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Record W3156906773 · doi:10.3390/act10040080

Trajectory Optimization Algorithm for a 4-DOF Redundant Parallel Robot Based on 12-Phase Sine Jerk Motion Profile

2021· article· en· W3156906773 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

VenueActuators · 2021
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsToronto Metropolitan University
FundersNatural Science Foundation of Hunan ProvinceNational Natural Science Foundation of China
KeywordsJerkControl theory (sociology)TrajectorySine waveMotion controlComputer scienceOscillation (cell signaling)MathematicsRobotEngineeringPhysicsArtificial intelligenceAccelerationClassical mechanicsVoltageControl (management)

Abstract

fetched live from OpenAlex

To improve high motion accuracy and efficiency in the high-speed operation of a 4-DOF (4 degrees of freedom) redundant parallel robot, this paper introduces a trajectory planning of the parallel robot in joint space based on the twelve-phase sine jerk motion profile. The 12-phase sine jerk motion profile utilizes the characteristics of a sine function. Furthermore, the penalty function is used to optimize the trajectory energy consumption under the constraint condition. The simulation and experimental results show that the energy consumption of joint space is slightly higher than that of the three-phase sine jerk motion profile, but the overall operation is more accurate and stable. Specifically, the sudden change of force and velocity in each joint is eliminated, which is the cause of mechanism oscillation. Moreover, the force of each joint is more average. The results indicate that each movement is closer to the maximum allowable limit and the running efficiency is higher.

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 score0.082
Threshold uncertainty score0.770

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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