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Record W2525601187 · doi:10.11159/icmie16.118

Comparison between Standard and Modified Denavit-Hartenberg Methods in Robotics Modelling

2016· article· en· W2525601187 on OpenAlexvenueno aff
M. J. García Granja, Nancy Chang, Victor de la Llana Granja, Mari Mar Duque, Fredy Llulluna

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsArtificial intelligenceRoboticsComputer scienceRobotComputer vision

Abstract

fetched live from OpenAlex

In the present article, the matrix fundamental method of Denavit and Hartenberg standard and modified are faced, after the comparison has been made, it is demonstrated that in boot cases is similar the way how the links and joints are numerated. The links are numerated in ascending order starting from 0 fixed link to n the last thing, in the same way the joints are numerated from 1 going forward, following in the same case the cinematic chain, also the matrices product is used in the same sequence of the movements made for the mobile coordinates systems. The two methods has different forms to assign coordinates systems. In the standard D-H method the origin of the system I is along the axis of the articulation i+1, and the sequence of movements is rotation and translation in Z, after that a translation and rotation along X is made. In the modified D-H method the origin of the systems i is along the joint I, and the sequence of movements is rotation and translation along X and the rotation and translation along Z.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.284
Teacher spread0.255 · 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 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

Citations21
Published2016
Admission routes1
Has abstractyes

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