Dynamic Point-to-Point Trajectory Planning Beyond the Static Workspace for Six-DOF Cable-Suspended Parallel Robots
Bibliographic record
Abstract
This paper proposes a point-to-point dynamic trajectory planning technique for reaching a series of poses with a six-degree-of-freedom (six-DOF) cable-suspended parallel robot. Each trajectory segment is designed to have zero translational and rotational velocity at its endpoints; transitions between segments have translational and rotational acceleration continuity. This formulation facilitates the synthesis of trajectories that extend beyond the static workspace of the robot. A basis motion is introduced, which is a mathematical function that can be adapted for each coordinate direction along each trajectory segment. Kinematic constraints are satisfied through the selection of the coefficients for this function. Dynamic constraints are imposed by defining feasible regions within the workspace for each segment endpoint, based on the previous endpoint. Spherical linear interpolation (SLERP) is used to produce singularity-free, optimally interpolated rotational trajectory segments. An experimental implementation is presented using a six-DOF prototype and a supplementary video file is included to demonstrate the 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".