Testbed of a Novel Robotic Pitch-Roll Wrist for Parameter Identification: Modeling and Analysis
Bibliographic record
Abstract
The paper reports work in progress on the development of an innovative gearless pitch-roll wrist (PRW) for robotic applications. The PRW bears the morphology of a bevel-gear differential, its novelty lying in the absence of gears. Indeed, the PRW motivating this study is based on cams and rollers, intended to overcome the drawbacks of their bevel-gear counterparts—backlash, Coulomb friction and low stiffness. A testbed designed for parameter identification is introduced here. The paper discusses the mathematical modeling of the testbed, starting from its iconic model. The mathematical model is used to obtain the frequency response of the whole testbed, regarded as a multiple-input-multiple-output system, under the assumption that the parts of the spherical epicyclic train are rigid. The numerical values for the inertia parameters used in the model were taken from CAD models, those for stiffness and damping, as yet unknown, were estimated from a similar testbed reported elsewhere. The work ahead targets the experimental derivation of the Bode plots of the testbed, from which the numerical values of its inertia, stiffness and damping parameters are to be estimated. Moreover, having computed the stiffness and damping parameters of the testbed, the next step will be to drive the PRW at high frequencies, of the order of 1 kHz, to enable the identification of the stiffness and damping parameters of the PRW proper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".