Temporal Logic-Guided DQN for Object Delivery and Transportation Using Magnetic Microrobots With Local Control

Abstract

Microrobots driven by magnetic fields hold significant promise in medical applications, particularly in drug delivery–a key area in the biomedical field. Traditional approaches primarily rely on global magnetic fields to control single microrobots, which makes it challenging to independently control multiple microrobots. To address this limitation, this paper introduces a local magnetic field generation system that enables independent control of multiple microrobots. The proposed system employs a printed circuit board (PCB) array-based magnetic microrobot system, utilizing a microcoil array for precise and localized control. To enhance task execution, we integrate Deep Reinforcement Learning (DRL) with Linear Temporal Logic (LTL) to generate obstacle-avoiding paths for single and dual magnetic microrobots. The system is validated through magnetic droplet transport experiments. Experimental results demonstrate the effectiveness of the proposed system in achieving autonomous multi-task drug delivery and droplet fusion. This work underscores the potential of magnetic field-driven microcoil array systems in advancing transportation and drug delivery technologies in biomedical engineering.