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Design of a High-torque Robot Joint and Its Control System

2022· article· en· W4283326703 on OpenAlexaff
Qiang Hua, Weigang Zhou, Shiqiang Zhu, Yunchang Yao, Chao Cheng, Anhuan Xie, Dan Zhang

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsYork University
Fundersnot available
KeywordsTorqueRobotRobot controlReducerEngineeringMiniaturizationJoint (building)Control theory (sociology)Computer scienceSimulationControl engineeringMobile robotMechanical engineeringControl (management)Artificial intelligenceStructural engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The robot joint is one of the key components of robots. With the wide application of robot joints, the demand for joint torque is becoming higher and higher. However, at present, most high-torque robot joints are generally large in weight and size, which creates problems for some applications. For this purpose, a high-torque and high-torque-density robot joint is proposed. To improve the joints’ torque density, the lightweight motor and reducer with a large reduction ratio are selected, and the mechanical structure design is based on weight reduction, miniaturization, and heat dissipation. The maximum torque of the proposed robot joint can reach up to 182Nm, and its mass is only 1.8kg. Besides, an embedded control system is designed to control the robot joint. Many experiments were conducted to measure the robot joint’s characteristics, such as torque, speed, and control features. And the designed robot joints are successfully applied to a biped robot. The results verify the performance of the designed robot joint and its control system.

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 score0.880
Threshold uncertainty score0.309

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 head0.017
GPT teacher head0.191
Teacher spread0.174 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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