MétaCan
Menu
Back to cohort
Record W2096304345 · doi:10.1017/s0263574708004426

A mobile robot with autonomous climbing and descending of stairs

2008· article· en· W2096304345 on OpenAlexaff
Pinhas Ben‐Tzvi, Shingo Ito, A.A. Goldenberg

Bibliographic record

VenueRobotica · 2008
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMobile robotRobotTraverseStairsStair climbingClimbingComputer scienceProcess (computing)Artificial intelligenceTask (project management)SimulationTerrainRoboticsHuman–computer interactionEngineeringEmbedded systemReal-time computingSystems engineering

Abstract

fetched live from OpenAlex

SUMMARY Mobile robots are used to operate in urban environments, for surveillance, reconnaissance, and inspection, as well as for military operations and in hazardous environments. Some are intended for exploration of only natural terrains, but others also for artificial environments, including stairways. This paper presents a mobile robot design that achieves autonomous climbing and descending of stairs. The robot uses sensors and embedded intelligence to achieve the task. The robot is a reconfigurable tracked mobile robot that has the ability to traverse obstacles by changing its track configuration. Algorithms have been further developed for conditions under which the mobile robot would halt its motion during the climbing process when at risk of flipping over. Technical problems related to the implementation of some of the robot functional attributes are presented, and proposed solutions are validated and experimentally tested. The experiments illustrate the effectiveness of the proposed approach to autonomous climbing and descending of stairs.

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 score0.001
Threshold uncertainty score0.005

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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

Citations35
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueRoboticaSame topicRobotic Locomotion and ControlFrench-language works237,207