MétaCan
Menu
Back to cohort
Record W2794780478 · doi:10.5539/mas.v12n4p171

A Simple and Inexpensive 3D Scanning of Remote Objects by Robot

2018· article· en· W2794780478 on OpenAlexvenueno aff
Amjad Hudieb, Saad Al-Azzam

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRobotComputer visionObject (grammar)ObstacleMobile robotArtificial intelligenceController (irrigation)Process (computing)Obstacle avoidanceRemote control3d scanningComputer hardwareGeography

Abstract

fetched live from OpenAlex

Accessing remote objects that might be hard for a human being to reach to has become feasible by the use of mobile robots, which can be equipped with numerous gadgets to facilitate the purpose of using this robot. Scanning a remote object to study it or build a model of it is one of the applications where such mobile robots can be used for. This research aims at providing a cheap and efficient robot that performs 3D scanning of remote objects in order to build a model at the controller’s station. The robot has its own obstacle avoidance and distance calculation mechanism, but it can also be remotely controlled. Images of the object are captured by two cameras; one normal camera and another Time-of-Flight dedicated camera for capturing the depth of the object. The collected fragments of the images collected by the scanning process are sent wirelessly and securely to the controller, where a CAD model is built of the scanned object’s received data. The application will be applied on Festo Robotino because of its movement that supports this job as it uses Swedish Wheel drive.

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 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicRobotics and Sensor-Based LocalizationFrench-language works237,207