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Record W2123129415 · doi:10.1109/robot.1993.292157

A system architecture for a mobile robot based on activities and a blackboard control unit

2002· article· en· W2123129415 on OpenAlexaff
Reda Fayek, Ramiro Liscano, G.M. Karam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsBlackboard (design pattern)Blackboard systemArchitectureComputer scienceMobile robotImplementationFunction (biology)RobotControl unitSystems architectureEmbedded systemArtificial intelligenceHuman–computer interactionComputer architectureSoftware engineeringOperating system

Abstract

fetched live from OpenAlex

The design and implementation of a system architecture for control of a mobile robot is presented based on the notion of activities that are supervised by a blackboard system. An activity is defined as an organizational unit which exhibits a basic skill. Six behavior and five design requirements are defined and are used in evaluating the performance of the system architecture. It is the author's contention that if a mobile robot is to function autonomously in an unknown environment, it must meet these requirements. Using these requirements the activity-based architecture presented can be compared to other existing systems. Actual implementations of two activities (a passageway and channel activity) are used to validate the performance of this architecture.>

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)0.001
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.226
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 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

Citations35
Published2002
Admission routes1
Has abstractyes

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