Neuro-Evolutionary Control for Optimal Dynamic Soaring
Bibliographic record
Abstract
Dynamic soaring is a flying technique that extracts energy from purely horizontal winds and their vertical wind gradients and represents one of the specific strategies that is utilized by birds to minimize energy expenditure during flight. Dynamic soaring cycles allow for persistent flight in a specific area making it of particular interest in application to Small Unmanned Aerial Vehicles (SUAVs). Most trajectory studies in dynamic soaring rely on direct methods to solve the trajectory optimization problem, which do not guarantee robustness to exogenous changes, such as wind profiles variations, for practical implementation on SUAVs. In this paper we introduce the use of an Neuro Evolution of Augmented Topologies (NEAT) algorithm which evolves a neural network control structure for optimal dynamic soaring flight trajectories. Compared to existing approaches the simplest possible neural structure is obtained requiring only sparse training information. Additionally, the control obtained is simple enough to be implemented directly in real time even for simple autopilot boards and maintains a large degree of robustness to changes in wind profile conditions.
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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) | 0.001 | 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".