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Record W2524782581 · doi:10.1109/aim.2016.7576926

Modeling and control of an intrinsic continuum robot actuated by pneumatic artificial muscles

2016· article· en· W2524782581 on OpenAlexaff
Bong-Soo Kang, Edward J. Park

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsKinematicsControl theory (sociology)TorqueRobotJacobian matrix and determinantPneumatic artificial musclesRobot kinematicsEuler anglesRobot end effectorComputer scienceEngineeringArtificial muscleControl engineeringPhysicsArtificial intelligenceClassical mechanicsMathematicsActuatorMobile robotControl (management)Geometry

Abstract

fetched live from OpenAlex

This paper presents the kinematic model of an intrinsic continuum robot actuated by pneumatic artificial muscles. The orientation of a robotic end-effector was estimated based on Euler angles considering the curvature and the arrangement of pneumatic artificial muscles on each segment of the continuum robot. Also, a torque control scheme using the Jacobian matrix was designed to give stable interaction of the continuum robot with rigid environment. Experimental results revealed that the proposed kinematic model and the torque control scheme for an intrinsic continuum robot gave good performance in predicting the orientation of the continuum robot and following the sinusoidal torque trajectory respectively.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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