Artificial Intelligence-Assisted Multimode Microrobot Swarm Behaviors

Abstract

Mimicking the swarm behaviors in nature, the microswarm has shown dynamic transformations and flexible assemblies in complex physiological environments, garnering increasing attention for its potential medical applications. However, because of the complexity of swarm behaviors and the corresponding influencing factors, achieving controllability, stability, and diversity of an artificial microswarm remains challenging. Here, a physically assisted artificial intelligence analysis framework was employed to predict the multimode swarm behaviors of a magnetic microswarm. By modulating 12 different parameters of a programmable magnetic field, we obtained various swarm patterns, including liquid, rod, network, ribbon, flocculence, and vortex. A physical model was developed to simulate the programmable 3D magnetic field and the corresponding collective behaviors. Explainable artificial intelligence analysis uncovered the relationship between control parameters and magnetic swarm patterns, achieving a prediction accuracy of 83.87% for pattern classification. Our stability analysis revealed that rod and vortex patterns exhibited higher stability, making them ideal for precise manipulation tasks. Leveraging this framework, we demonstrated environmentally adaptive swarm navigation through complex channels and swarm hunting of specific targets. This study could not only advance the understanding of microswarm control but also provide a strategy for targeted delivery and micromanipulation in potential clinical applications.