MétaCan
Menu
Back to cohort
Record W2126524312 · doi:10.1109/robot.2003.1241832

Control and data transmission for Internet robots

2004· article· en· W2126524312 on OpenAlexaff
Peter Liu, Max Q.‐H. Meng, Jason Gu, Simon X. Yang, Chao Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsUniversity of AlbertaUniversity of GuelphDalhousie UniversityCarleton University
Fundersnot available
KeywordsTeleoperationThe InternetComputer scienceJitterRobotMobile robotTeleroboticsData transmissionComputer networkRemote controlTransmission (telecommunications)Real-time computingTelecommunicationsArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

For Internet-based tele-robotic systems (Internet robots), the most challenging and distinct difficulties are associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth availability, which might lead to dramatic performance degradation or even instability. In this paper, a new approach to dealing with these problems is explored and implemented. Specifically, a rate-based end-to-end transport protocol is developed for real-time data transmission and an adaptive control scheme is developed to control the robot remotely. A mobile robot teleoperation system, ArtBot-I, is developed to verify and test the solutions. In the experiments, the users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely via the Internet using a web browser.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.268
Teacher spread0.239 · 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
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

Citations7
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicMobile Agent-Based Network ManagementFrench-language works237,207