Autonomous UAV Control for Low-Altitude Flight in an Urban Gust Environment
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
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 in proximity to buildings and other obstacles requires a thorough understanding of the nature of these disturbances and the expected performance of an autopilot in their presence. This study focuses on the position control of a quadrotor UAV in an urban wind environment. A literature review provides an in-depth survey of the state of the art in quadrotor flight control. Urban wind conditions are modelled around a single building through a Computational Fluid Dynamics (CFD) analysis using Large Eddy Simulation (LES). Modelled transient wind flow velocities are applied to create a realistic simulation environment for a custom-built quadrotor prototype named TARA. Four different control techniques are selected and implemented for the autonomous position control of TARA. A precise simulation methodology is employed to ensure consistent flight testing under diverse representative wind conditions. The results are evaluated under a carefully-crafted set of criteria and selected performance metrics. Based on the analysis, a hybrid control scheme is proposed, with simulation and experimental data confirming its improved ability in dealing with realistic urban wind disturbances with an average position hold within a single body length.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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