Kinematic Synthesis of Nonspherical Orientation Manipulators: Maximization of Dexterous Regular Workspace by Multiple Response Optimization
Bibliographic record
Abstract
Kinematic synthesis of a parallel manipulator refers to the systematic determination of the optimum geometry that maximizes a set of kinematic performance characteristics. Essentially, this is an optimization problem where the objective function is composed of certain kinematic performance metrics that encapsulate specific requirements. Additional constraints (e.g., choice of an actuator) limit the parameter space and thus force kinematic synthesis to find a local optimum that is consistent with all design requirements. The volume and the dexterity of the workspace characterize the kinematic performance of an orientation manipulator requiring a small form factor. In this paper, the optimum geometries of two orientation manipulators differing in limb configurations (i.e., kinematic architecture) are synthesized through the application of the efficient and statistically robust response surface methodology (RSM). To this end, a gradient-based iterative technique is employed to estimate the objective function by solving the direct kinematics of each manipulator. The optimization procedure presented in this paper begins with an arbitrarily chosen initial parameter space. A hybrid approach consisting of a space-filling and an IV-optimal (integrated variance) experiment design is employed in order to reduce the initial search space and to find appropriate regression models that adequately fit the objective function. Subsequently, the empirical models thus determined are employed to find an optimum parameter set that maximizes the objective function. This solution approach efficiently identifies the optimal manipulators for both architectures that can accommodate a prospective linear actuator capable of delivering high dynamic performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".