Law Junhui, Yu Jiangfan, Tang Wentian, Gong Zheyuan, Wang Xian, Sun Yu
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada.
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China.
ACS Nano. 2023 Jul 25;17(14):12971-12999. doi: 10.1021/acsnano.2c11733. Epub 2023 Jul 11.
Swarms, which stem from collective behaviors among individual elements, are commonly seen in nature. Since two decades ago, scientists have been attempting to understand the principles of natural swarms and leverage them for creating artificial swarms. To date, the underlying physics; techniques for actuation, navigation, and control; field-generation systems; and a research community are now in place. This Review reviews the fundamental principles and applications of micro/nanorobotic swarms. The generation mechanisms of the emergent collective behaviors among the micro/nanoagents identified over the past two decades are elucidated. The advantages and drawbacks of different techniques, existing control systems, major challenges, and potential prospects of micro/nanorobotic swarms are discussed.
群体源于个体元素之间的集体行为,在自然界中很常见。自二十年前以来,科学家们一直在试图理解自然群体的原理,并利用这些原理来创建人工群体。迄今为止,相关的基础物理学、驱动、导航和控制技术、场生成系统以及一个研究群体已经形成。本综述回顾了微纳机器人群体的基本原理和应用。阐述了过去二十年来所确定的微纳主体之间涌现出的集体行为的产生机制。讨论了不同技术的优缺点、现有的控制系统、主要挑战以及微纳机器人群体的潜在前景。