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Record W2051378666 · doi:10.1177/095965180221600307

A modification of cooperative collision avoidance for multiple mobile robots using the avoidance circle

2002· article· en· W2051378666 on OpenAlexaff
A. Fujimori, Yoshinao Ogawa, P.N. Nikiforuk

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · 2002
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCollision avoidanceMobile robotComputer scienceRobotCollisionSimulationCollision avoidance systemArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This paper presents a modification of cooperative collision avoidance for multiple mobile robots. The original cooperative collision avoidance technique by Fujimori and co-workers is briefly explained and its drawbacks are shown in the navigation simulation using a simulator of an experimental robot, Pioneer-1. To improve upon the drawbacks, an avoidance circle is introduced and a modified cooperative collision avoidance technique is proposed. The improved performance of the proposed modification is demonstrated by another navigation simulation in which the conditions are the same as those in the simulation using the original technique. For 100 trials using four mobile robots, the number of the successful cases using the original and the modified techniques was 74 and 96 respectively.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.225
Teacher spread0.202 · 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 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

Citations4
Published2002
Admission routes1
Has abstractyes

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