SELF CALIBRATION OF 3-PRS MANIPULATOR WITHOUT REDUNDANT SENSORS
Bibliographic record
Abstract
In this paper a new calibration strategy that does not require any sensors beyond those used to control actuators is applied to the 3-PRS parallel manipulator. Parallel manipulators have several advantages over their serial counterparts, but have seen limited use because of low accuracy, among other reasons. Calibration allows the kinematic model that is used to control the manipulator to be adjusted to more closely replicate the physical manipulator. The architecture and kinematics of the 3-PRS are presented, as well an explanation of this new calibration strategy. The strategy makes use of direct kinematic singularities to obtain the redundant information required for calibration Implementation of the algorithms accomplished via a nested series of optimization problems, each one accomplishing a simpler stage of the overall procedure. A simulated calibration is performed, and the algorithm successfully returns the exact values used to generate the test data.
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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".