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Record W1978540956 · doi:10.1109/tbme.2010.2066274

Analysis of Wormlike Robotic Locomotion on Compliant Surfaces

2010· article· en· W1978540956 on OpenAlexaff
David Zarrouk, Inna Sharf, Moshe Shoham

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCrawlingRobotFlexibility (engineering)Robot locomotionComputer scienceFunction (biology)Biological materialsControl engineeringSimulationEngineeringHuman–computer interactionMechanical engineeringArtificial intelligenceMobile robotRobot controlBiochemical engineeringBiologyMathematicsAnatomy

Abstract

fetched live from OpenAlex

An inherent characteristic of biological vessels and tissues is that they exhibit significant compliance or flexibility, both in the normal and tangential directions. The latter in particular is atypical of standard engineering materials and presents additional challenges for designing robotic mechanisms for navigation inside biological vessels by crawling on the tissue. Several studies aimed at designing and building wormlike robots have been carried out, but little was done on analyzing the interactions between the robots and their flexible environment. In this study, we will analyze the interaction between earthworm robots and biological tissues where contact mechanics is the dominant factor. Specifically, the efficiency of locomotion of earthworm robots is derived as a function of the tangential flexibility, friction coefficients, number of cells in the robot, and external forces.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations28
Published2010
Admission routes1
Has abstractyes

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