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Record W2004727989 · doi:10.1145/1247069.1247080

Finding curvature-constrained paths that avoid polygonal obstacles

2007· article· en· W2004727989 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 British Columbia
Fundersnot available
KeywordsPath (computing)CurvatureFast pathBounded functionMathematicsMotion planningDomain (mathematical analysis)Any-angle path planningAlgorithmLongest path problemComputer scienceMathematical optimizationTopology (electrical circuits)CombinatoricsGeometryMathematical analysisShortest path problemGraphRobotArtificial intelligence

Abstract

fetched live from OpenAlex

We describe an algorithm to find a unit-curvature path between specified configurations in an arbitrary polygonal domain. Whenever such a path exists, the algorithm returns an explicit description of one such path in time that is polynomial in n (the number of features of the domain), m (the precision of the input) and k (the number of segments on the simplest obstacle-free Dubins path connecting the specified configurations). Our algorithm is based on a new normal form for unit-curvature paths and a dynamic path filtering argument that exploits a separation bound for distinct paths in this normal form.The best result known for the feasibility of bounded-curvature motion in the presence of arbitrary polygonal obstacles involves a reduction to the first-order theory of the reals. It just determines if a feasible path exists (it does not return a path) and requires exponential time and space.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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