Parameters Identification of the Path Placement Optimization Problem for a Redundant Coordinated Robotic Workcell
Bibliographic record
Abstract
This paper proposes a method to identify the number of independent parameters in order to optimize the placement of a given path for a coordinated redundant robotic workcell. The workcell consists of a generic 6 DoF serial manipulator and a 1 DoF redundancy provider (RP). The RP is not attached to the serial manipulator, but the workpiece is attached to the RP. Two cases of RPs are investigated, namely a rotary table and a linear guide. In general, 6 parameters are needed in order to place a path on the RP, and 6 parameters to place the RP in the workspace of the serial manipulator. However, because of the symmetricities and the degree of redundancy involved in the problem, not all 12 parameters can independently affect the placement operation. Therefore, it is important to identify the number of independent parameters in order to improve the efficiency of the placement optimization process. This paper presents an innovative method for determining the number of independent parameters for both cases under study, i.e., the rotary table and the linear guide, with and without considering each one’s joint limits. The optimization process is briefly introduced and the results of using all 12 parameters, as opposed to only the independent ones, are compared. Finally, the performance of the rotary table is compared to the linear guide, for a sample path.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".