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Near-Optimal Trajectory Generation for Flexible Motion Systems using Two-Boundary Approach

2023· article· en· W4385451887 on OpenAlexafffund
Yazan M. Al-Rawashdeh, Vasanth Reddy, Mohammad Al Saaideh, Almuatazbellah Boker, Hoda Eldardiry, Mohammad Al Janaideh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education
KeywordsTrajectoryMotion (physics)Computer scienceBoundary (topology)Process (computing)Interval (graph theory)Motion controlInvariant (physics)AlgorithmControl theory (sociology)Artificial intelligenceMathematicsControl (management)Mathematical analysisRobotCombinatoricsPhysicsProgramming language

Abstract

fetched live from OpenAlex

The dynamics of the supposedly known flexible motion system is given a voice in the making process of the desired trajectory signals it has to follow. In doing so, and according to the herein proposed approach, a singularly perturbed version of the system dynamics is obtained which allows the system to be treated as time-invariant despite any existing time dependency. Based on the nature of the system assigned task, the trajectory making process is subdivided into several intervals, where each interval has its own boundary conditions that need to be assigned by the motion designer. In this sense, the boundary conditions act as way-points that govern the smooth states evolution over time, and are used to build internal and self-driven optimal reference trajectories to fulfill the desired actual system motion profile. Despite its simplicity, the superiority of the proposed technique is compared to the 2<sup>nd</sup>-order, 3<sup>rd</sup>-order, and sinusoidal standard motion trajectories, and its performance is evaluated through simulation.

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: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.480

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 head0.038
GPT teacher head0.246
Teacher spread0.208 · 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
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

Citations1
Published2023
Admission routes2
Has abstractyes

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