Singularity Analysis and Representation of the General Gough-Stewart Platform
Bibliographic record
Abstract
In this paper, the singularity loci of the Gough-Stewart platform are studied and a graphical representation of these loci in the manipulator’s workspace is obtained. The algorithm presented is based on analytical expressions of the determinant of the Jacobian matrix, using two different approaches, namely, linear decomposition and cofactor expansion. The first approach is used to assess the effect of the architecture parameters on the nature of the singularity loci, while the second approach leads to a significant reduction of the computational complexity of the determinant. It is shown that, for a given orientation of the platform, the singularity locus in the Cartesian space is represented by a polynomial of degree three. Moreover, this polynomial equation is applied to several simplified Gough-Stewart architectures and it is shown that the expression is reduced when the base of the mechanism is coplanar and for other special geometries. A comparison with the results obtained using Grassmann geometry is then presented, which illustrates the advantages of using one single compact equation for the singularity loci. The generalization of Fichter’s singular configuration is also developed, and several observations are then made. Finally, a brief discussion on the architecture singularities is presented and a graphical representation of the singularity loci in the Cartesian workspace of the manipulator is obtained. Two examples clearly illustrate the usefulness of the results for the analysis and design of parallel manipulators.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".