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Record W2096304345 · doi:10.1017/s0263574708004426

A mobile robot with autonomous climbing and descending of stairs

2008· article· en· W2096304345 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
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