MétaCan
Menu
Back to cohort
Record W2999722249 · doi:10.1139/tcsme-2019-0173

Development of a trajectory planning algorithm for a 4-DoF rockbreaker based on hydraulic flow rate limits

2020· article· en· W2999722249 on OpenAlexaffvenue
Louis-Francis Y. Tremblay, Marc Arsenault, Meysar Ƶeinali

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTrajectoryControl theory (sociology)Decoupling (probability)Parameterized complexityMotion planningComputer scienceTrajectory optimizationPath (computing)Nonlinear systemPoint (geometry)Mathematical optimizationMathematicsAlgorithmControl engineeringEngineeringOptimal controlRobotArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

In this paper, a novel trajectory planning methodology is proposed for use within a semi-automated hydraulic rockbreaker system. The objective of the proposed method is to minimize the trajectory duration while hydraulic fluid flow rate limits are respected. Within the trajectory planning methodology, a point-to-point path planning approach based on the decoupling of the motion of the rockbreaker’s first joint is compared with an alternative approach based on Cartesian straight-line motion. Each of these path types is parameterized as a function of time based on an imposed trajectory profile that ensures smooth rockbreaker motions. A constrained nonlinear optimization problem is formulated and solved with the trajectory duration as the objective function while constraints are applied to ensure that flow rate limits through the rockbreaker’s proportional valves and hydraulic pump are not exceeded. The proposed methodology is successfully implemented to compute a set of representative trajectories, with the path planning approach based on the decoupling of the motion of the rockbreaker’s first joint consistently producing shorter trajectory durations.

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 score0.838
Threshold uncertainty score0.689

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 head0.022
GPT teacher head0.206
Teacher spread0.183 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicHydraulic and Pneumatic SystemsFrench-language works237,207