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Record W4411569957 · doi:10.2316/j.2025.206-1208

JERK-CONTINUOUS ONLINE TRAJECTORY PLANNING AND FEEDFORWARD CONTROL FOR FLEXIBLE JOINT ROBOTS, 1-8.

2025· article· en· W4411569957 on OpenAlexvenueno aff
Pengxiao Jia, Yifeng Li, Jianhua Yang

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2025
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFeed forwardJerkTrajectoryRobotJoint (building)Computer scienceControl theory (sociology)Control (management)Control engineeringEngineeringArtificial intelligencePhysicsStructural engineering

Abstract

fetched live from OpenAlex

This paper presents a novel seventh-degree polynomial joint space trajectory planning algorithm.This algorithm features continuous jerk, enabling efficient online computation and handling of singular configurations in robotic systems.Furthermore, a fourth-order feedforward controller is implemented to investigate vibration control in flexible joint robots.Comparative experiments demonstrate the proposed trajectory planning algorithm's ability to generate smoother trajectories.The fourth-order feedforward control effectively leverages information from the reference trajectory.The combined approach of the proposed trajectory planning algorithm and fourth-order feedforward control significantly enhances the control performance of flexible joint robots.

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 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 score0.767
Threshold uncertainty score0.365

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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