Kinematically-Constrained Redundant Cable-Driven Parallel Robots: Modeling, Redundancy Analysis, and Stiffness Optimization
Bibliographic record
Abstract
This paper develops a general model for kinematically-constrained redundant cable-driven parallel robots (CDPRs), and studies stiffness improving effects and redundancy resolution of such robots aiming stiffness optimization to minimize their undesired perturbations under external disturbances in a desired direction. In the developed model, assuming an axially flexible model for the cables, motion equation is derived. Considering the role of constrained cables in restriction of CDPR's rotational degrees of freedom, the vibration equation of the moving platform is separated from the equation of motion. The resulted vibration equation is a linear dynamic system with a stiffness matrix formed by the cables' tension and the constrained cables' axial stiffness. Based on that, the substantial effects of constrained cables and the potential effects of cables' tension on the stiffness improvement of CDPRs are shown. Accordingly, the cables' tension redundancy problem is formulated. Redundancy resolution is studied considering the directional stiffness of the moving platform as the objective function to maximize. This objective function is derived as a linear function of cables' redundant tensions and the corresponding redundancy problem solved by using a time-efficient method of linear programming. The developed model and the proposed redundancy resolution approach are experimentally tested on an actual warehousing robot to maximize its translational stiffness. Comparison of theoretical and experimental results demonstrates the validity of the proposed optimization approach and the effectiveness of kinematically-constrained actuation method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".