MétaCan
Menu
Back to cohort
Record W2041655521 · doi:10.1145/1341012.1341064

Geometric algorithms for clearance based optimal path computation

2007· article· en· W2041655521 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 Calgary
Fundersnot available
KeywordsComputational geometryShortest path problemPath (computing)AlgorithmDisjoint setsComputer scienceComputationMotion planningPath lengthAny-angle path planningMathematical optimizationObstacleMathematicsCombinatoricsTheoretical computer scienceGraphArtificial intelligence

Abstract

fetched live from OpenAlex

In path planning, it is often more practical to evaluate the quality of a path not only on the basis of its length but also on the clearance from obstacles. In this paper, we propose computational geometry methods to compute the shortest path with a user-specified minimum clearance between two points in the plane in the presence of disjoint, simple, polygonal obstacles. The algorithm has time complexity O(nlogn) where n is a multiple of the number of obstacle vertices. By setting the minimum clearance to zero, we demonstrate that our algorithm can provide a high quality approximation of the shortest path between source and destination points.

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: Methods
Teacher disagreement score0.687
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicRobotic Path Planning AlgorithmsFrench-language works237,207