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Record W2104743730 · doi:10.5772/5676

Mechanical Implementation and Simulation of MoboLab, A Mobile Robot for Inspection of Power Transmission Lines

2007· article· en· W2104743730 on OpenAlexfundno aff
Mostafa Nayyerloo, Seyyed Mohammad Mehdi Yeganehparast, Alireza Barati, Mahmud Saadat Foumani

Bibliographic record

VenueInternational Journal of Advanced Robotic Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsObstacleElectric power transmissionDamperLine (geometry)RobotMobile robotSimulationPower (physics)Controller (irrigation)Computer scienceTransmission linePower transmissionTransmission (telecommunications)EngineeringElectrical engineeringArtificial intelligenceControl engineeringPhysics

Abstract

fetched live from OpenAlex

This paper describes the first phase in development of a mobile robot that can navigate aerial power transmission lines completely unattended by human operator. Its ultimate purpose is to automate inspection of power transmission lines and their equipments. The authors have developed a scaled functional model of such a mobile robot with a preliminary simple computer based on-off controller. MoboLab (Mobile Laboratory) navigates a power transmission line between two strain towers. It can maneuver over obstructions created by line equipments such as insulators, warning spheres, dampers, and spacer dampers. It can also easily negotiate the towers by its three flexible arms. MoboLab has an internal main screw which enables the robot to move itself or its two front and rear arms independently through changing gripped points. When the front arm gets close to an obstacle, the arm detaches from the line and goes down, the robot moves forward, the arm passes the obstacle and grippes the line again. In a same way another arms pass the obstacle.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.010
GPT teacher head0.318
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2007
Admission routes1
Has abstractyes

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