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Record W2115641275 · doi:10.1109/robot.2009.5152214

Decentralized localization for dynamic and sparse robot networks

2009· article· en· W2115641275 on OpenAlexaff
Keith Y. K. Leung, Timothy D. Barfoot, Hugh H. T. Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRobotComputer scienceMobile robotNetwork topologyDistributed computingState (computer science)Information exchangeTelecommunications networkRange (aeronautics)Decentralised systemRobot kinematicsTopology (electrical circuits)Computer networkArtificial intelligenceEngineeringControl (management)AlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Finite-range sensing and communication are factors in the connectivity of a dynamic mobile robot network. State estimation becomes a difficult problem when communication connections for information exchange between all robots are not guaranteed. This paper presents a decentralized state estimation algorithm guaranteed to work in dynamic networks without connectivity requirements. We show that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible, while ensuring the same can be performed by all other robots in the network. Our technique is validated through simulations.

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: Methods · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.332

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 head0.006
GPT teacher head0.214
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
GenreMethods

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

Citations10
Published2009
Admission routes1
Has abstractyes

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