Gull-inspired joint-driven wing morphing allows adaptive longitudinal flight control
Bibliographic record
Abstract
Birds dynamically adapt to disparate flight behaviours and unpredictable environments by actively manipulating their skeletal joints to change their wing shape. This in-flight adaptability has inspired many unmanned aerial vehicle (UAV) wings, which predominately morph within a single geometric plane. By contrast, avian joint-driven wing morphing produces a diverse set of non-planar wing shapes. Here, we investigated if joint-driven wing morphing is desirable for UAVs by quantifying the longitudinal aerodynamic characteristics of gull-inspired wing-body configurations. We used a numerical lifting-line algorithm (MachUpX) to determine the aerodynamic loads across the range of motion of the elbow and wrist, which was validated with wind tunnel tests using three-dimensional printed wing-body models. We found that joint-driven wing morphing effectively controls lift, pitching moment and static margin, but other mechanisms are required to trim. Within the range of wing extension capability, specific paths of joint motion (trajectories) permit distinct longitudinal flight control strategies. We identified two unique trajectories that decoupled stability from lift and pitching moment generation. Further, extension along the trajectory inherent to the musculoskeletal linkage system produced the largest changes to the investigated aerodynamic properties. Collectively, our results show that gull-inspired joint-driven wing morphing allows adaptive longitudinal flight control and could promote multifunctional UAV designs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".