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Record W2968724018 · doi:10.1109/icuas.2019.8798137

Discrete-time control of LineDrone: An assisted tracking and landing UAV for live power line inspection and maintenance

2019· article· en· W2968724018 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMultirotorTracking (education)Focus (optics)Power (physics)Transient (computer programming)Computer scienceLine (geometry)Control (management)EngineeringControl engineeringControl theory (sociology)SimulationAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the design of a discrete-time control algorithm for power line tracking and assisted landing of Hydro-Quebec's LineDrone robot, a multirotor unmanned aerial vehicle (UAV) designed to land on and roll along live power lines. The algorithm automatically aligns the UAV with the cable while the pilot remains in control of the vertical and longitudinal positions, hence facilitating landing by having fewer degrees of freedom on which the pilot must focus. Emphasis is placed on the design of the discrete-time control law, which results in a closed-form algebraic solution of gains for given transient specifications. The proposed control system is also designed to meet the requirements of operation near live lines, which means that the system is immune to electromagnetic interference. The proposed control algorithm is experimentally validated on LineDrone hybrid UAV under real outdoor conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.488

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)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.225
Teacher spread0.219 · 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

Citations34
Published2019
Admission routes2
Has abstractyes

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