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Record W2059361384 · doi:10.1117/12.852702

Integration of a high degree of freedom robotic manipulator on a large unmanned ground vehicle

2010· article· en· W2059361384 on OpenAlexaffabout
J. Giesbrecht, Blaine Fairbrother, Jack Collier, Blake Beckman

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsUnmanned ground vehicleDoorsTerrainComputer scienceService (business)Point (geometry)SimulationEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The Multi-Agent Tactical Sentry Unmanned Ground Vehicle, developed at Defence R&D Canada - Suffield, has been in service with the Canadian Forces for five years. This tele-operated wheeled vehicle provides a capability for point detection of chemical, biological, radiological, and nuclear agents. Based on user experience, it is obvious that a manipulator capability would greatly enhance the vehicle's utility and increase its mobility in urban terrain. This paper details technical components of this development, and describes a number of trials undertaken to perform tasks with a manipulator arm such as picking up objects, opening vehicle and building doors, recording video, and creating 3D models of the environment. The lessons learned from these trials will guide further development of the technology.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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

Citations12
Published2010
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRobotics and Automated SystemsFrench-language works237,207