The Dimensional Synthesis of the Linear Delta Robot for a Force-Feedback Device
Bibliographic record
Abstract
We perform the dimensional synthesis of a parallel manipulator to be used as a force-feedback device in a virtual reality application for surgeon training in prostate brachytherapy. For such brachytherapy operations, the characteristics of the required workspace point towards the architecture of the linear DELTA robot to be used as the force-feedback device to the surgeon. In this paper, we address the dimensional synthesis of the linear DELTA robot for the prescribed workspace. To this end, we propose the minimum relative kinematic sensitivity as an objective function, a kinematic performance index that is different from most of the commonly used metrics, i.e., manipulability and dexterity. The minimum relative kinematic sensitivity represents the ratio of the minimum to the maximum effect of a unity-bounded set of actuator displacements on the moving-platform pose. These extremum sensitivities are computed independently over the prescribed workspace. Thence, the dimensional synthesis problem consists in finding the robot dimensions that maximize the minimum relative kinematic sensitivity, so it is guaranteed within a narrow interval over the prescribed workspace. This optimization problem is nonconvex, which poses a challenge from the computational point of view. However, because of symmetry in the mechanism and other simplifications, the number of optimization variables is reduced to four. This allows a reasonably fine discretization of the search domain, giving the designers confidence that the ensuing local optimum is close to the global optimum.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".