Neural network based control of a four rotor helicopter
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
In this paper the design and development of an intelligent controller based on neural networks for a hoverable flying robot to be capable of achieving vertical take off and landing and to be able to sustain a specified attitude is presented. The ability to be able to autonomously navigate through a predefined path was designated for a future phase. This work is different from most autonomous flying robots as it focuses on a four-propeller configuration. This is a very rare helicopter design because of its inherent instability and it is believed that an autonomous robot of this configuration has not yet been successfully developed. In addition, this project uses fixed pitch propellers instead of variable pitch rotors resulting in a greatly reduced cost and mechanical complexity. The downside is that this introduces significant additional challenges in the control. Relative stability was achieved in three axis and all the supporting modules were successfully designed and implemented. However, significant challenges were encountered including the complexities of creating a neural networks controller (NNC) to work in real-time in a slow microcontroller as well as to develop the training process.
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