Robust control of aerial vehicle flight: Simulation and experimental results
Bibliographic record
Abstract
A lot of work has been carried out over the last decade on the automation of helicopter fight. Recent developments in computer and sensor technology have made the control of miniature flying robots, such as minihelicopters, possible. The automatic fight of the miniature helicopters emerged with modern aviation and has evolved over time to satisfy the increasingly restrictive needs. It can be used when a task is too repetitive or too difficult. The objective of this automated fight is to control the aerial behavior of the miniature helicopters in order to manage the natural risks of the environment (measurement of air pollution) and to increase the safety areas (surveillance of the airspace, urban, and interurban traffic). A helicopter is a complex mechanical system with strongly nonlinear characteristics; therefore, understanding the fight's behavior is essential to ensuring its proper control. Nowadays, model helicopters are widely available for many academic and commercial purposes. The ability to describe and explain various phenomena involved in the interaction of helicopter dynamics has a large impact in practice. Consequently, the aim of this type of modeling is to adequately evaluate and achieve flawless control of an aerodynamic fight as soon as possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".