A Three-Actuator Cable-Driven Parallel Robot With a Rectangular Workspace
Bibliographic record
Abstract
Abstract In the realm of cable-driven parallel robots (CDPRs), the conventional notion entails that each cable is directly actuated by a corresponding actuator, implying a direct relationship between the number of cables and actuators. However, this article introduces a paradigm shift by contending that the number of cables should be contingent upon the desired workspace, while the number of actuators should align with the robot’s degrees-of-freedom (DoF). This novel perspective leads to an unconventional design methodology for CDPRs. Instead of commencing with the number of actuators and cables in mind, we propose an approach that begins with defining the required workspace shape and determines the requisite number of cables. Subsequently, an actuation scheme is established where each actuator can drive multiple cables. This process entails the formulation of a transmission matrix that captures the interplay between actuators and cables, followed by the mechanical implementation of the corresponding cable-pulley routing. To illustrate this approach, we provide an example involving a 2-DoF CDPR aimed at covering a rectangular workspace. Notably, the resulting wrench-closure workspace (WCW) and wench-feasible workspace (WFW) of the proposed designs exhibit favorable comparisons to existing CDPRs with more actuators.
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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 | 0.001 |
| 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".