Robust Neurocontrol for Autonomous Dynamic Soaring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1417.vid The application of dynamic soaring techniques on small unmanned aerial vehicles (SUAVs) aims to exploit naturally occurring wind gradients to increase flight endurance. However, considering the limited computational resources onboard SUAVs and the imperfect nature of real-world environments and physical systems, there is a practical need to design simple and robust control systems. As such, this paper presents a neuroevolutionary strategy for generating efficient neurocontrollers that exhibit generalized and robust behavior. The Neuroevolution of Augmenting Topologies (NEAT) algorithm is applied to evolve networks in a way that preserves simplicity while maximizing performance. Simulated flight tests in stochastic environments show that resulting controllers can perform dynamic soaring for a range of initial conditions and time-varying parameters. Flight trajectories and robustness metrics are presented and compared for a small autonomous aircraft operating in such environments.
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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