3-D Autonomous Manipulation System of Helical Microswimmers With Online Compensation Update

Steering microswimmers toward 3-D autonomous manipulation tasks has received extensive attention. Our previous works have accomplished autonomously manipulating microswimmers in the 2-D space. This article aims to extend the 2-D autonomous manipulation to 3-D autonomous manipulation. Specifically, this article addresses the problem of an autonomous system that consists of 3-D path planning and 3-D path following for magnetically driven helical microswimmers. The path-planning algorithm called optimal Bidirectional RRT* is formulated to explore the shortest route in the confined 3-D space. A proxy-based sliding mode control (PSMC) approach is developed to design stable controllers based on the error model in the Serret-Frenet frame. We transport the swimming model trained by a kind of neural network to another new helical microswimmer according to an online updating scheme. The updating scheme can identify and refine compensating angles between the swimming direction of the microswimmer and the magnetic direction in the 3-D space facing the weight disturbances of the swimmer and lateral disturbances. The experiments are conducted to quantitatively validate the 3-D autonomous manipulation system. Experimental results show the effectiveness of path planning and path following with submillimeter accuracy in a 3-D space. Future works will focus on autonomous manipulations in dynamic environments. Note to Practitioners-This article is motivated by the issue of 3-D autonomous manipulation tasks for magnetically driven helical microswimmers. The formulated path planning is responsible for finding the shortest route in the 3-D confined space. The closed-loop controller is charge of steering the helical microswimmers on a reference path based on an online updating model trained by neural networks. It is demonstrated that the helical microswimmer can find the shortest path and follow it in a 3-D space with submillimeter accuracy.