Local flow sensing on helical microrobots for semi-automatic motion adaptation

Helical microrobots with dimensions below 100 mu m could serve many applications for manipulation and sensing in small, closed environments such as blood vessels or inside microfluidic chips. However, environmental conditions such as surface stiction from the channel wall or local flow can quickly result in the loss of control of the microrobot, especially for untrained users. Therefore, to automatically adapt to changing conditions, we propose an algorithm that switches between a surface-based motion of the microrobot and a 3D swimming motion depending on the local flow value. Indeed swimming is better for avoiding obstacles and difficult surface stiction areas but it is more sensitive to the flow than surface motion such as rolling or spintop motion. First, we prove the flow sensing ability of helical microrobots based on the difference between the tracked and theoretical speed. For this, a 50 mu m long and 5 mu m diameter helical microrobot measures the flow profile shape in two different microchannels. These measurements are then compared with simulation results. Then, we demonstrate both swimming and surface-based motion using closed-loop control. Finally, we test our algorithm by following a 2D path using closed-loop control, and adapting the type of motion depending on the flow speed measured by the microrobot. Such results could enable simple high-level control that could expand the development of microrobots toward applications in complex microfluidic environments.