Parasitic Inclinations in Cable-Driven Parallel Robots using Cable Loops
Bibliographic record
Abstract
Cable-Driven Parallel Robots (CDPRs) also noted as wire-driven robots are parallel manipulators with flexible cables instead of rigid links. A CDPR consists in a base frame, a Moving-Platform (MP) and a set of cables connecting in parallel the MP to the base frame. CDPRs are well-known for their advantages over the classical parallel robots in terms of large workspace, reconfigurability, large payload capacity and high dynamic performance. In spite of all the mentioned advantages, one of the main shortcomings of the CDPRs is their limited orientation workspace. The latter drawback is mainly due to cable interferences and collisions between cables and surrounding environment. Hence, a planar four-Degree-of-Freedom (DoF) under-constrained CDPR with an articulated MP is introduced and studied in this paper. The end-effector is articulated through a cable loop, which enables the robot to obtain a modular pose determination, namely orientation and positioning. As a result, the mechanism under study has an unlimited and singularity-free orientation workspace in addition to a large translational workspace. It should be noted that some unwanted rotational motions of the moving platform, namely, parasitic inclinations, arise due to the cable loop. Finally, those parasitic inclinations are modeled and assessed for the mechanism at hand.
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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 | 0.000 |
| 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".