Near-Optimal Trajectory Generation for Flexible Motion Systems using Two-Boundary Approach
Bibliographic record
Abstract
The dynamics of the supposedly known flexible motion system is given a voice in the making process of the desired trajectory signals it has to follow. In doing so, and according to the herein proposed approach, a singularly perturbed version of the system dynamics is obtained which allows the system to be treated as time-invariant despite any existing time dependency. Based on the nature of the system assigned task, the trajectory making process is subdivided into several intervals, where each interval has its own boundary conditions that need to be assigned by the motion designer. In this sense, the boundary conditions act as way-points that govern the smooth states evolution over time, and are used to build internal and self-driven optimal reference trajectories to fulfill the desired actual system motion profile. Despite its simplicity, the superiority of the proposed technique is compared to the 2<sup>nd</sup>-order, 3<sup>rd</sup>-order, and sinusoidal standard motion trajectories, and its performance is evaluated through simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".