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Record W2322328123 · doi:10.1115/ipc2004-0682

Vertical Pipe Inspection Using Swarm of Independent Robots

2004· article· en· W2322328123 on OpenAlexaff
Daniela Pellizzari, Alejandro Ramirez‐Serrano, Giovanni C. Pettinaro

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Calgary
FundersEuropean Commission
KeywordsRobotSwarm behaviourClimbRigidity (electromagnetism)Computer scienceMobile robotWork (physics)Motion (physics)SimulationArtificial intelligenceEngineeringMechanical engineeringStructural engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The work reported here describes a solution to the problem of inside vertical pipe inspection using a swarm of independent mobile robots. The proposed solution assumes the pipe diameter to be known, however, it does not make any further assumptions concerning the height to be climbed/inspected or the material and characteristics of the pipe. The robots considered are fully independent/intelligent/autonomous units and are able to connect to each other firmly as needed so as to form diverse rigid configuration structures (swarms). By exploiting the rigidity of such connections, the robots composing a swarm climb the inner vertical walls of a pipe simply by exerting together a force towards the pipe walls and by jointly moving forward at the same speed (as a team). During the joint motion, the robots are able to monitor their peers as well as the integrity of the surface by using a small CCD camera mounted on top of each robot. If one of them detects an unusual condition, it lits up and continues its motion waiting for all the other to acknowledge the situation by litting up them-selves. When this happens, they all jointly stop preventing in this way a sudden break of the force exerted on the walls and a consequent collapse of the swarm structure. In this way diverse pipe configurations can be effectively inspected.

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: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.505

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venue2004 International Pipeline Conference, Volumes 1, 2, and 3Same topicSoft Robotics and ApplicationsFrench-language works237,207