Autonomous UAV Control for Low-Altitude Flight in an Urban Gust Environment
Bibliographic record
Abstract
With rapid advances in the unmanned aerial vehicle (UAV) field and their growing popularity in a wide range of civilian and commercial applications, UAV operation in urban areas is inevitable.For small-size UAVs conducting low-level flight in an urban landscape, wind disturbances pose a significant challenge.Ensuring safety while flying And then it was over.With congratulations from professors and colleagues, this doctoral research spanning over the past five years and eight months had successfully come to an end.Looking back there are many important individuals who played crucial roles in helping me reach the finish line.I will begin with a sincere thanks to Professor Joshua Marshall who most graciously recommended me to Professor Jason Etele.During my time working under the stellar supervision and mentorship of Professor Etele I have enjoyed a research environment with the freedom to explore and try new ideas.I would like to convey my utmost gratitude for your complete support and understanding throughout this endeavour.Your impressive ability to deduce control actions from flight test videos frame by frame taught me to pay attention to minute details in order to obtain meaningful results.Over many iterations you have helped me refine this thesis into its present state.A special thanks for bringing together our team of graduate students over memorable breakfast meetings, and feedback-rich progress report presentations, which helped me in improving my work over these years.Many thanks as well to Dr. Giovanni Fusina of DRDC for funding this research without which it would have been impossible for me to complete this work.I would like to acknowledge the MAE laboratory staff members, beginning with v Steve Truttmann for his friendship and overall helpfulness in conducting my laboratory teaching sessions.Stephan Biljan for helping in rotorcraft laboratory experiments and the 3D printing of replacement parts for TARA.I would also like to thank Alex Proctor and Kevin Sangster for their guidance in the machine shop
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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".