Estimating the Behaviour of a Prismatic-Revolute Robot using a Robust Filtering Strategy
Bibliographic record
Abstract
Estimating the states of a manipulator is a challenging task as it consists of sinusoidal functions that cannot be represented by a simple, linear model. In such cases, the well-known extended Kalman filter (EKF) may not yield reliable estimates. The calculation of the Jacobian matrix, as part of the EKF, may not be straightforward and introduces errors in the nonlinear approximations. A relatively new estimation strategy called the sliding innovation filter (SIF) offers an alternative solution to the EKF. The SIF forces the estimates to be within a region of the measurements, with some differences due to system modeling. In this paper, the SIF is used to estimate the states of a robotic arm of type prismatic-revolute (PR). The results are compared with the well-known EKF. A faulty scenario is considered when the robot parameters are poorly defined. The results demonstrate the effectiveness and robustness of the SIF, and offers an alternative estimation strategy for estimating different robot types.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".