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Record W2130750661 · doi:10.1109/icma.2005.1626882

Proposed wall climbing robot with permanent magnetic tracks for inspecting oil tanks

2006· article· en· W2130750661 on OpenAlexaff
Wei‐Min Shen, Jason Gu, Yanjun Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRobotMobile robotMechanism (biology)Robot controlTeleoperationSimulationClimbingEngineeringWorkspaceArticulated robotComputer scienceArtificial intelligenceStructural engineering

Abstract

fetched live from OpenAlex

This paper presents a proposed research on the wall climbing robot with permanent magnetic tracks. A brief review about the wall climbing robot is given, different prototypes of wall climbing robot are compared, and the different application fields are introduced. A proposed wall climbing robot with permanent magnetic adhesion mechanism for inspecting oil tanks is put forward. The mechanical system architecture is detailed in the paper. By analyzing the robot's workspace, permanent magnetic adhesion mechanism is chosen for the robot. Also, tracked locomotion mechanism is applied to the robot. By static and dynamic force analysis of the robot, design parameters about adhesion and locomotion mechanism are derived. In addition, safety constraints for the robot are obtained. Finally, the electrical system architecture for the robot is offered. To improve the efficiency, hierarchy control architecture is employed in the robot system. An embedded system is used and installed in the robot to manage multiple sensors and to communicate with the master computer through the wireless link. And the Web-based teleoperation for the robot is illustrated in the paper.

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 score0.011

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations140
Published2006
Admission routes1
Has abstractyes

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