Simulation of automatic helicopter deck landings using nature inspired flight control
Bibliographic record
Abstract
Abstract Research studies have indicated that the optical flow parameter, time to close tau, is the basis of purposeful control in the animal world, and used by both fixed wing and helicopter pilots during manoeuvring. This parameter is defined as the instantaneous time to close a gap (spatial or force) at the current closing rate. A novel automatic flight control strategy has been developed that makes use of optical flow theory and in particular, the parameter tau. This strategy has been applied to two distinct problems; (1) the landing of a helicopter on a ship and (2) the lateral repositioning of a helicopter. The first is a challenging case because the landing of a helicopter on a ship is one of the most dangerous of all helicopter flight operations. Furthermore, helicopters are often subject to torque oscillations during rapid collective control, which increases pilot workload significantly when operating with low power margins and/or whilst performing tasks that require accurate heave control. The second case demonstrates the generality of the technique. Both automatic manoeuvres were simulated successfully within desired limits, with the novel control strategy creating a ‘natural’, smooth, tau motion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".