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Record W2274906167 · doi:10.48550/arxiv.1602.07340

A Reconfigurable USAR Robot Designed for Traversing Complex 3D Terrain

2015· preprint· en· W2274906167 on OpenAlexaff
Krispin A. Davies, Alejandro Ramirez‐Serrano

Bibliographic record

VenuearXiv (Cornell University) · 2015
Typepreprint
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTraverseRobotTerrainComputer scienceUrban search and rescueRoboticsArtificial intelligenceCricketClimbSimulationRobustness (evolution)EngineeringMobile robotAerospace engineeringGeography

Abstract

fetched live from OpenAlex

The use of robotics in Urban Search and Rescue (USAR) is growing steadily from their initial inception during the 2001 World Trade Centre incident. Despite years of progress, the core design of robots currently in use for USAR purposes has deviated little, favoring software and control development and optimization of the basic robot template to improve performance instead. Presented here is a novel design description of the Cricket, an advanced robot with a broader range of physical capabilities than traditional USAR robots. By incorporating the tracked structure of earlier robots, appreciated for energy efficiency and robustness, into a multi-limbed walking design, the Cricket enables the use of advanced locomotion techniques. The ability to climb over obstacles many times the height of the robot, ascend vertical shafts without the assistance of a tether, and traverse rough and near vertical terrain improves the Cricket's capability to successfully locate victims in confined spaces.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.124
GPT teacher head0.195
Teacher spread0.071 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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