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Record W2106878815 · doi:10.1109/robio.2013.6739472

Collision-free single-step motion planning of biped pole-climbing robots in spatial trusses

2013· article· en· W2106878815 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMotion planningRobotComputer scienceClimbingPath (computing)ClimbTrajectoryTrussControl theory (sociology)Robot kinematicsConvergence (economics)Collision detectionEngineeringCollisionArtificial intelligenceMobile robotStructural engineering

Abstract

fetched live from OpenAlex

For a biped pole-climbing robot (BiPCR) with dual grippers to climb poles, trusses or trees, a feasible collision-free climbing path is inevitable. In this paper, we utilize the sampling-based algorithm, Bi-RRT, to plan a feasible single-step collision-free climbing motion for BiPCRs in spatial trusses. Under the orientation limit of a 5-DoFs BiPCR, a new state representation along with corresponding operations including sampling, metric calculation and interpolation is presented. A simple but effective model of BiPCRs in trusses is proposed, through which the climbing path planning problem is transformed to be similar to that of an industrial robot. In addition, the pre- and post- processes are introduced not only to expedite the convergence of the Bi-RRT, but also to ensure the safe movement for the robot near the poles. The effectiveness and efficiency of the presented Bi-RRT algorithms for climbing motion planning are verified in the simulation.

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: Methods · Consensus signal: none
Teacher disagreement score0.514
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.244
Teacher spread0.216 · 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

Quick stats

Citations15
Published2013
Admission routes1
Has abstractyes

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