Design of a Planar Cable-Driven Parallel Crane Without Parasitic Tilt
Bibliographic record
Abstract
Abstract Cable-Driven Parallel Robots (CDPRs) offer high payload capacities, large translational workspace and high dynamics performances. Their rotational workspace is generally far more limited, however, which can be resolved by using cable loops, as was shown in previous research. In the case of fully-constrained CDPRs, cable loops can induce unwanted torques on the moving-platform, causing it to tilt and move away from its intended position, which we call parasitic tilt. Hence, the orientation accuracy of such robots is usually limited. This paper deals with the design, modelling and prototyping of a planar CDPR with infinite rotations, without parasitic tilt and without an additional motor. This robot, which we call a Cable-Driven Parallel Crane (CDPC), is composed of a mobile platform (MP) with an embedded mechanism and a transmission module. The MP is linked with the frame by a parallelogram of three cables to constrain its orientation, including a cable loop, as well as a fourth cable. The two-degree-of-freedom (dof) motions of the moving-platform of the CDPC and the internal dof of its embedded mechanism are actuated by a total of three actuators, which are fixed to the frame. As a consequence, the overall system is fully-actuated, its total mass and inertia in motion is reduced and it is free of parasitic tilts.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".