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Record W2126605958 · doi:10.1109/iros.2004.1389661

Code reusability tools for programming mobile robots

2005· article· en· W2126605958 on OpenAlexaff
Carle Côté, Dominic Létourneau, François Michaud, Jean-Marc Valin, Yannick Brosseau, Clément Raïevsky, Martin Lemay, Vu Tran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsReusabilityComputer scienceCode reuseMobile robotVisual programming languageReuseRobotEvent-driven programmingModular designReactive programmingExtensible programmingRoboticsCode (set theory)Software engineeringProgramming languageHuman–computer interactionProgramming paradigmInductive programmingArtificial intelligenceSoftwareEngineering

Abstract

fetched live from OpenAlex

This paper describes two initiatives aiming at improving code reusability for programming mobile robots: robotflow/flowdesigner, a data-flow programming environment; MARIE (mobile and autonomous robotics integration environment), a programming environment allowing multiple applications, programs and tools, to operate on one or multiple machines/OS and work together on a mobile robot implementation. Robotflow/flowdesigner's objective is to provide a modular, graphical programming environment that would help visualize and understand what is really happening in the robot's control loops, sensors, actuators, by using graphical probes. MARIE aims at avoiding making an exclusive choice on particular programming tools, making it possible to reuse code and applications.

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)0.002
metaresearch head score (Gemma)0.008
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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.046
GPT teacher head0.312
Teacher spread0.266 · 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
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

Citations103
Published2005
Admission routes1
Has abstractyes

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