Autonomous Fixed-Wing Aerobatics: From Theory to Flight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are increasingly being proposed for a wide range of applications. A promising new class of these vehicles, known as agile fixed-wing UAV s, is intended to bridge the gap between conventional fixed-wing aircraft, which can cover long distances efficiently, and rotorcraft, which are typically very maneuverable. This paper addresses the implementation of a controller for agile UAVs, beginning with a hardware-in-the-loop (HIL) simulator, followed by testing on a real platform, both implemented on the Pixhawk microcontroller. We replace the Xplane physics engine used in the standard Pixhawk HIL with our own in-house Matlab/Simulink high-fidelity simulation of an agile UA V. The HIL simulator is found to provide substantial advantages in the transition from pure simulation to experimental testing. Once the controller is integrated into the flight platform, flight tests are conducted, and the results of those tests are compared to those from the HIL simulation and those obtained from the pure simulation environment, for maneuvers including hover, aggressive turnaround, knife-edge, and rolling Harrier. The desired position and orientation time histories were successfully tracked with the proposed implementation, demonstrating the impressive autonomous maneuverability that can be achieved by this type of aircraft.
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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) | 0.001 | 0.002 |
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