Autonomous Navigation and Station-keeping of High-Altitude Balloon Using Extremum Seeking Control
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
Stratospheric high-altitude balloons are non-extensible, sealed flexible structures designed to operate in the earth's stratosphere for extended periods. These balloons do not have propulsion engines, and their dynamics are entirely based on prevailing atmospheric wind conditions, making them vulnerable to being carried by wind currents. Station-keeping involves maintaining the balloon within a specific region for an extended duration. The standard control strategy utilizes atmospheric wind velocity variability with altitude, allowing a controller to predict the altitude with favourable wind velocities. In recent years, reinforcement learning-based station-keeping controllers have gained popularity. These controllers require extensive, realistic historical atmospheric training datasets to perform effectively. In the absence of such datasets, we propose a data-driven control strategy based on dual-mode extremum-seeking control (ESC) with vanishing oscillation for the navigation and station-keeping of high-altitude balloon platforms. Through simulation studies using real wind data from the National Oceanic and Atmospheric Administration (NOAA), we demonstrated that our proposed real-time optimization algorithm can successfully steer the balloon from one location to another without explicit knowledge of the prevailing wind dynamics.
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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