Modeling and Compensation of Backlash and Harmonic Drive-Induced Errors in Robotic Manipulators
Bibliographic record
Abstract
Harmonic drives, used widely in robot transmission systems, can induce significant periodic, joint-dependent position errors. Further, backlash in transmission systems, caused by wear or improper assembly, can considerably limit the overall repeatability, and therefore accuracy, of robotic manipulators. To measure the kinematic errors induced by both the harmonic drive and backlash, a laser tracker system, accurate to 10 μm at 20 m, is used to measure the end-effector position of a FANUC 200i LR Mate as its first joint is actuated randomly through ±130° (i.e., the range visible by the laser tracker). A joint-dependent model is then derived to account for the error seen in the measurements. Using a maximum likelihood estimator, the joint-dependent model coefficients and the amount of backlash are simultaneously identified. After backlash compensation is implemented, the maximum residual calculated between the nominal predicted position and the measured position of the end-effector, 0.2969 mm, is reduced by approximately 68%, to 0.0947 mm and the mean is reduced by 58% from 0.0631 to 0.0264 mm, after the error is modeled and compensation is implemented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".