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Record W4249159122 · doi:10.1504/ijmrs.2016.081116

Full-scale Atlas motion platform: structure, actuation, and control

2016· article· en· W4249159122 on OpenAlexaff
Zachary Copeland, B. Jung, M. John D. Hayes, Robert Langlois

Bibliographic record

VenueInternational Journal of Mechanisms and Robotic Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsCarleton University
Fundersnot available
KeywordsAtlas (anatomy)Scale (ratio)Motion controlComputer scienceCartographyArtificial intelligenceGeographyGeologyRobot

Abstract

fetched live from OpenAlex

This paper presents an overview of the design of the first full-scale prototype of the Atlas flight simulator motion platform for pilot training. The Atlas concept was introduced in 2005, and is unique such that orientation is decoupled from positioning, and unlimited rotations are possible about any axis of the mechanism. Detail design and manufacturing are complete, and assembly is in progress. Central to the design are three Mecanum wheels in an equilateral arrangement, which impart angular displacements to a sphere that houses the cockpit, thereby providing rotational actuation. Since the Atlas sphere rests on these Mecanum wheels, there are no joints or levers constraining its motion, allowing full 360° rotation about all axes, yielding an unbounded orientation workspace that is singularity free. In this paper, the current state of the design and assembly regarding actuation, the spherical S-glass shell, and modelling for motion control are discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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 designBench or experimental
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

Citations9
Published2016
Admission routes1
Has abstractyes

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