Kinematic calibration of a 3‐DOF planar parallel robot
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe a calibration method developed to improve the absolute accuracy of a novel three degrees‐of‐freedom planar parallel robot. The robot is designed for the precise alignment of semiconductor wafers and, even though its complete workspace is slightly larger, the accuracy improvements are performed within a target workspace, in which the positions are on a disc of 170 mm in diameter and the orientations are in the range ±17°. Design/methodology/approach The calibration method makes use of a single optimization model, based on the direct kinematic calibration approach, while the experimental data are collected from two sources. The first source is a measurement arm from FARO Technologies, and the second is a Mitutoyo coordinate measurement machine (CMM). The two sets of calibration results are compared. Findings Simulation confirmed that the model proposed is not sensitive to measurement noise. An experimental validation on the CMM shows that the absolute accuracy inside the target workspace was improved by reducing the maximum position and orientation errors from 1.432 mm and 0.107°, respectively, to 0.044 mm and 0.009°. Originality/value This paper presents a calibration method which makes it possible to accurately identify the actual robot's base frame (base frame calibration), at the same time as identifying and compensating for geometric errors, actuator offsets, and even screw lead errors. The proposed calibration method is applied on a novel planar robot, and its absolute accuracy was found to improve to 0.044 mm.
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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 | 0.001 | 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".