A tension distribution algorithm for cable-driven parallel robots operating beyond their wrench-feasible workspace
Bibliographic record
Abstract
One of the main concerns in the control of over-constrained cable driven parallel mechanisms is the handling of the tension distribution, which is crucial to the proper mechanism behaviour. For example, it dictates the power consumption and stiffness of the mechanism. One problem that remains to be addressed is the handling of cable tensions when the end-effector moves beyond its wrench-feasible workspace, a situation that can arise when the robot is used as a haptic interface. Most existing algorithms are capable of determining whether a specified wrench is unfeasible, but cannot return a suitable second-best tension distribution in such situations. This paper presents an algorithm based on quadratic programming that is capable of handling these situations in real time. The algorithm provides the exact tension distribution for exerting the prescribed wrench when the end-effector is inside the robot workspace. Moreover, when the end-effector is outside of the robot workspace, the algorithm returns a tension distribution that approximately generates the prescribed wrench. The effectiveness of the algorithm is first illustrated using the simulation of a simple cable-driven parallel robot (CDPR). Experimental results are then provided for an eight-cable six-degree-of-freedom CDPR using a real-time implementation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".