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Record W2293312031 · doi:10.1109/csci.2015.92

C-Theta*: Cluster Based Path-Planning on Grids

2015· article· en· W2293312031 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 Windsor
Fundersnot available
KeywordsGridMotion planningShortest path problemPath (computing)Computer scienceCluster analysisConstraint (computer-aided design)Widest path problemGrid referenceAny-angle path planningGrid method multiplicationAlgorithmCluster (spacecraft)Path lengthMathematical optimizationK shortest path routingMathematicsArtificial intelligenceTheoretical computer scienceMobile robotGraphGeometry

Abstract

fetched live from OpenAlex

Path planning is used to solve the problem of moving an agent towards a destination. Theta* is a well know any angle path planning algorithm which works by utilizing line of sight checks during the search. To find shorter paths that are not constraint to grid edges, there is a compromise in the time taken to reach the destination which makes Theta* undesirable as the grid map size increases. To solve this problem and enhance the search performance we propose a method which divides a map into high and low density regions using an unsupervised clustering algorithm based on the number of blocked nodes on a grid map. After comparing the proposed model with theta* the results show the time taken to find the shortest path to be reduced significantly in comparison with Theta* while the path length will remain as short as Theta.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.426
Threshold uncertainty score0.878

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)0.0000.001

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 head0.062
GPT teacher head0.288
Teacher spread0.226 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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