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Record W2048848526 · doi:10.1109/tie.2013.2245612

A Decentralized Cooperative Control Scheme With Obstacle Avoidance for a Team of Mobile Robots

2013· article· en· W2048848526 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

VenueIEEE Transactions on Industrial Electronics · 2013
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsMobile robotRobotObstacleObstacle avoidanceRobot controlPolygon (computer graphics)Computer scienceRobot kinematicsSimulationEngineeringArtificial intelligenceComputer networkGeography

Abstract

fetched live from OpenAlex

The problem of formation control of a team of mobile robots based on the virtual and behavioral structures is considered in this paper. In the virtual structure, each mobile robot is modeled by an electric charge. The mobile robots move toward a circle, and due to repulsive forces between the identical charges, regular polygon formations of the mobile robots will be realized. For swarm formation, a virtual mobile robot is located at the center of the circle, and other mobile robots follow it. In the introduced approach, each mobile robot finds its position in the formation autonomously, and the formation can change automatically in the case of change in the number of the mobile robots. This paper also proposes a technique for avoiding obstacles based on the behavioral structure. In this technique, when a mobile robot gets close to an obstacle, while moving toward its target, a rotational potential field is applied to lead the mobile robot to avoid the obstacle, without locating in local minimum positions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.236
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