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Record W1523827670 · doi:10.1109/cdc.2003.1271943

Modifed newton's method applied to potential field navigation

2004· article· en· W1523827670 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 institutionsWestern University
Fundersnot available
KeywordsRobotComputer scienceTask (project management)Field (mathematics)Newton's methodStability (learning theory)Descent (aeronautics)Gradient descentCollision avoidanceSoftwareCollisionControl engineeringSimulationArtificial intelligenceEngineeringMathematicsAerospace engineeringArtificial neural networkNonlinear system

Abstract

fetched live from OpenAlex

In this paper, we propose the use of a modification of Newton's method for potential field navigation. The use of the modified Newton's method, which applies anywhere C/sub 2/ continuous navigation functions are defined, greatly improves system performance in the presence of obstacles, when compared to the standard gradient descent approaches. To illustrate the technique, we also propose a hierarchical software architecture for robots that supports multi-robot coordination and can be easily extended for other applications. Simulations show that a robot team can accomplish a cooperative material-handling task in an initially unknown environment while avoiding collision with static obstacles and other team members. We derive a control law based on the modified Newton method that guarantees team stability for all time.

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: Methods
Teacher disagreement score0.177
Threshold uncertainty score0.412

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 science0.0010.000
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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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
Published2004
Admission routes1
Has abstractyes

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