A MODEL FOR OPTIMIZATION AND CONTROL OF SPATIAL COMPLIANT MANIPULATORS
Bibliographic record
Abstract
This paper will present a kinetoelastic model appropriate for spatial compliant manipulators that will be used for size optimization and motion control of these devices. This model will be applicable to a class of compliant manipulators based on a parallel architecture that combines the characteristics of parallel manipulators with the low-cost, small-scale capabilities resulting from a compliant structure design. The model will address both the forward and inverse kinematic analysis of such devices, as well as form a design tool for dimensional synthesis in an optimal sense based on sensitivity to joint strain limits and manufacture, parameters that are critical in the performance of compliant manipulators. The model will then be applied to a specific compliant 3-degree-of-freedom manipulator topology to demonstrate its use in size optimization of the dimensional parameters of the selected compliant manipulator. The ability of the model to accurately solve the forward and inverse kinematics will also be evaluated through testing with the prototype. The authors provide a general discussion geared to the future implementation of this model in control of positioning compliant 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".