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Record W2763529930 · doi:10.1109/lra.2017.2759789

Cooperative Continuum Robots: Concept, Modeling, and Workspace Analysis

2017· article· en· W2763529930 on OpenAlexafffund
Amir Lotfavar, Shahir Hasanzadeh, Farrokh Janabi‐Sharifi

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWorkspaceReachabilityRobotComputer scienceMathematical optimizationSimulationControl theory (sociology)MathematicsControl (management)Artificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

In this letter, we present cooperative continuum robot (CCR) concept, kineto-static analysis, and model validation. Our motivation is to provide increased reachability and maneuverability required in a wide range of continuum robot applications, such as medical interventions. We present a model for the CCR with a minimum number of unknown parameters by formulating the governing equations of each continuum robot in the local frame. The obtained equations are then reformulated in a scaled and nondimensional iterative scheme, leading to a computationally stable and efficient model of the CCR, at rates that enable real-time simulation, design optimization, and control. As a case study, a coupled catheters system is chosen to perform the experimental verifications of the proposed CCR model. Simulations are also conducted to evaluate the enhancement of the workspace and dexterity of the CCR.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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
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

Citations36
Published2017
Admission routes2
Has abstractyes

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