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A tailless timing belt climbing platform utilizing dry adhesives with mushroom caps

2011· article· en· W2043375561 on OpenAlexaff
Jeffrey Krahn, Yahong Liu, Alireza Sadeghi, Carlo Menon

Bibliographic record

VenueSmart Materials and Structures · 2011
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAdhesiveClimbingRobotModular designAdhesionEngineeringMaterials scienceStructural engineeringMechanical engineeringSimulationComputer scienceComposite materialArtificial intelligence

Abstract

fetched live from OpenAlex

In many instances, a climbing robot that utilizes dry adhesives as an attachment method may be found to be very useful due to the inherent nature of biomimetic fibrillar dry adhesives in the applications of space, security, surveillance and nuclear reactor cleaning and maintenance. In this paper, a novel tank-like modular robot is developed that does not require a tail to provide a preload to the front of the robot while climbing. Biomimetic fibrillar dry adhesives with mushroom caps manufactured into belts are used as an attachment method. The manufacturing of the dry adhesive belts is discussed and the adhesion properties are examined. The timing belt based climbing platform (TBCP-II) utilizes two tank-like modules connected with an active joint with continual surface–robot distance measuring providing feedback for active adhesive preloading. The mechanical, electronic and software design is discussed. Reliable vertical surface climbing is achieved and the preloading strategy and response is examined. TBCP-II is shown to be capable of both horizontal to vertical and vertical to horizontal surface transfers over both inside and outside corners.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.001

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 head0.027
GPT teacher head0.218
Teacher spread0.191 · 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

Citations67
Published2011
Admission routes1
Has abstractyes

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