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Record W1998319395 · doi:10.1109/ccece.2006.277523

A Wall Climbing Robotic System for Non Destructive Inspection of Above Ground Tanks

2006· article· en· W1998319395 on OpenAlexaff
Love Kalra, Wei‐Min Shen, Jason Gu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsClimbingRobotComputer scienceUnmanned ground vehicleArtificial intelligenceEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The inspection and maintenance of above ground storage tanks (AST) can be very time consuming and dangerous when performed manually. The motivation behind this paper is to simplify the task of nondestructive testing of above ground storage tanks in oil refineries and other industrial applications. The proposed robotic system consists of an autonomous mobile platform that can move on the vertical walls of the tanks carrying the testing probes, a ground station where the sensor data can be monitored for faults or internal cracks in the tank walls and the wireless communication link. In this paper we are presenting the proposed mechanical design for the robotic vehicle, the control system and a coverage algorithm to autonomously scan the cylindrical surface of the oil storage tank with the mounted sensor for fault detection. The control system consists of hierarchal control architecture with four different layers viz: task layer, behavior layer, control layer, and physical layer

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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