Ultrasound Image-Based Average $Q$-Learning Control of Magnetic Microrobots

Magnetic microrobots have garnered significant attention and hold great potential for biomedical research applications. However, achieving precise manipulation in vivo poses significant challenges, particularly in medical image-based real-time feedback control, because it is difficult for a visual camera to track the motion of magnetic microrobots inside the body in biomedical applications. To realize the precise control of magnetic microrobots, it is also necessary to design and implement a simple and powerful control method. This approach allows for avoiding resource-intensive and complex control strategies. In this article, we present a learning-based real-time control method utilizing ultrasound images. Inspired by the ADboost concept, we use a reinforcement learning approach to integrate two simple control methods: a proportional-integral-derivative controller and a guiding vector field controller. We develop a novel $Q$-learning method called average $Q$-learning that incorporates average operation and $n$-step bootstraps. Its primary objective is to dynamically adjust the outputs of the different simple controllers. While each controller individually offers a straightforward solution, their integration contributes to a powerful control approach. To demonstrate its scalability, a nonsmooth path is utilized to investigate the integration performance of three simple controllers. In addition, we enhance a classic segmentation module, U-net, by incorporating an atrous spatial pyramid pooling module. To validate the effectiveness of the proposed control method, we conduct simulations and experiments using various planar paths. The quantitative analysis of the results demonstrates the efficacy of our approach in achieving precise manipulation, leveraging real-time control based on medical images for magnetic microrobots. Overall, this study provides a preliminary investigation into the field of medical image-based precise manipulation of magnetic microrobots in vivo applications.