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Record W3084194898 · doi:10.32393/csme.2020.1279

Jacobian Formulation for Two Classes of Cooperative Continuum Robots

2020· article· en· W3084194898 on OpenAlexafffund
Somayeh Norouzi‐Ghazbi, Ali Mehrkish, Farrokh Janabi‐Sharifi

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJacobian matrix and determinantRobotComputer scienceControl engineeringControl theory (sociology)Artificial intelligenceMathematicsEngineeringApplied mathematicsControl (management)

Abstract

fetched live from OpenAlex

Continuum robots (CRs) are an important class of robots inspired by the biological counterparts.Recently, a new paradigm of using multiple CRs, under cooperative continuum robots (CCRs), has been introduced to increase the performance of CRs.The motivation behind this work is to provide a detailed discussion on the concept of CCRs and a detailed description of its two different sub-configurations, including Co-manipulative and Target-assist CCRs.This paper also presents kinematic modeling and Jacobian derivation of these two sub-configurations.It has been shown that kinematics of Co-manipulative CCRs could be well estimated using the DH approach; while, for deriving kinematics of the second subconfiguration, a combination of DH approach and an exact kinematic modeling approach, using Cosserat rod theory, has to be used.The obtained Jacobians could be used for control purposes and CR-based grasp synthesis.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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 designTheoretical or conceptual
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

Citations11
Published2020
Admission routes2
Has abstractyes

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Same venueProgress in Canadian Mechanical Engineering. Volume 3Same topicSoft Robotics and ApplicationsFrench-language works237,207