Fastening tool tracking system using a Kalman filter and particle filter combination
Bibliographic record
Abstract
This paper presents a position tracking system which estimates the position of the tip of a fastening tool. The proposed system uses a Kalman filter (KF) and particle filter (PF) combination to synthesize measurements from an inertial measurement unit (IMU) and a position sensor. The KF part is used to estimate the position of the centre of mass of the tool, and the PF is used to estimate the orientation of the tool. In addition, a rule-based logic system is used to reduce angular velocity measurement error and identify the fastening action of the tool. The proposed system was validated experimentally using various scenarios representative of assembly tasks in a factory environment. The experiment results show that the proposed system can accurately identify the fastened bolt even when the angular velocity measurement is not accurate provided that a large enough number of particles is used. In addition, even when there are multiple possibilities for fastened bolt positions, the experimental results show that the proposed system can correctly identify the fastened bolt by utilizing the accumulated position error of each particle.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".