A Novel Leader-Follower Framework for Control of Helicopter Formation
Why this work is in the frame
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Bibliographic record
Abstract
A framework for formation control of a group of autonomous helicopters is presented. We introduced two control schemes named as I - alpha and I - I, which are tailored to control the relative positions of a helicopter constrained by either one or two neighboring leaders, respectively. To stabilize the internal formation parameters of these schemes, a nonlinear model predictive controller is developed. The controller finds the future control commands by optimizing a cost function, which includes formation parameter errors among other parameters such as control forces. The gradient descent method is considered as a suitable optimizer candidate for our approach. The design steps of the I - I controller is presented in this work. By designing both the two l - alpha and I - I control schemes, any user-defined three dimensional grid pattern could be achieved by a group of autonomous helicopters.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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