Complete, Minimal and Continuous Kinematic Error Models of Perfect Multi-DOF Joints for Parallel Manipulators
Bibliographic record
Abstract
Abstract Kinematic error model plays an important role in improving the positioning accuracy of robot manipulators by kinematic calibration. In order to get a better calibration result, the error model should satisfy complete, minimal and continuous criteria. In order to meet the complete requirement, the multi degree-of-freedom (DOF) joints, such as universal or spherical joint in parallel robots, have to be regarded as serial chains formed by multiple independent single DOF joints, such that the manufacturing errors of these joints can be considered. However, several previous work found that these manufacturing errors for some parallel manipulators have little effect on the accuracy improvement. Besides, considering these kind of errors will cause the kinematics to be much more complicated. Therefore, under the assumptions of perfectly manufactured universal, spherical and cylinder joints, a complete, minimal and continuous (CMC) error model is presented in this paper. The identifiability of the kinematic errors of these multi-DOF joints are analytically analyzed. In order to verify the correctness and effectiveness of the proposed method, a numerical simulation of kinematic calibration is conducted on a 6-UPS parallel manipulator. The calibration result is also compared to the one derived from the error model with 138 error parameters. Since the error model and calibration methods are described uniformly, it can be applied to most parallel manipulators.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".