Navigation and Visual Feedback Control for Magnetically Driven Helical Miniature Swimmers

Abstract

In this paper, controlling miniature swimmers via electromagnetic actuation has received considerable attention due to their minor invasive trait in various biomedical applications and ease of passing through the complex environments. Studying the navigation and control system is an essential step towards such applications. Currently, navigation and control for magnetically driven miniature swimmers are still challenging research issues. This paper aims to formulate a navigation and control system of magnetically driven helical miniature swimmers. First, a global planning algorithm named informed optimal random exploring tree (Informed RRT*) is applied to compute the feasible path in cluttered environments. Second, a closed-loop control algorithm is presented to follow various of reference paths using visual feedback on a planar substrate. In particular, a single-hidden layer feedforward neural networks is employed to approximate the mapping relationship between the magnetic self-rotation direction and the actual moving direction of helical miniature swimmers. The neural network is first implemented to control the magnetically driven miniature swimmers in this paper. Experiments are conducted to verify the ability of navigation and visual feedback control tasks.