Microrobot Fabrication and Characterization

Microtechnology is becoming increasingly relevant in our everyday lives. To account for this shift in technological advancements, we are developing a microrobot factory to manipulate objects at the nano and micro scale. An important part of this factory is a reliable transportation system that can carry a payload to other microrobots. This study explores a new method of tracking a payload-carrying micro robotic crawler, the SolarPede. The microrobot uses 8 electrothermal Micro Electro Mechanical System (MEMS) actuators connected to 4 assembled legs consisting of 600-micron spheres. Leg motion is controlled via a microcontroller producing small 15 V electric pulses that move the actuators via thermal expansion. The design of the actuators was specifically chosen to actuate in only one direction and an appropriate gait combination of these actuators allows the SolarPede to have omnidirectional movement. During this project, we implemented a randomization algorithm for the gait pattern of the microrobot that can be combined with a microscope camera tracking system and machine learning to automatically adjust its gait pattern to guide a payload to its destination. The implementation of these algorithms improved the amount and reliability of gait data compared to previously tested manual signal generation and visual tracking. In the future, machine learning algorithms will be experimentally tested for the SolarPede, leading to its controlled and precise movement in the micro-factory.