Towards Parallel Cable-Driven Pantographs
Bibliographic record
Abstract
We propose a new class of pantographs, i.e., of mechanisms that allow the reproduction of the displacements of an input link, the master, with an output link, the slave. The application we envision for these devices is the telemanipulation of objects from small distances, at low cost, where magnetic fields or other design constraints prohibit the use of electromechanical systems. Despite the long history of pantographs, which were invented in the 17th century, the class of pantographs proposed here is new, as it relies on parallel cable-driven mechanisms to transmit the motion. This allows the reproduction of rigid-body displacements, while previous pantographs were limited to point displacements. This important characteristic and others are described in the paper. One important challenge in the design of the proposed systems is that the cables must remain taut at all time. We address this issue by introducing nonlinear springs that passively maintain a minimum tension in the cables, while approximating static balancing of the mechanism over its workspace. Approximating static balancing allows the forces applied at the slave to reflect more accurately at the master, and vice versa. As a preliminary validation, a two-degree-of-freedom parallel cable-driven pantograph is designed. A prototype of this apparatus that does not include approximate static balancing is built, which demonstrates the working principle of these mechanisms.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".