Anisotropic shadow-based operation assistant for a pipeline-inspection robot using a single illuminator and camera
Bibliographic record
Abstract
This paper presents an anisotropic shadow-based operation assistant method for a multilink-articulated wheeled pipeline-inspection robot by using a single illuminator and camera. By displacing the position of the illuminator relative to that of the head camera, a crescent-shaped shadow appears in the images captured in a bent pipe. The size, position, and orientation of the shadow depend on the robot's orientation around the pipe axis, and the shadow disappears in a certain robot's orientation (anisotropic shadow). Generally, as for shadow based navigation systems, disappearances of the shadow should be avoided because the robot loses its way. However, our previously developed robot (AIRo-2) adapts to a bent pipe without any control when the robot's orientation and the pathway direction of the bent pipe are aligned. By aligning those two specific orientations, we propose operation assistant system to pass through winding pipes. In this paper, the shadow region is extracted using two types of image binarization. The proposed system was experimentally verified in pipelines including seven bent pipes by applying the pathway direction of the bent pipe (obtained from the shadow) to the rolling movement of the robot.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".