MétaCan
Menu
Back to cohort
Record W4243300667 · doi:10.32920/ryerson.14661336

Development and multiple mode control of modular and reconfigurable robot

2021· preprint· en· W4243300667 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTorqueRobotControl theory (sociology)Harmonic driveNoise (video)Modular designControl engineeringEngineeringHarmonicMode (computer interface)Computer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

There is a strong desire for robots to manipulate in uncontrolled environments. In uncontrolled environments, the robot has to adapt to the world consisting of only partially known or unknown objects and tasks, and real-time constraints. The capability of robots working in active or passive modes and switching between them helps enabling the robots to work in unstructured environments. Joint torque sensing is essential for implementing multiple mode control of robots. Though there have been a number of means of joint torque sensing, the existing joint sensing techniques have diverse limitations, such as in installation, reliability, cost, and noise immunity. This dissertation work develops a new joint torque sensing method for a modular and reconfigurable robot (MRR) with harmonic drive joints and provides solutions to multiple mode control of MRR based on the proposed sensing technique. This research consists of two main parts. In the first part, a novel mathematical model for compliance of harmonic drives has been proposed. The proposed model captures not only the nonlinear stiffness but also the hysteresis phenomenon of harmonic drive transmission. Based on the developed harmonic drive compliance model, a joint torque estimation method using position measurements is developed. Torque estimation using position measurements provides an advantage of noise immunity to the estimated joint torque. Using the compliance of harmonic drives instead of an additional elastic component does not change the joint dynamics. Building upon the new torque estimation technique, a multiple working mode control algorithm for MRR is developed and experimentally validated. The objective of the second part is to make the wrist suitable for dexterous manipulation in unstructured environments, such as door opening. A robust adaptive controller is developed for tracking control of the wrist in active mode; and a new interactive force compensation technique is proposed based on force sensor measurement, enabling passive working mode of the compact wrist without using mechanical solutions, which not only saves weight and volume, but also avoids losing tracking of the joints’ position when switching from passive mode to active mode. Experiments on a prototype wrist have demonstrated the effectiveness of the proposed method.

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)0.000
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 score0.591
Threshold uncertainty score0.866

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 head0.019
GPT teacher head0.216
Teacher spread0.197 · 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

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicModular Robots and Swarm IntelligenceFrench-language works237,207