CALIBRATION OF THE 3-PRS PARALLEL MANIPULATOR USING A MOTION CAPTURE SYSTEM
Bibliographic record
Abstract
In this paper, a kinematic calibration method for the 3-PRS parallel manipulator using a motion capture system is presented. Although parallel mechanisms present numerous advantages over their serial counterparts, an accurate kinematic model must be developed to facilitate their operation. Kinematic calibration is used to accurately determine the kinematic parameters of the kinematic model to improve the overall accuracy of the mechanism. The kinematic calibration of the 3-PRS parallel manipulator will be examined by identification of the manipulator's kinematic parameters, an introduction to the motion capture system used, and the presentation of die calibration method itself. For preliminary testing purposes, a virtual model of the manipulator has been generated in CAD to validate the calibration method. The calibration method initially determines the joint locations and orientations, from which the remaining kinematic parameters can be resolved. Preliminary testing using the virtual model indicates the method is valid and can accurately determine the modelled parameters. Once the physical manipulator is operational, alterations the calibration method will be required to account for manufacturing and assembly tolerances/errors, joint offsets and noise during the static captures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".