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Record W2092499874 · doi:10.1002/rob.10083

Pose Optimization of Serial Manipulators Using Knowledge of Their Velocity‐Degenerate (Singular) Configurations

2003· article· en· W2092499874 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Robotic Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDegenerate energy levelsSerial manipulatorControl theory (sociology)Computer scienceControl engineeringArtificial intelligenceEngineeringPhysicsRobotControl (management)Parallel manipulator

Abstract

fetched live from OpenAlex

Abstract This work investigates the exploitation of velocity‐degenerate configurations to optimize the pose of either nonredundant or redundant serial manipulators to sustain desired wrenches. An algorithm is developed that determines a desirable start point for the optimization of a serial manipulator's pose. The start‐point algorithm (SPA) uses analytical expressions of the velocity‐degenerate (singular) configurations of a serial manipulator to determine a pose that would be best suitable to sustain a desired wrench. Results for an example redundant serial manipulator are presented. The example results show that by using the SPA with the optimization routine, the resulting poses obtained require less effort from the actuators when compared to the poses obtained without using the SPA. It is shown that when no constraint is imposed on the position of the end‐effector, the SPA excels at providing a better solution with less iterations than running the optimization without the SPA. © 2003 Wiley Periodicals, Inc.

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 score0.793
Threshold uncertainty score0.508

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 science0.0000.000
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 head0.024
GPT teacher head0.231
Teacher spread0.207 · 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