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Record W3183451895 · doi:10.1139/tcsme-2020-0084

Design and analysis of a tendon-driven snake-arm robot based on a spherical magnets

2021· article· en· W3183451895 on OpenAlexvenueno aff
Bei Liu, Long Huang, Lairong Yin, Peng Zhang, Kefu Yi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
FundersState Key Laboratory of Industrial Control TechnologyZhejiang UniversityNational Natural Science Foundation of China
KeywordsRobotKinematicsWorkspaceRobotic armSnake-arm robotBendingMagnetEngineeringSimulationTendonArm solutionArticulated robotRobot kinematicsComputer scienceControl theory (sociology)Structural engineeringMechanical engineeringPhysicsArtificial intelligenceMobile robotClassical mechanics

Abstract

fetched live from OpenAlex

A tendon-driven snake-arm robot can achieve a bending motion with multiple degrees of freedom (DOF) with a compact structure, which enables the robot to be widely used in confined environments. However, if a conventional tendon-driven snake-arm robot is subject to a lateral force on the distal end, it will experience passive compliance. In this paper, a 2-DOF rolling joint is proposed, based on the opposite-pole attraction of spherical magnets, which has a relatively simple structure compared with traditional joints. By serially connecting the 2-DOF rolling joints, a novel snake-arm robot is designed utilizing a tendon-driven approach. The kinematic model and workspace of the snake-arm robot are obtained, and the bending motion is validated. Based on the kinematic model, it is theoretically proven that the proposed robot can avoid passive compliance. In addition, this feature is verified through load experiments on the developed prototype.

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: none
Teacher disagreement score0.833
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

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