Independent Control and Path Planning of Microswimmers with a Uniform Magnetic Field

Abstract

Artificial bacteria flagella (ABFs) are magnetic helical microswimmers that can be remotely controlled via a uniform, rotating magnetic field. Previous studies have used the heterogeneous response of microswimmers to external magnetic fields for achieving independent control. Herein, analytical and reinforcement learning control strategies for path planning to a target by multiple swimmers using a uniform magnetic field are introduced. The comparison of the two algorithms shows the superiority of reinforcement learning in achieving minimal travel time to a target. The results demonstrate, for the first time, the effective independent navigation of realistic microswimmers with a uniform magnetic field in a viscous flow field.

AI-enhanced biomedical micro/nanorobots in microfluidics

“…133 In dealing with complex and unknown environments, AI plays a crucial role for microswimmer navigation, especially through RL methods. [134][135][136][137] AI will potentially foster the development of MNRs navigation by facilitating more autonomous capabilities and proactive adaptability.…”

Optimal active particle navigation meets machine learning (a)

“…In line with that, several recent works have used reinforcement learning to explore the swimming mechanism of deformable agents [102][103][104][105]. Related works have used learning approaches to understand how a swimmer needs to deform to swim as fast as possible [106], to follow a predetermined path [107], to exhibit chemotaxis [108] or to achieve optimal point-to-point navigation [109][110][111]. For example, ref.…”

Challenges and attempts to make intelligent microswimmers

“…It is also possible to use an RL approach to implement pathplanning for multiple microswimmers at the same time. Amoudruz and Koumoutsakos [134] used the actor-critic RL method to realize independent control of two magnetic helical microswimmers using a uniform rotating magnetic field. Compared with a semi-analytical method, the RL approach works in not only quiescent flow but also complex flow background.…”