Closed-Loop Control for a Heterogeneous Group of Magnetically-Actuated Microrobots

Logan E Beaver,Zameer Hussain Shah,Max Sokolich, Alp Eren Yilmaz,Yanda Yang,Calin Belta,Sambeeta Das

International Conference on Manipulation Automation and Robotics at Small Scales (MARSS) International Conference on Manipulation Automation and Robotics at Small Scales（2023）

Department of Mechanical and Aerospace Engineering | Department of Mechanical Engineering | Division of Systems Engineering

Cited 0|Views20

Abstract

The development of magnetically-actuated microrobots is of great interest for emerging medical applications due to their inherent safety, low cost to manufacture, and flexibility. In many practical applications, precise control over the motion of the microrobots is a strong requirement. In these contexts, closed-loop control is a practical tool to adjust the microrobots' control inputs in real time. In this work, we describe a process to quickly fabricate a large number of heterogeneous microrobots using colloidal synthesis. We simultaneously develop a closed-loop control law that drives the microrobots to a desired formation in the plane. In addition, we prove that heterogeneity in the microrobot dynamics is necessary to generate arbitrary formations. Finally, using experimental data, we show in simulation that $N=4$ microrobots can be driven to any arbitrary formation using our control law.

Translated text

Key words

Microscale Self-Assembly,Micro/Nanomotors

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper