External Wrench Estimation for UAVs Based on Variational Bayesian Unscented Kalman Filter
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Bibliographic record
Abstract
External wrench estimation has become a necessary part in many emerging applications of the Unmanned Aerial Vehicles (UAVs) such as aerial contact tasks, tactile mapping and human-UAV interaction, etc. Since measurement noises of sensors may be unknown or time-varying, this letter proposes a novel external wrench estimator based on variational Bayesian unscented Kalman Filter (VBUKF). The VB algorithm is combined into an UKF based estimator for estimating the external wrench and unknown measurement noise covariance simultaneously. Simulations and actual experiments are conducted to verify the proposed method. The results demonstrate that the VBUKF based estimator is robust to unknown and changing measurement noise. It has a good precision of external wrench estimation, which is consistent with the force sensor.
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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 | 0.000 | 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