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Record W2328685260 · doi:10.11159/ijmem.2012.003

Kinematic Analysis and Locomotion Strategy of a Pipe Inspection Robot Concept for Operation in Active Pipelines

2012· article· en· W2328685260 on OpenAlexvenueno aff
Amr Bekhit, Abbas Dehghani, Robert C. Richardson

Bibliographic record

VenueInternational Journal of Mechanical Engineering and Mechatronics · 2012
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsPipeline transportRobotComputer scienceMarine engineeringEngineeringArtificial intelligenceMechanical engineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

A robot capable of operating in active pipelines would be of great commercial and industrial benefit. This paper outlines the requirements for such a robot and considers the advantages and disadvantages of existing systems. A new design for an inchworm robot based on the Gough-Stewart parallel platform is presented. The use of inchworm locomotion keeps the control system relatively simple, whilst the use of the Gough-Stewart platform allows the robot to benefit from the accuracy, rigidity and speed of parallel robots and provides a flexible base for any manipulators the robot may use. The design aims to provide minimal resistance to fluid flow by providing a low frontal area and by distributing the body of the robot towards the edge of the pipe, where the flow is slowest. An analysis of the robot’s degrees of freedom, a derivation of the robot’s inverse kinematics equations, and a static force analysis are presented along with a description of the robot’s proposed locomotion algorithm.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

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)0.0030.001

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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

Citations25
Published2012
Admission routes1
Has abstractyes

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