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Record W2126534328 · doi:10.1109/iros.2006.282637

Integrated Autonomous System for Exploration and Navigation in Underground Mines

2006· article· en· W2126534328 on OpenAlexafffund
Joseph Nsasi Bakambu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsPolytechnique Montréal
FundersAustralian Centre for Field RoboticsPolytechnique Montréal
KeywordsContext (archaeology)SupervisorNavigation systemMode (computer interface)PlannerComputer scienceReal-time computingRobotMobile robot navigationHuman–computer interactionArtificial intelligenceMobile robotGeography

Abstract

fetched live from OpenAlex

This paper describes an autonomous platform for exploration and navigation within networks of tunnels, as those typically found in underground mines and caves. In this context, we propose a system allowing two modes of operation: exploration/surveying mode and autonomous navigation mode. In the exploration mode, a remotely located supervisor instructs the platform to move through successive sections of the network, gathering range data that is then concatenated into 2D/3D survey maps of the environment. In the navigation mode, the supervisor specifies high-level missions on the previously acquired survey maps. A motion planner then translates each mission into a set of consecutive navigation actions, separated by natural landmarks. Mission execution consists of autonomously detecting landmarks, self-localizing and performing the planned navigation actions. Advanced and innovative features, mostly related to exploration capabilities, navigation mode switcher, and the integrated aspect of our system distinguish it from the systems described in the literature

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.024
GPT teacher head0.245
Teacher spread0.220 · 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

Citations10
Published2006
Admission routes2
Has abstractyes

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