A Data-Driven Approach to Motion Planning and Optimal Control of Medical Nanorobots with Dynamic Window Approach⋆

Abstract

Targeted drug delivery via nanorobots has become an area of prominent research. However, since such robots are microns in size (and in the future, nanometers in size), implementing control laws to make sure the nanorobot does not deviate from a desired trajectory becomes exceedingly difficult. Building upon the existing nonlinear dynamic models of a nanorobot, this work proposes a data-driven method for optimal path planning and control of nanorobots. Specifically, the collision avoidance strategy, Dynamic Window Approach (DWA) implemented with linear quadratic regulator (LQR) control, is employed to simulate a randomly chosen nanorobot moving through the pulmonary artery against blood flow. The nanorobot is simulated as though it is being guided from the initial site to the target site via an induced magnetic field gradient generated by an MRI. It is shown that when full-state feedback control is implemented for nanorobot control, the LQR controller is able to quickly bring the nanorobot to desired states.