Wrench Feasibility and Workspace Expansion of Planar Cable-Driven Parallel Robots by a Novel Passive Counterbalancing Mechanism
Bibliographic record
Abstract
This article focuses on the essential limitations of planar point-mass cable-driven parallel robots (CDPRs) in covering all poses of their footprint, which results in concave-shape static workspaces (SW) and also providing a zero force level on the borders of such a SW. Accordingly, a novel passive counterbalancing mechanism is proposed which not only expands SW to fully cover the footprint but also enables CDPR's platform to balance a desired minimum force magnitude in any arbitrary direction all over the SW. Maximizing such force magnitude is defined as an optimization problem which is used to find the optimal values of the proposed mechanism's design parameters. By comparing the SW of different CDPRs with and without the proposed mechanism, effectiveness of the proposed approach is demonstrated. In some examples, it is shown that the effects of the proposed method on the SW size increment is more than doubling the size and number of actuators. Finally, two experimental setups are presented and tested, where effectiveness of the proposed approach in covering the CDPRs’ footprint and also providing the desired minimum force level over the SW are demonstrated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".