MétaCan
Menu
Back to cohort
Record W2960274683

Indoor Robot Localisation with Active RFID

2012· article· en· W2960274683 on OpenAlexvenueno aff
Wei Wei, Kevin Curran

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2012
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsnot available
Fundersnot available
KeywordsRadio-frequency identificationRobotComputer scienceTracking (education)Real-time computingMobile robotIdentification (biology)Embedded systemArtificial intelligenceComputer security
DOInot available

Abstract

fetched live from OpenAlex

Radio Frequency Identification (RFID) is a technology of location determination and data capture. An RFID based system relies on the interaction between readers (also known as interrogator) and tags (transponders). Active RFID technology is suitable for tracking costly assets or moving objects such as mobile robots. Once affixed with RFID tags, a robot can be localised. However, there is a tendency for accuracy to vary greatly as well as delay in readings. Those problems may be enlarged in real time applications. This paper provides an overview of implementing RFID in precision tracking of mobile robots.

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: none
Teacher disagreement score0.738
Threshold uncertainty score0.318

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 communication0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.208 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Robotics and AutomationSame topicRobotics and Sensor-Based LocalizationFrench-language works237,207