Design of a reconfigurable automated landing system for VTOL unmanned air vehicles
Bibliographic record
Abstract
With the release of Bombardier's redesigned vertical take-off and landing unmanned air vehicle (VTOL UAV), the company's development team was interested in exploring fresh avenues for automatically landing the craft. The focus of this redesign revolved around navigation via DGPS data. The development of three principle components were identified as being paramount to the success of the system. First was the need for an algorithm to locate an appropriate intercept point on the intended landing profile. Landing initiated the switch of navigation modes from one using GPS to one employing DGPS. This differing of sources and their respective accuracies led to position errors between expected and actual craft location, thereby necessitating the inclusion of the flight-path intercept algorithm. With the establishment of concrete target points, a corroborative effort was required between the second and third components of the autoland system to provide motion control between two arbitrary points in space. The first of the two, a trajectory generator, provides an ideal locus of points based on a time law, paying careful attention to the craft's acceleration. A controller using the ideal points generated by the trajectory generator drives the craft and was the second component of the motion control system. The controller configuration was kept simple, due in no small part to the project's scope. The initial evaluation tool for theory development was a simplified version of Bombardier's overall craft dynamics model for the CL-327. This was then followed by tests with a high-fidelity model. Currently, flight testing is in progress.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".