Quadcopter Disturbance Estimation using Different Learning Methods
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
Precise modeling of quadcopter dynamics is challenging due to the complex nature of its construction, aerodynamic effects, friction at rotors, and wind effects involved. In general analysis, these unmodeled dynamics are kept as external disturbances to the system. Machine learning techniques can effectively be used to estimate or predict the unknown kinetic effects in the quadcopter dynamical model. This paper attempts to compare the effectiveness of two popular machine learning techniques in modeling vehicle dynamics, namely neural networks (NN) and Gaussian process regression (GPR). The dynamic model of the quadcopter is expressed as a combination of a known nominal model and the unknown term, which was learned separately using the two methods. The performance of these two approaches is evaluated using a dataset collected by manually flying the AscTec Hummingbird quadcopter under an OptiTrack motion capture system. The learning process has been performed off-line, and a performance comparison between NN and GPR is discussed in the paper.
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