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Record W4411430879 · doi:10.1051/epjconf/202532801006

Autonomous navigation system for remote campus exploration

2025· article· en· W4411430879 on OpenAlexaff
Venkat Reddy Annapureddy

Bibliographic record

VenueEPJ Web of Conferences · 2025
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsCanada Mortgage and Housing Corporation
Fundersnot available
KeywordsObstacle avoidanceInterface (matter)Human–computer interactionRoboticsComputer scienceRobotSoftwareMobile robotMobile robot navigationObstacleRemote controlPath (computing)Motion planningArtificial intelligenceRobot controlOperating system

Abstract

fetched live from OpenAlex

A novel Autonomous Navigation System has been developed to facilitate remote campus exploration by enabling users to control a mobile robot through an intuitive web interface. The system integrates autonomous robotics with real-time remote control, allowing users to navigate across a university campus, interact with its spaces, and access real-time video feedback from the robot‘s camera. The robot‘s autonomous capabilities, including localization, path planning, and obstacle avoidance, ensure safe and accurate navigation. The web interface is responsive, device-agnostic, and does not require software installation, making it broadly accessible. This paper presents the design, implementation, and evaluation of the Virtour system, a reliable and secure telepresence platform for virtual exploration.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.030
GPT teacher head0.285
Teacher spread0.254 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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