Exploitation of Thermals in Powered and Unpowered Flight of Autonomous Gliders
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned Aerial Gliders (UAGs), are a class fixed-wing Unmanned Aerial Vehicles (UAVs) that are particularly well-suited to applications involving long endurance and range. By relying on atmospheric energy, most commonly in the form of thermal updraft, UAGs are able to gain altitude and extend flight time while preserving on-board battery energy to further prolong the mission. While previous research has investigated autonomous soaring for UAGs, the focus has been on detecting and exploiting thermals while in gliding flight. In this work, soaring algorithms are adapted to detect the presence of thermal updraft while in either powered or gliding flight. This allows the aircraft to latch onto thermals at any point during flight and capitalize on atmospheric energy to preserve on-board battery energy. Furthermore, to generate a sink polar for use in the soaring algorithms, both analytically and experimentally, digital DATCOM is used to analyze the aerodynamics of the aircraft in the former case, and a Rauch-Tung-Striebel smoother is proposed to filter experimental flight data for postprocessing. The algorithm is subsequently integrated into the PX4 flight stack and testing is performed in a Software-in-the-Loop environment. Simulation results show the improvement in efficiency gained by using atmospheric energy through all phases of flight.
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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