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Record W2593477479 · doi:10.1016/j.procs.2017.01.182

Vision Based Navigation for Omni-directional Mobile Industrial Robot

2017· article· en· W2593477479 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsToronto Metropolitan University
FundersBeijing Municipal Science and Technology CommissionScience and Technology Commission of Shanghai Municipality
KeywordsComputer scienceMobile robotAerospaceWorkspaceRobotIndustrial robotMachine visionFlexibility (engineering)SimulationArtificial intelligenceComputer visionAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, an Omni-directional mobile industrial robot drilling system for aerospace manufacture is introduced. Mecanum wheels are used for the robot's maneuverability in congested workspace. An industrial robot is applied to complete the drilling work for a rocket shell. A vision system is applied to enhance the precision of mobile drilling. Additional sensor systems such as laser measurement system and displacement measurement system are equipped to do the autonomous navigation and anti-collision job. To increase the flexibility and working volume of the mobile industrial robot, the autonomous mobile drilling scheme is presented. In order to fulfil the requirement for drilling precision in aerospace industry, a vision-based deviation rectification solution is developed. Some experiments are carried out to compare the influence of different calibration targets on the robot system. Numerical tests show that the rectification system is able to satisfy the accuracy of the positioning in the autonomous drilling work.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.414

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 studies0.0010.000
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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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