MétaCan
Menu
Back to cohort
Record W4323527350 · doi:10.23977/acss.2023.070109

Arduino-based intelligent handling robot design

2023· article· en· W4323527350 on OpenAlexvenueno aff
Guofeng Sun, Guangxia Bei

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsnot available
Fundersnot available
KeywordsArduinoServomotorTracingRobotMicrocontrollerComputer scienceSoftwareEmbedded systemControl engineeringCode (set theory)ServoComputer hardwareArtificial intelligenceEngineeringOperating systemProgramming languageSet (abstract data type)

Abstract

fetched live from OpenAlex

Design of a robot for autonomous reception, autonomous recognition of tasks and material handling based on Arduino control. The Arduino microcontroller is the core of the robot control, the mechanical structure design, motor drive, QR code scanning, colour recognition and other basic structures are implemented. The design, production, selection and optimisation of customised modules for tracking and tracing, DC servo motors and mechanical jaws, the control software programs and the logic for the recovery system are written in the very powerful C language for each module. After the first installation of the system was optimised, the robot was able to quickly and accurately identify the QR codes corresponding to the different handling tasks and was able to accurately handle and deliver materials of different colours according to the material handling sequence specified by the QR codes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.008

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Computer Signals and SystemsSame topicIoT-based Smart Home SystemsFrench-language works237,207