Bibliographic record
Abstract
Purpose The aim of this paper is to develop a redundancy‐resolution (RR) algorithm to optimize the joint space trajectory of the six‐rotation‐axis industrial robot as performing arc‐welding tasks. Design/methodology/approach The rotation of the tool around its symmetry axis is clearly irrelevant to the view of the task to be accomplished besides some exceptional situations. When performed with a general 6‐degrees‐of‐freedom (DOF) manipulator, there exists one DOF of redundancy that remains. By taking advantage of the symmetry axis of the welding electrode, the authors decompose the required instantaneous twist of the electrode into two orthogonal components, one lying into the relevant task subspace and one into the redundant task subspace, respectively. Joint‐limits and singularity avoidance are considered as the optimization objectives. Findings The twist‐decomposition algorithm is able to optimize effectively the joint space trajectory. It has been tested and demonstrated in simulation. Originality/value A new RR algorithm is introduced for the six‐rotation‐axis industrial robot performing welding tasks. A new kinetostatic performance index is proposed on evaluating the kinematic quality of robotic postures. It can also be used in other applications like milling, deburing and many other tasks requiring less than 6‐DOF in tool frame.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".