Determination and Management of Cable Interferences Between Two 6-DOF Foot Platforms in a Cable-Driven Locomotion Interface
Bibliographic record
Abstract
The intrinsic interaction of a robotic system that includes two 6-degree-of-freedom cable-driven platforms sharing a common workspace might result in cable interferences for random trajectories. This paper presents and analyzes computational methods for geometrically determining and managing these interferences for any trajectory constrained with variable loads. The algorithms considered determine which cable can be released from an active actuation state while allowing control in a minimal tension state, thereby ensuring that both platforms stay in a controllable workspace. The process of managing cable interferences constitutes a challenge as one must take into account the inherent limitations of the workspace, which not only include the possibility of interference itself, but also the geometry of the cable-driven locomotion interface (CDLI), its dynamics, the nonideal behavior of real cables, and the requirement that both platforms must be completely constrained at any time. As releasing a cable from an active actuation state might generate tension discontinuities in the other cables, this paper also proposes collision prediction schemes that are only applied to redundant actuators in order to reduce or completely eliminate such discontinuities. Finally, a simulation of a CDLI embedded as a peripheral in a virtual environment, in which the load applied on each platform comes from the wrench measured under the foot for a natural gait walking, is thoroughly analyzed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".