Machine Vision Tracking and Automation of a Microrobot (sAFAM)

Abstract

In this paper, we propose a method for tracking a microrobot’s three-dimensional position using microscope machine vision. The microrobot, theSolid Articulated Four Axis Microrobot (sAFAM), is being developed to enable the assembly and manipulation of micro and nanoscale objects. In the future, arrays of sAFAMS working together can be integrated into a wafer-scale nanofactory, Prior to use, microrobots in this microfactory need calibration, which can be achieved using the proposed measurement technique. Our approach enables faster and more accurate mapping of microrobot translations and rotations, and orders of magnitude larger datasets can be created by automation. Cameras feeds on a custom microscopy system is fed into a data processing pipeline that enables tracking of the microrobot in real-time. This particular machine vision method was implemented with a help of OpenCV and Python and can be used to track the movement of other micrometer-sized features. Additionally, a script was created to enable automated repeatability tests for each of the six trajectories traversable by the robot. A more precise microrobot workable area was also determined thanks to the significantly larger datasets enabled by the combined automation and machine vision approaches.