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Record W4323527350 · doi:10.23977/acss.2023.070109

Arduino-based intelligent handling robot design

2023· article· en· W4323527350 on OpenAlex
Guofeng Sun, Guangxia Bei

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.980
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
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