Robust adaptive attitude synchronisation of rigid body networks on <i>SO</i> (3)
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Bibliographic record
Abstract
In this study, the authors consider the robust adaptive attitude synchronisation problem for a network of rigid body agents using a modified version of the error function, which is recently introduced for constructing the attitude errors on SO (3). These attitude error vectors are particularly useful for networks with large initial attitude difference. They focus on devising an adaptive geometric approach to cope with situations where the inertia matrices are not available for measurement. The Frobenious norm is used as a measure for the difference between the actual values of moments of inertias and their estimated values, to construct the individual adaptive laws of agents. Compared to the previous methods for synchronisation on SO (3) such as those using quaternions, the authors' approach based on the introduced modified error function enables us to avoid any ambiguity for attitude representation. Finally, they study the robustness of the synchronisation task in the presence of external disturbances and unmodelled dynamics and propose a method to attenuate such effects. Simulation results illustrate the effectiveness of the proposed approach.
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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 | 0.000 |
| 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