MétaCan
Menu
Back to cohort
Record W2890366480 · doi:10.1109/icra.2018.8461250

LineDrone Technology: Landing an Unmanned Aerial Vehicle on a Power Line

2018· article· en· W2890366480 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMultirotorPayload (computing)Line (geometry)Descent (aeronautics)Computer scienceAutomotive engineeringFlight testPower (physics)Controller (irrigation)Monocular visionRemotely operated underwater vehicleSimulationArtificial intelligenceEngineeringAerospace engineeringRobotMobile robot

Abstract

fetched live from OpenAlex

This paper presents the design of a multirotor unmanned aerial vehicle (UAV) capable of landing semiautomatically on a power line while carrying a payload. The vehicle then rolls along the line to perform an inspection. Special attention is given to the vehicle's onboard vision system, which consists of a monocular camera and LiDAR used together to compute the pose of the vehicle relative to the power line. Landing assistance is provided to the pilot by a position-based visual controller that aligns and keeps the vehicle centered along the power line. The pilot remains in control of vertical and longitudinal movement during descent. The proposed approach was tested on a full-scale test line and shows promise for future applications of high value to the electric industry such as non-destructive testing of power transmission lines.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.248
Teacher spread0.237 · 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

Quick stats

Citations72
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicPower Line Inspection RobotsFrench-language works237,207