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Record W180524895 · doi:10.33774/miir-2022-vcbd2

Development of a Quaternion-based algorithm to process data in robotics applications

2022· preprint· en· W180524895 on OpenAlexaff
David A. Edwards, Jacqueline Ashmore, Kishor D. Bhalerao, J Robert Buchanan, Jonathan Cass, Allen B. Downey, Joseph D. Fehribach, Elena Fertig, Jérôme Grand’Maison, Nebojsa Murisic, John Nangle, J. R. Ockendon, Joel Phillips, Filippo Posta, Christopher J. Raymond

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsMcGill University
Fundersnot available
KeywordsQuaternionOrientation (vector space)Reference frameFrame (networking)Euler anglesPosition (finance)Computer visionRotation (mathematics)Gravitational fieldProcess (computing)Artificial intelligenceComputer scienceRoboticsFrame of referenceFocus (optics)RobotGeometryPhysicsMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

To implement automated control of a robot arm, it is critical to have an accurate description of the arm's position, orientation, and velocity in 3-space. Such a description can be facilitated with data about how the arm is aligned with the gravitational and magnetic fields of the Earth (the Earth frame). Using various sensors, TIAX can provide data on the orientation of the Earth's gravitational field with respect to the body frame of the robot arm, as well as the angular velocities of each axis making up the body frame. In this work, we focus on the transformation of this data from the body frame into the Earth frame. We examine the transformations in four different contexts: Euler angles, quaternions, rotations about a single axis, and transition matrices. We find that the contexts that do not rely upon angle definitions are superior. Unfortunately, consumers often demand the contexts that do.

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 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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.293
Threshold uncertainty score0.529

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 head0.036
GPT teacher head0.305
Teacher spread0.269 · 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 teacher head, not a consensus.

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

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

Citations0
Published2022
Admission routes1
Has abstractyes

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