Trajectory-Planning and Normalized-Variable Control for Parallel Pick-and-Place Robots
Bibliographic record
Abstract
Trajectory planning and an efficient control scheme play a crucial role in improving the performance of pick-and-place robots. This paper introduces a novel method of trajectory planning with cycle time and path constraints. Assuming that a smooth trajectory is given, to be followed within a prescribed cycle time, the newly proposed method of trajectory planning removes the torque peaks of the actuators by a suitable scheduling of the velocity of the moving plate. Since pick-and-place robots are usually expected to meet the end poses in a certain time span, while disregarding the intermediate poses, the velocity can be tuned properly around the critical points of the trajectory by means of a time-scaling function. Moreover, the authors report the formulation of a linear quadratic regulator (LQR) controller with normalized variables to be used in conjunction with our trajectory-tracking control scheme for an in-house-developed Schönflies-motion generator. This parallel robot offers a functionally symmetric, single-loop architecture, with an isostatic kinematic chain, and virtually unlimited rotatability of its gripper. A comparison between two actuation systems developed by the authors is conducted via simulation results.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".