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Record W2171888714 · doi:10.1109/robio.2007.4522221

Control of energetic robotic swarm systems

2007· article· en· W2171888714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsSwarm behaviourTrajectorySwarm roboticsController (irrigation)Control theory (sociology)Mobile robotComputer scienceParticle swarm optimizationRobotFeedback linearizationControl engineeringControl (management)EngineeringArtificial intelligenceAlgorithmPhysics

Abstract

fetched live from OpenAlex

An M-Member robotic swarm system is studied in this paper. A controller is developed such that a swarm of wheeled mobile robots (WMR) can follow a desired trajectory with different swarm temperature. This control approach has two separate layers. The control approach in a lower layer is feedback linearization method which makes each WMR agent follow its desired trajectory. The upper layer is a temperature controller for a virtual swarm. The virtual swarm provides reference trajectory for energetic swarm of mobile robots. It is shown that a more thorough foraging behavior can be obtained at higher swarm temperature. Finally, simulation is used to validate the results and to demonstrate the new approach.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Quick stats

Citations5
Published2007
Admission routes1
Has abstractyes

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