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Record W4392985035 · doi:10.23977/jaip.2024.070115

Robot Trajectory Planning and Simulation Based on Matlab Robotics Toolbox

2024· article· en· W4392985035 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2024
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsToolboxRoboticsArtificial intelligenceMATLABComputer scienceTrajectoryRobotComputer visionControl engineeringSimulationHuman–computer interactionEngineeringProgramming languagePhysics

Abstract

fetched live from OpenAlex

The trajectory planning of robots refers to the motion design of the pose, velocity, and acceleration of the end effector (robot operating arm) in spatial motion. According to the requirements of the robot's task, the end effector moves along the expected trajectory from the initial state to the endpoint state. This article takes the PUMA560 robot as the object, uses an improved D-H parameter method to establish a coordinate system and design parameters, solves the forward and inverse kinematics of the robot. This article uses the fifth degree polynomial interpolation method to obtain the curves of robot joint angle, angular velocity, and angular acceleration over time, and uses Matlab's robot toolbox for trajectory planning simulation to verify the good motion performance of robot joints and the rationality of parameter design.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.582
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.367
Teacher spread0.291 · 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