Adaptive Inspection Path Planning Algorithm for Oil Pipeline Robots Driven by Fluid Kinetic Energy
Bibliographic record
Abstract
Pipelines are the core infrastructure for energy transportation, but their long-distance service and complex internal environment, such as fluid eddies, bends, branches, and sediments, pose significant challenges to the reliability and efficiency of pipeline testing. The traditional pipeline testing methods (manual testing and fixed sensor monitoring) provide low coverage, high labor costs, and adaptability to harsh environments. Although liquid driven pipeline robots do not require external power and are suitable for remote data collection, existing trajectory planning algorithms have not fully considered the dynamic characteristics of the flow field and the complexity of pipeline structures, resulting in high energy consumption, lack of recognition coverage, and poor trajectory adaptability. To address the aforementioned issues, this paper proposes an adaptive recognition route planning algorithm for liquid powered pipeline robots. The experimental results show that the algorithm has good dynamic adaptability, with a coverage rate always above 98% and energy consumption mostly below 80J, effectively improving the adaptability and recognition of liquid powered robots in complex environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".