Giant Big Stik R/C UAV Computer Model Development in JSBSim for Sense and Avoid Applications
Bibliographic record
Abstract
Open-source aerospace simulation packages often lack unmanned aerial vehicles (UAVs) models, limiting the study of their interaction with other elements in the airspace. These events, which are a consequence of encounters between manned and unmanned aircraft, have recently attracted interest due to the uncertainties created by UAVs in real environments. In this paper, a fit-for-purpose flight dynamics model specific for sense and avoid (SAA) strategies in near mid-air collision scenarios is developed based on existing model development practices and adjusted from flight data. The Giant Big Stik is recognized as the representative aircraft for testing SAA manoeuvres due to its capabilities. The simulation platform is based on the JSBSim open-source flight dynamics model, and the SAA application is carried out following the current regulations and flight recommendations for UAVs in Canada. Through this methodology, the error between the real and the computer model is reduced in every step that is minimal for the SAA application. The relevance of this paper is also shown in future applications, where this model will be incorporated into more complex simulations with manned aircraft for the study of avoidance manoeuvres that will serve the safe integration of UAVs into the airspace.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".