The Kinetostatic Conditioning of Two-Limb Schönflies Motion Generators
Bibliographic record
Abstract
This paper introduces a study on the kinetostatic conditioning of two-limb Schönflies motion generators. These are robots capable of producing the motions undergone by the end-effector of what is known as selective-compliance assembly robot arm (SCARA) systems, which can best be described as the motions of the tray of a waiter: three independent translations plus one rotation about an axis of fixed orientation. SCARA systems are usually understood as four-axis serial robots, Schönflies motion generators being a generalization thereof, that encompass first and foremost parallel architectures. Kinetostatic conditioning is understood here in connection with the condition number of each of the two Jacobian matrices of the parallel robot under study. After a brief introduction on the geometry and the kinematics of two-limb parallel systems, the kinetostatics of this class of robots is discussed; whence, the calculation of the kinetostatic conditioning of these robots is undertaken. The motivation behind this work is the need to understand an unstable behavior of the prototype in a substantial part of its workspace, which is attributed to poor conditioning. A main result of this paper is the correlation between the shortest dimension of the robot kinematic chain and the characteristic length, which hints to the need of specifying the range of the characteristic length when optimizing the dimensions of robots of the class studied here, a result that may equally hold for parallel robots in general.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".