Search for: All records

The sAFAM is a novel mm-size microrobot built using MicroElectroMechanical Systems (MEMS) technology. It consists of a monolithically fabricated microrobotic arm assembled onto four in-plane actuators, capable of moving along four degrees of freedom, including translational movement in X and Y axes as well as pitch and yaw. In this paper, several design modifications were proposed to increase movement precision, stability, and controllability to the sAFAM tip. An interface is developed to assist a human operator accurately position the microrobot tip during nano-object handling. A Python-based graphical user interface (GUI) was programmed to make it intuitive for an operator to use and obtain required tip precision under a microscope. Experimental results demonstrate the functionality of the proposed control solution, and the tip motion resolution using microscope images of the microrobot tip under 20x magnification during operation. The hardware and software requirements for the proposed experimental setup and control platform are discussed in detail. 

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. Keywords: Micro robotics, machine vision, nano microscale manufacturing.