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Record W2054002439 · doi:10.1115/1.4025861

Model-Based Control of Three Degrees of Freedom Robotic Bulldozing

2013· article· en· W2054002439 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

VenueJournal of Dynamic Systems Measurement and Control · 2013
Typearticle
Languageen
FieldEngineering
TopicBelt Conveyor Systems Engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsController (irrigation)Process (computing)Position (finance)Control theory (sociology)Blade (archaeology)EngineeringDegrees of freedom (physics and chemistry)Computer scienceControl (management)SimulationArtificial intelligenceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This brief paper investigates the control of a robotic bulldozing operation. Optimal blade position control laws were designed based on a hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. Experiments were conducted with a scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the best optimal controller demonstrated a 33% increase in the average material removal rate compared to the rule-based controller.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.176
Teacher spread0.160 · 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