Bibliographic record
Abstract
This thesis presents a new method for kinematic modeling and analysis of a six degree-of-freedom parallel robot enclosed by a number of sliding panels, called panel enclosed mechanism. This type of robots has been seen in applications where mechanisms are covered by changeable surfaces, such as aircraft morphing wings made of variable geometry truss manipulators. Based on the traditional parallel robot kinematics, the proposed method is developed to model the motions of a multiple segmented telescopic rigid panels that are attached to the moving branches of the mechanism. Through this modeling and analysis, a collision detection algorithm is proposed to analyze the collisions that could occur between adjacent sliding panels during motion over the workspace of the mechanism. This algorithm will help to design a set of permissible panels used to enclose the mechanism free of collision. A number of cases are simulated to show the effectiveness of the proposed method. In addition, an extra link is added to provide an additional degree-of-freedom. Various search methods are employed to evaluate optimal orientation angles to minimize collisions of adjacent panels. Finally, the effect of increased mobility is analyzed and validated as a potential solution to reduce panel collisions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".