Dynamic Point-to-Point Trajectory Planning of a Two-DOF Cable-Suspended Parallel Robot
Bibliographic record
Abstract
This paper presents two trajectory-planning approaches for the point-to-point motion of planar two-degree-of-freedom (dof) cable-suspended parallel mechanisms. The proposed techniques can be used to plan trajectories that extend beyond the static workspace of the mechanism. Trajectories are specified as a list of target points that must be reached in sequence, with a zero velocity at each of the target points. In the first technnique, polynomial trajectories are designed to connect the target points, while the second approach uses trigonometric functions. Both techniques ensure continuity of the accelerations. Based on the dynamic model of the robot, algebraic inequalities are obtained that represent the constraints on cable tensions. These inequalities are used to determine the feasibility of the planned trajectories. Polynomial trajectories must be discretized in order to verify feasibility, while trajectories that are based on trigonometric functions can be verified globally, based on a set of simple algebraic equations. Example trajectories are given in order to illustrate the approach. An experimental validation is also presented using a two-dof prototype, and two video extensions are provided to demonstrate the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".