SYNCHRONIZED TRIGONOMETRIC S-CURVE TRAJECTORY FOR JERK-BOUNDED TIME-OPTIMAL PICK AND PLACE OPERATION
Bibliographic record
Abstract
Abstract Industrial robots are predominately used in point-to-point applica-tions such as machine loading and unloading and spot welding. Asmooth and time-optimal trajectory of robot is essential for precisehandling applications. Lot of jerk-limited motion profiles are pro-posed in the literature and are classified under two approaches. Inthe first approach, the motion profiles are generated using prede-fined intermediate points called knot or control points which arespecifiedbytheuserforitsinterpolation. S-curvemotionisanotherapproach for jerk-limited motion. This paper presents an approachto generate a new synchronized jerk-bounded trigonometric S-curvetrajectory for 6 DOF robotic manipulator that has the followingfeatures: acceleration and deceleration phases follow a sine waveform of jerk profile; each phase (acceleration, constant velocity anddeceleration) of motion of all the “n joints start and end at thesame time instant (synchronized motion of the “n joints). The re-sultsofnumericalillustrationsshowthatproposedtrajectoryabletogenerate synchronized, smooth trajectory with minimum executiontime and much lesser jerk values when compared with spline-basedtrajectorieswhicharefound in literatures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".