Challenges and attempts to make intelligent microswimmers

Frontiers in Physics

Physics

Xin Bian¹  Chaojie Mo²  Gaojin Li³ 

[1] State Key Laboratory of Fluid Power and Mechatronic Systems, Department of Engineering Mechanics, Zhejiang University, Hangzhou, China;State Key Laboratory of Fluid Power and Mechatronic Systems, Department of Engineering Mechanics, Zhejiang University, Hangzhou, China;Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo, China;State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiaotong University, Shanghai, China;

关键词: microswimmers;    reinforcement learning;    biological fluid;    fluid–solid interaction;    microrobot;    low Reynolds;   

DOI  :  10.3389/fphy.2023.1279883

 received in 2023-08-18, accepted in 2023-09-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

The study of microswimmers’ behavior, including their self-propulsion, interactions with the environment, and collective phenomena, has received significant attention over the past few decades due to its importance for various biological and medical applications. Microswimmers can easily access micro-fluidic channels and manipulate microscopic entities, enabling them to perform sophisticated tasks as untethered mobile microrobots inside the human body or microsize devices. Thanks to the advancements in micro/nano-technologies, a variety of synthetic and biohybrid microrobots have been designed and fabricated. Nevertheless, a key challenge arises: how to guide the microrobots to navigate through complex fluid environments and perform specific tasks. The model-free reinforcement learning (RL) technique appears to be a promising approach to address this problem. In this review article, we will first illustrate the complexities that microswimmers may face in realistic biological fluid environments. Subsequently, we will present recent experimental advancements in fabricating intelligent microswimmers using physical intelligence and biohybrid techniques. We then introduce several popular RL algorithms and summarize the recent progress for RL-powered microswimmers. Finally, the limitations and perspectives of the current studies in this field will be discussed.

【 授权许可】

Unknown   
Copyright © 2023 Mo, Li and Bian.