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Record W3145472785

MULTI-DEGREES-OF-FREEDOM WIRELESS ACTUATION OF SMALL MAGNETIC MECHANISMS

2020· dissertation· W3145472785 on OpenAlexaff
Sajad Salmanipour

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWirelessDegrees of freedom (physics and chemistry)Computer sciencePhysicsTelecommunicationsQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

Magnetic millimeter-scale robots are often actuated using externally generated magnetic fields. For most applications, these remote magnetic microrobots are located relatively far from the magnetic field generation sources. In this condition, all microrobots receive approximately the same driving magnetic field, which we term a homogeneous magnetic field. For many microrobotic tasks such as drug dispensing, biopsy tool activation or grasping, multiple system degrees of freedom (DOF) must be controlled. To achieve multi-DOF control in a homogeneous magnetic field, clever system design is required. While some progress has been made in this area allowing up to six independent DOFs to be individually commanded, there has been no rigorous effort in determining the maximum achievable number of DOFs for systems with homogeneous magnetic field input. In this work, we show that this maximum is eight and we introduce the theoretical basis for this conclusion, relying on the number of independent usable components in a magnetic field at a point. To verify the claim experimentally, we first develop an electromagnetic field generation system capable of generation all the eight independent magnetic components at a single point, followed by a simple 8-DOF demonstration mechanism, to show the feasibility of eight independently actuated motions. Next, we introduce a design process to utilize the maximum number of independently actuated DOFs on a microrobot system. We make use of four classes of microrobotic mechanisms which are commonly used in practice and allow for the creation of more complex microrobotic mechanisms with up to eight actuated DOFs. The systematic design framework is presented in the form of an optimization problem, where the designer specifies the number of magnets, and the type and quantity of mechanisms of the microdevice. The result gives the optimized position and orientation of on-board magnets and axes for mechanism motion. To verify the functionality of the design process, we utilize it to develop a 7-DOF wireless robot for drug delivery applications. Next, to investigate the feasibility of utilizing magnetic actuation methods in minimally invasive surgery procedures, a 3-DOF wireless gripper prototype and an 8-DOF two-grippers mechanism will be presented. The method and design process presented here for achieving up to eight actuated DOFs in homogeneous quasi-static magnetic fields can be applied to any microrobotic system where multiple motions and on-board mechanisms can lead to a more effective 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.299
Teacher spread0.238 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

Explore more

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