REDUCED MOTION OF PARALLEL ROBOT MANIPULATORS DUE TO ACTIVE JOINT JAM
Bibliographic record
Abstract
In this study, the effect of active joint jam (lock) on the velocity of parallel manipulators is investigated. The reduced motion capability of parallel manipulators is discussed in light of the change occurring in the Jacobian matrix after active joint jam. A criteria equation is formulated to determine whether a given task velocity is achievable after an active joint jam. After active joint jam, the manipulator is either shut down, pending repair, or is used to complete the task after reducing the task requirements. This is done by classifying the task velocity space into a subspace in which the manipulator has to follow the desired task trajectory, and a subspace in which the manipulator could deviate from the task trajectory assuming this is permissible by the task (reducing task requirements). If the task is reduced such that the failed parallel manipulator becomes kinematically redundant relative to the reduced task requirements, optimization could be used to determine the post-failure end-effector trajectory. Optimum post-failure end-effector trajectories are discussed in terms of minimization of the end-effector velocity norm, minimization of the end-effector velocity error norm and minimization of the joint rates. The post-failure scenarios are implemented in a simulation of a 3-3 6-degree-of-freedom (6-DOF) Stewart-Gough manipulator.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".