ON THE IN-PIPE INSPECTION ROBOTS TRAVERSING THROUGH ELBOWS
Bibliographic record
Abstract
A general robotic mechanism was presented for in-pipe inspection oflevel pipes with varied diameter or curved pipelines. The robot employed three legs comprised of parallelogram linkages mechanism which enables adapting to various elbow joints in the piping systems. The curvatures in pipeline are the most important constraints in front of the robot through navigation process. To study the adaptability of in-pipe robots to the elbow, geometrical analysis was used to determine the minimum required diameter of an assumed resizable cylinder when it traverses through elbows. The contact points of the cylinder and the elbow are located at the medial longitudinal cross section of the elbow. However, for any designed configuration of the robots, the contact points are located at other longitudinal cross sections. For any elbow joint, a 3D space, so-called “curved pipe limited area” was defined using the minimum required width along all longitudinal cross sections in elbows. The traversing robot should be adaptable to this limited area which is a function of robot’s length, pipes’ diameter and radius of curvature. A set of computer simulation was used to verify the derived analytical equations. The verified equations in this paper enable designers to confirm the dimensions of the robots for guaranteed traversing through standard elbows in pipeline. In addition to optimizing the robot’s dimensions in designing process, the proposed equations can be used for active controlling of robot’s diameter when it traverses through elbows.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".