Neural network based control of a four rotor helicopter
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".