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Record W4238178774 · doi:10.32920/ryerson.14657178

Development of modular and reconfigurable robot with multiple working modes

2021· preprint· en· W4238178774 on OpenAlexfundno aff
Xiaojia He

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designWrenchJacobian matrix and determinantProcess (computing)EngineeringControl engineeringRobotMode (computer interface)KinematicsCompensation (psychology)SimulationControl theory (sociology)Computer scienceControl (management)Mechanical engineeringArtificial intelligencePhysicsMathematics

Abstract

fetched live from OpenAlex

A modular and reconfigurable robot (MRR) with multiple working modes for performing manipulation in uncontrolled environments is developed in this thesis. In the proposed MRR design, each joint module can independently work in active mode or passive mode. Major contributions of this thesis include the development of the passive mode with a unique friction compensation method and the use of force control in manipulation, such as door opening. In order to implement force control, the kinematics model and Jacobian matrix of the manipulator are derived by using the twist and wrench method, which is superior to the common D-H method, and the complete force analysis of the spherical wrist is presented as well. As a case study, the door opening process using force control is investigated by simulation and experiments. Door opening is successfully demonstrated using the developed MRR with multiple working modes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.211
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2021
Admission routes1
Has abstractyes

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