Kinematic characterisation of hexapods for industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose two simple tools for the kinematic characterization of hexapods. The paper also aims to share the experience of converting a popular commercial motion base (Stewart‐Gough platform, hexapod) to an industrial robot for use in heavy duty aerospace manufacturing processes. Design/methodology/approach The complete workspace of a hexapod is a six‐dimensional entity that is impossible to visualize. Thus, nearly all hexapod manufacturers simply state the extrema of each of the six dimensions, which is very misleading. As a compromise, a special 3D subset of the complete workspace is proposed, an approximation of which can be readily obtained using a computer‐aided design (CAD)/computer‐aided manufacturing (CAM) software suite, such as computer‐aided 3D interactive application (CATIA). While calibration techniques for serial robots are readily available, there is still no generally agreed procedure for calibrating hexapods. The paper proposes a simple calibration method that relies on the use of a laser tracker and requires no programming at all. Instead, the design parameters of the hexapod are directly and individually measured and the few computations involved are performed in a CAD/CAM software such as CATIA. Findings The conventional octahedral hexapod design has a very limited workspace, though free of singularities. There are important deviations between the actual and the specified kinematic model in a commercial motion base. Practical implications A commercial motion base can be used as a precision positioning device with its controller retrofitted with state‐of‐the‐art motion control technology with accurate workspace and geometric characteristics. Originality/value A novel geometric approach for obtaining meaningful measures of the workspace is proposed. A novel, systematic procedure for the calibration of a hexapod is outlined. Finally, experimental results are presented and discussed.
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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 | 0.002 | 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 | 0.001 |
| 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".