Contribution to the optimum design of Schönflies motion generators
Bibliographic record
Abstract
There is a class of robots capable of a special type of motion, namely, those termed SCARA (Selective-Compliance Assembly Robot Arm). This class involves three independent translations and one rotation about an axis of fixed direction. Such motions are known to form a subgroup of the displacement group of rigid-body motions, termed the Schönflies subgroup. Most robots found in industry are of the serial type, but, over the years, researchers devised robots with parallel architectures, which offer increased stiffness. Moreover, with the motors installed on the fixed base, the links of parallel robots can be lighter, which allows for higher velocities and shorter cycle times. In this light, a parallel Schönflies-Motion Generator (SMG) was developed at McGill University. The McGill SMG is an innovative robot designed to provide Schönflies-motion capabilities of its mobile platform. In this work, contributions to the optimum design of Schönflies-Motion Generators are reported, in order to develop a second-generation McGill SMG prototype. First, a modified version of the orthogonal-decomposition algorithm (ODA) is introduced to solve equality-constrained optimization problems with arbitrary objective functions. This method is used to solve the optimization of the SCARA test trajectory, made by minimizing the variance of the kinetic energy of the payload. The outcome of this optimization is a trajectory that can be used as a standard to fairly compare the performance of the different SCARA systems available on the market. Then, using this optimum trajectory, the performance of the McGill SMG is evaluated using an innovative mobile platform, which was designed to enable unlimited rotation of the payload.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".