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Record W4398152784 · doi:10.1109/tsmc.2024.3391800

Degeneracy-Aware Full-Pose Path Planning Strategy for Robot Manipulator

2024· article· en· W4398152784 on OpenAlexafffund
Henghua Shen, Wenfang Xie, Ningyu Zhu

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsConcordia University
FundersConcordia University
KeywordsMotion planningPath (computing)Mathematical optimizationRobotComputer scienceJacobian matrix and determinantAny-angle path planningOrthogonalityMathematicsControl theory (sociology)Artificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

In this article, we present a novel full-pose path planning strategy with degenerate direction avoidance for robot manipulators. Effort seeks to define an efficient multiobjective cost function for finding an optimal path in task space by minimizing the path length, and, meanwhile, maximizing orthogonality to the degenerate direction of the path. Since a robot manipulator by nature is a noncommensurate system, the unit dual quaternion is used to unify the pose representation and formulate the cost function of path length. Additionally, to justify the robot’s ability to maneuver, another cost evaluation of orthogonality is developed on the basis of the analytical Jacobian matrix decomposition. The optimized rapidly exploring random tree (RRT*) is, then, applied to execute the synthesized cost function in planning a path for a six-degree-of-freedom (6-DOF) FANUC-M-20iA industrial robot. By comparing with two relevant path planning methods, simulation analysis results illustrate the superior performance of the proposed path planning approach in terms of path direction and joint change.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score1.000

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 communication0.0010.000
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 head0.028
GPT teacher head0.244
Teacher spread0.216 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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