Autonomous soaring and surveillance in wind fields with an unmanned aerial vehicle
Why this work is in the frame
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Bibliographic record
Abstract
Small unmanned aerial vehicles (UAVs) play an active role in developing a low-cost, low-altitude autonomous aerial surveillance platform. The success of the applications needs to address the challenge of limited on-board power plant that limits the endurance performance in surveillance mission. This thesis studies the mechanics of soaring flight, observed in nature where birds utilize various wind patterns to stay airborne without flapping their wings, and investigates its application to small UAVs in their surveillance missions. In a proposed integrated framework of soaring and surveillance, a bird-mimicking soaring maneuver extracts energy from surrounding wind environment that improves surveillance performance in terms of flight endurance, while the surveillance task not only covers the target area, but also detects energy sources within the area to allow for potential soaring flight. The interaction of soaring and surveillance further enables novel energy based, coverage optimal path planning. Two soaring and associated surveillance strategies are explored. In a so-called static soaring surveillance, the UAV identifies spatially-distributed thermal updrafts for soaring, while incremental surveillance is achieved through gliding flight to visit concentric expanding regions. A Gaussian-process-regression-based algorithm is developed to achieve computationally-efficient and smooth updraft estimation. In a so-called dynamic soaring surveillance, the UAV performs one cycle of dynamic soaring to harvest energy from the horizontal wind gradient to complete one surveillance task by visiting from one target to the next one. A Dubins-path-based trajectory planning approach is proposed to maximize wind energy extraction and ensure smooth transition between surveillance tasks. Finally, a nonlinear trajectory tracking controller is designed for a full six-degree-of-freedom nonlinear UAV dynamics model and extensive simulations are carried to demonstrate the effectiveness of the proposed soaring and surveillance strategies.
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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