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Record W3091815193

Place revisiting for planetary rovers: An enabling technology and field testing of three mission concepts

2013· article· en· W3091815193 on OpenAlexaff
Timothy D. Barfoot, Braden Stenning, Jonathan D. Gammell, Chi Hay Tong, Colin McManus, Laszlo-Peter Berczi, G. R. Osinski, M. G. Daly, C. Dickinson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsWestern UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMars Exploration ProgramComputer scienceField (mathematics)Sample (material)Path (computing)Aerospace engineeringReal-time computingTracking (education)Controller (irrigation)SimulationSystems engineeringAstrobiologyEngineeringOperating system
DOInot available

Abstract

fetched live from OpenAlex

Abstract — Planetary rovers to date have been operated mainly in a serial mode; they are driven from one place to the next, away from the lander, and seldom return to previously visited places. We have been developing a visual navigation technique, called network of reusable paths (NRP), that can be thought of as a low-computational-cost version of simultaneous localization and mapping coupled to a path-tracking controller. The result is that a rover can be returned accurately to any place it has previously visited using only visual feedback; this enables science to be gathered from multiple sites in parallel. We will describe how NRP works, and present field test results of two mission concepts where we have made use of this technology: a lunar-sample-return scenario and a Mars-methane-hunting scenario.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.238
Teacher spread0.217 · 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
Published2013
Admission routes1
Has abstractyes

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