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Record W4224280143 · doi:10.1139/tcsme-2021-0152

A novel bionic gripper based on the front tarsi of scutigers

2022· article· en· W4224280143 on OpenAlexvenueno aff
Qian Cong, Xiaojie Shi, Yu Xiong, Ju Wang, Zhen Yang, Weijun Tian

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
FundersPeople's Government of Jilin ProvinceNational Natural Science Foundation of China
KeywordsBristleGrippersDisplacement (psychology)Finite element methodBrushEngineeringAccelerationMechanical engineeringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

A novel bionic gripper with bristles is designed based on the biological structure of the front tarsi of scutigers, and its simulation model is established. It was verified that the proposed bionic gripper not only achieved the expected gripping action but also completed the pinching motion with better grasping performance. Related parameters, such as displacement, velocity, acceleration, force, and torque, were also analyzed. Friction contact finite element analysis of the bionic gripper with bristle structure was performed using ABAQUS and compared with the control group. The finite element analysis results showed that the bristles could effectively improve the capture efficiency of the bionic gripper. The diameter and density of the bristles in the bionic gripper were optimized, and their influence on the gripping efficiency was analyzed. This study provides a reference for the structural design of bionic grippers and the practical application of bionic non-smooth surfaces.

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: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.181
Teacher spread0.169 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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