Estimation of the flexural states of a macro-micro manipulator using acceleration data
Bibliographic record
Abstract
The subject of this paper is a state-estimation algorithm which uses the twist data, velocity and angular velocity, of the base of a micro-manipulator, placed on the end-effector of its macro counterpart, to estimate the flexural states of the flexible links of the macro-manipulator. The twist data are inferred from the acceleration signals delivered by an accelerometer array $a kinematically redundant array of tri-axial accelerometers. The array signals can also be utilized to calculate the translational and angular accelerations of the micro-manipulator base, which are in turn used to obtain a set of dynamics equations for the macro-manipulator, thus reducing the order of the dynamics model. Next, the dynamics equations of the macro-manipulator and the state-output relations are linearized, the latter in closed form so as to lower the computational cost in a control loop. The relations thus obtained are then used in an extended Kalman filter to estimate the flexural states of the system.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".