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Record W4319865702 · doi:10.1109/tie.2023.3239912

An Inchworm-Snake Inspired Flexible Robotic Manipulator With Multisection SMA Actuators for Object Grasping

2023· article· en· W4319865702 on OpenAlexaff
Wuji Liu, Zhongliang Jing, Jianzhe Huang, Xiangming Dun, Lingfeng Qiao, Henry Leung, Wujun Chen

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Calgary
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsActuatorSMA*KinematicsArtificial intelligenceRobotControl engineeringController (irrigation)Computer scienceObject (grammar)EngineeringComputer visionControl theory (sociology)Control (management)

Abstract

fetched live from OpenAlex

Aiming at achieving the object grasping task, an inchworm-snake inspired flexible robotic manipulator (FRM) is presented in this article. By using both flexible and rigid materials in the robot structure, the FRM has the stability and control accuracy of a rigid robot as well as the compliant behavior of a soft robot. The piecewise-linear driving characteristic of the shape memory alloy (SMA) actuator enables the accurate establishment of the FRM's kinematic model and model-based control method. Deep learning-based eye-in-hand binocular visual perception is designed to obtain the state feedback without relying on external sensors, making it suitable to perform tasks in confined and unstructured environment. A model-based controller with the deformation planning is introduced with the aim of achieving accurate control in object grasping. The experiments demonstrate that the FRM possesses the capability of performing the object grasping with the proposed methods.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
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 science0.0000.000
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 head0.042
GPT teacher head0.261
Teacher spread0.220 · 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 designBench or experimental
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

Citations26
Published2023
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Industrial ElectronicsSame topicSoft Robotics and ApplicationsFrench-language works237,207