Spatial locating of magnetic mobile robot in closed steel box girder based on UWB sensors and structural boundary
Bibliographic record
Abstract
Abstract Fatigue cracks in orthotropic steel decks pose a significant threat to bridge safety, yet traditional inspection methods are inefficient and inaccurate. To address this, we propose a spatial positioning method for magnetic mobile robots inside closed steel box girders, combining ultra-wideband ranging with structural boundary constraints. Distance measurements between anchors and a mobile tag are processed via a multispherical intersection algorithm, and the geometric boundaries of the girder are incorporated as hard constraints to optimize trajectory estimation using adaptive filtering. Experimental validation on an in-service bridge showed that static positioning errors can be controlled within 10 cm, while dynamic trajectory errors are significantly reduced after constraint optimization. The layout of anchors and robot speed were found to critically influence accuracy, with a recommended speed limit of 0.05 m s−1 for sub-decimeter precision. This method provides reliable, low-power positioning support for robotic inspection in complex steel structures.
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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) | 0.000 | 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 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".