Fast and Efficient Aerial Climbing of Vertical Surfaces Using Fixed-Wing UAVs
Why this work is in the frame
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Bibliographic record
Abstract
We present improvements to Sherbrooke's multimodal autonomous drone (S-MAD), a microspine-based perching fixed-wing UAV that enables thrust-assisted climbing along vertical surfaces. Aircraft models are used to predict the performance of various aerial climb regimes and to design a controller for wall distance tracking. It is found that fast, long, and vertical climbs are favorable. Both short and long vertical autonomous climb maneuvers are demonstrated on rough surfaces (e.g., brick, roofing shingles). Results show that the S-MAD compares favorably with existing climbers, reaching a specific resistance of 19 with a much faster vertical speed (i.e., 2 m/s). A reduction in S-MAD's aerodynamic drag and an improved motor efficiency could bring its specific resistance down to 7, at a vertical speed of 5 m/s.
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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