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Design, Analysis, and Applications of Mobile Manipulators

2018· book-chapter· en· W2914955664 on OpenAlexaff
Tao Song, Feng Xi, Shuai Guo

Bibliographic record

VenueAdvances in computational intelligence and robotics book series · 2018
Typebook-chapter
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWrenchSlip (aerodynamics)Control theory (sociology)InertiaPayload (computing)KinematicsEngineeringJoint (building)SimulationComputer scienceControl engineeringStructural engineeringPhysicsAerospace engineeringArtificial intelligenceControl (management)Network packetClassical mechanics

Abstract

fetched live from OpenAlex

Presented in this chapter is a method for design and analysis of a mobile manipulator. The wrench induced by the movement of the robot arm will cause system tip-over or slip. In tip-over analysis, three cases are considered. The first case deals with the effect of the link weights and tip payload on the horizontal position of the CG. The second case deals with the effect of the joint speeds through the coupling terms including centrifugal forces and gyroscopic moments. The third case deals with the effect of the joint accelerations through the inertia forces and moments. In slip analysis, the first case considers the reaction force in relation to the stand-off distance between system and work-piece. The second and third cases investigate the effects of the joint speeds and accelerations. Then, the mobile platform is optimized to have maximum tip-over stability which optimizes the placement of the robot arm and accessory on the mobile platform. The effectiveness of the proposed method is demonstrated.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.206
Threshold uncertainty score0.912

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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
Published2018
Admission routes1
Has abstractyes

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