Development of a Mobile Robotic Platform for the Underground Distribution Lines
Bibliographic record
Abstract
This paper discusses the work accomplished for the development of a new mobile robotic platform for the Hydro-Quebecpsilas underground power distribution lines. After an overview of few past robotic systems elaborated for the distribution networks, this article details the systems of the mobile robotic platform which seems to be the first in the literature dedicated to the underground distribution lines. We present the vehicle with an articulated chassis selected for this research project to reach the site of an underground vault of the Hydro-Quebecpsilas distribution network. Then, we describe a five-degree-of-freedom hydraulic arm that is assembled on the vehicle and designed to go down in an underground vault. We present the specific tools made for the operation and maintenance of an underground switch. The different tools are handled by a six-degree-of-freedom electric manipulator with integrated electronics especially designed for this project. We discuss the geometrical architecture, the mechanical design, and the kinematic and dynamic simulations of this manipulator. The first experimental tests were successfully performed in November and December 2007 at the Hydro-Quebecpsilas Research Institute site in a simulated vault where the tasks on the underground switch were done in an automatic mode (no teleoperation) supervised by the operator.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".