Application of response surface methodology for performing kinematic calibration of a 3-PSS/S parallel kinematic mechanism
Bibliographic record
Abstract
Kinematic calibration of a robotic manipulator determines its fabricated dimensions that inevitably deviate, due to manufacturing tolerances, from the specified/designed geometry. Conventional calibration methods achieve this goal by deriving a system of constraint equations from the kinematic structure. Workspace coordinates and the corresponding joint space coordinates are experimentally obtained so that these constraint equations can be solved for the optimal geometric parameters that best fit the data. This paper proposes an alternative kinematic calibration method that leverages response surface methodology to obtain empirical models of direct and inverse kinematics from the same experimental data. The advantages the proposed method include nullifying the requirements of deriving constraint equations and performing joint sensor calibration. In addition, since the developed models are empirical in nature, whether closed-form solutions to the direct or the indirect model is available becomes conveniently inconsequential. Both physical and simulation experiments were conducted on a 3-PSS/S to evaluate the accuracy of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".