Control of Unmanned Aerial Vehicle with Wing Shape Identification using Vision System and Sensor Fusion
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Bibliographic record
Abstract
This thesis presents a Deflection-Detection-Vision-System (DDVS) for unmanned aerial vehicles (UAV) fixed-wing for control and navigation. This technique allows measurement of the fixedwing shape, deflection, and identification of the aerodynamic coefficient acting on the system, using information from the stereo camera and strain gauge. It determines specific points to identify the wing's shape and deflection. The model UAV is equipped with a stereo camera fixed at the top rear end of the device and strain gauges placed at eight different points marked on the wing. Both sensors measure the deflection in chosen locations simultaneously. The DDVS performance and dynamic parameters are tested in a wind tunnel at speeds ranging from 10 km/h to 35 km/h, angles of attack (AOA), and roll angles ranging from 0 degrees to 30 degrees, respectively. An image acquisition, feature extraction, matching process, 3D reconstruction, and stereo camera calibration are presented in this thesis as a part of proposed identification procedure. This approach measures the wing deflection at each selected point and identifies the maximum deflection location based on various aerodynamic conditions such as wind speed, AOA, and roll angle. The drag and lift forces were obtained using the wing's surface area, and the experiment shows that less force is required for lifting as the AOA increases. The DDVS was implemented in a UAV and tested in the wind tunnel. Extensive experiments were conducted to determine the deflection of the wing in the function of flight parameters like angle of attack, roll angle, and flow velocity. The experimental results have shown that the integration of strain gauge and vision system sensors identify wing deflections accurately. Extensive simulation results were compared with the experimental results and demonstrated that the proposed method-based sensor fusion could be used even in the most demanding environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it