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Record W4389540751 · doi:10.17118/11143/21107

Design and development of an autonomous mobile patrol robot

2023· article· en· W4389540751 on OpenAlexafffund
Jonathan Selvanathan, Haoxiang Lang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Ontario Institute of Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMobile robotComputer scienceRobotHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract: Autonomous mobile robots (AMRs) are especially useful for completing tasks that humans find repetitive or unsafe. An industry that would benefit from the integration of an AMR is security, where a robot could be tasked with patrolling areas to report potential concerns, hazards or violations based on the environment. To research and experiment with these possibilities, a versatile robotic platform capable of operating outdoor and indoor amongst cars and humans is required. Commercial and open-sourced AMRs are available and range in sizes, however, there is no existing mobile robotic system in the market that can be directly adopted in the application with all required capabilities. Therefore, a newly designed mobile robotic system, specifically for patrol and policing applications, is proposed in this paper. It is a robust 1/3rd scale car-like robot with a modular, and reproducible hardware architecture. The design and development of the SPR is presented as it aims to expand the possibilities of research and validation in simulation and experimentation. The test result of the designed robot showed its ability to navigate and localize in an environment, displaying its readiness for autonomy.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.699
Threshold uncertainty score0.288

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 head0.040
GPT teacher head0.271
Teacher spread0.231 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes2
Has abstractyes

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