Inertial analyses based on the generalized inertia matrix for parallel robots
Bibliographic record
Abstract
Analyzing the inertial properties is meaningful for parallel robots, especially those interacting with the environment. This paper provides tools for the analysis of the inertial properties of parallel robots based on the generalized inertia matrix (GIM). Since most interactions between the environment and robots happen through the mobile platform, the inertia of the whole robot reflected at the platform is considered. In this framework, the GIM is expressed in Cartesian space to yield inertial characteristics with a clear physical meaning. Then, the inertia of the whole robot is thereby reduced to an equivalent mass/inertia at the platform. Unlike for serial robots, obtaining the GIM of parallel robots in Cartesian space is complex due to the inherent closed-loop structures and the possibility of including two different types of redundancy. Two methods are proposed to solve the mentioned problems, which can simplify the derivations of the required GIMs for parallel robots. Detailed analysis and usages of the proposed methods are given based on different examples, and the results demonstrate the effectiveness of the proposed approaches.
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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 | 0.001 | 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 | 0.000 |
| 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".