding, and landing states, and the corresponding cooperative transportation control flow algorithm is designed for four states. Finally, we develop this system and conduct experiments. The quantitative and qualitative results demonstrate the effectiveness of our method.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-02-21T06:05:21Z
DOI: 10.1177/17568293231158443
Issue No: Vol. 15 (2023)
- Airflow-based odometry for MAVs using thermal anemometers
Authors: Ze Wang, Jingang Qu, Pascal Morin
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
This article concerns airflow-based odometry for estimating MAV flight speed from airflow measurements provided by a set of thermal anemometers. Our approach relies on a Gated Recurrent Unit (GRU) based deep learning approach to extract deep features from noisy and turbulent measurement signals of triaxial thermal anemometers, in order to establish the underlying mapping between the airflow measurement and the flight speed. The proposed solution is validated on a multi-rotor MAV. The results show that the GRU-based model can effectively extract noise features and perform denoising, and compensate for induced velocity effects along the propellers’ rotation axis. As a consequence, robust prediction of the flight speed is performed, including during takeoff and landing that induce ground effects and strong variations of vertical airflow.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-13T06:53:39Z
DOI: 10.1177/17568293221148385
Issue No: Vol. 15 (2023)
- A data-driven predictive maintenance model to estimate RUL in a
multi-rotor UAS
Authors: Erkan Caner Ozkat, Oguz Bektas, Michael Juul Nielsen, Anders la Cour-Harbo
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
Unmanned Aircraft Systems (UAS) has become widespread over the last decade in various commercial or personal applications such as entertainment, transportation, search and rescue. However, this emerging growth has led to new challenges mainly associated with unintentional incidents or accidents that can cause serious damage to civilians or disrupt manned aerial activities. Machine failure makes up almost 50% of the cause of accidents, with almost 40% of the failures caused in the propulsion systems. To prevent accidents related to mechanical failure, it is important to accurately estimate the Remaining Useful Life (RUL) of a UAS. This paper proposes a new method to estimate RUL using vibration data collected from a multi-rotor UAS. A novel feature called mean peak frequency, which is the average of peak frequencies obtained at each time instance, is proposed to assess degradation. The Long Short-Term Memory (LSTM) is employed to forecast the subsequent 5 mean peak frequency values using the last 7 computed values as input. If one of the estimated values exceeds the predefined 50 Hz threshold, the time from the estimation until the threshold is exceeded is calculated as the RUL. The estimated mean peak frequency values are compared with the actual values to analyze the success of the estimation. For the 1st, 2nd, and 3rd replications, RUL results are 4 s, 10 s, and 10 s, and root mean square error (RMSE) values are 3.7142 Hz, 1.4831 Hz, and 1.3455 Hz, respectively.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-12T06:40:54Z
DOI: 10.1177/17568293221150171
Issue No: Vol. 15 (2023)
- Seeing with sound; surface detection and avoidance by sensing
self-generated noise
Authors: Simon Wilshin, Stephen Amos, Richard J Bomphrey
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
Here, we demonstrate obstacle and secondary drone avoidance capability by quadcopter drones that can perceive and react to modulation of their self-generated acoustic environment when in proximity to surfaces. A ground truth for the interpretation of self-noise was established by measuring the intrinsic, three-dimensional, acoustic signature of a drone in an anechoic chamber. This was used to design sensor arrangements and machine learning algorithms to estimate the position of external features, obstacles or another drone, within the environment. Our machine learning approach took short segments of recorded sound and their Fourier transforms, fed these into a convolutional neural network, and output the location of an obstacle or secondary drone in the environment. The convolutional layers were constructed with a suitable topology that matched the physical arrangement of the sensors. Our surface detection and avoidance algorithms were refined during tethered flight within an anechoic chamber, followed by an exercise in free flight without obstacle avoidance, and finally free flight obstacle detection and avoidance. Our acoustic sense-and-avoid capability extends to vertical and horizontal planar surfaces and tethered secondary drones.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-05T06:41:37Z
DOI: 10.1177/17568293221148377
Issue No: Vol. 15 (2023)
- Low order modeling of dynamic stall using vortex particle method and
dynamic mode decomposition
Authors: Van Duc Nguyen, Viet Dung Duong, Minh Hoang Trinh, Hoang Quan Nguyen, Dang Thai Son Nguyen
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
Low order modelings are performed in this paper, including iterative Brinkman penalized vortex method (IBVM) and data-driven dynamic mode decomposition (DMD) for dynamic stall study of symmetric airfoil. The data are extracted from IBVM as input for flow field reconstruction using combinations of DMD dominant modes, representing extracted flow features. The primary mode together with its harmonics, and the mean mode are termed to be dominant for the airfoil wake duplication at fixed angles of attack ([math]) ranging from [math] to [math]. For the dynamic stall duplication, at small and large pitching amplitudes, the nearfield and farfield vorticty contours from the DMD generally agree well with those from the IBVM. In addition, the lift coefficient from the DMD collapses well with that from the IBVM and the experiment.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-04T07:15:04Z
DOI: 10.1177/17568293221147923
Issue No: Vol. 15 (2023)
- Evaluation of drag coefficient for a quadrotor model
Authors: Gautier Hattenberger, Murat Bronz, Jean-Philippe Condomines
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
This paper focuses on the quadrotor drag coefficient model and its estimation from flight tests. Precise assessment of such a model permits the use of a quadrotor as a sensor for wind estimation purposes without the need for additional onboard sensors. Firstly, the drag coefficient has been estimated in a controlled environment via wind generator and motion capture system. Later, the evolution of the coefficient is observed for various mass and fuselage shapes. Finally, an estimation method is proposed, based on the least-squares optimization, that evaluates the drag of the quadrotor directly from outdoor flight data. The latter leads the methodology towards easier adoption in other researchers’ systems without the need for complex and expensive flight testing facilities. The accuracy of the proposed method is presented both in simulation, based on a realistic flight dynamics model, and also for real outdoor flights.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-03T11:24:04Z
DOI: 10.1177/17568293221148378
Issue No: Vol. 15 (2023)
- Indoor and outdoor in-flight odometry based solely on optic flows with
oscillatory trajectories
Authors: L Bergantin, C Coquet, J Dumon, A Negre, T Raharijaona, N Marchand, F Ruffier
Abstract: International Journal of Micro Air Vehicles, Volume 15, Issue , January-December 2023.
Estimating distance traveled is a frequently arising problem in robotic applications designed for use in environments where GPS is only intermittently or not at all available. In UAVs, the presence of weight and computational power constraints makes it necessary to develop odometric strategies based on minimilastic equipment. In this study, a hexarotor was made to perform up-and-down oscillatory movements while flying forward in order to test a self-scaled optic flow based odometer. The resulting self-oscillatory trajectory generated series of contractions and expansions in the optic flow vector field, from which the flight height of the hexarotor could be estimated using an Extended Kalman Filter. For the odometry, the downward translational optic flow was scaled by this current visually estimated flight height before being mathematically integrated to obtain the distance traveled. Here we present three strategies based on sensor fusion requiring no, precise or rough prior knowledge of the optic flow variations generated by the sinusoidal trajectory. The “rough prior knowledge” strategy is based on the shape and timing of the variations in the optic flow. Tests were performed first in a flight arena, where the hexarotor followed a circular trajectory while oscillating up and down over a distance of about [math] m under illuminances of [math] lux and [math] lux. Preliminary field tests were then performed, in which the hexarotor followed a longitudinal bouncing [math]-long trajectory over an irregular pattern of grass.
Citation: International Journal of Micro Air Vehicles
PubDate: 2023-01-03T11:10:54Z
DOI: 10.1177/17568293221148380
Issue No: Vol. 15 (2023)
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Open Access journal
