Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Harvard Wyss Institute for Biologically Inspired Engineering have successfully designed and programmed a cockroach inspired robot called Harvard Ambulatory Microbot or HAMR.
The robot is really small in size but do not let its size fool you because the researchers claim that this tiny robot can perform almost all of the tasks of its bigger predecessor.
“Most robots at this scale are pretty simple and only demonstrate basic mobility,” said Kaushik Jayaram, a former postdoctoral fellow at SEAS and Wyss and first author of the paper. “We have shown that you don’t have to compromise dexterity or control for size.”
The research was presented virtually at the International Conference on Robotics and Automation (ICRA 2020). The researchers brainstormed if they could re-use the pop-up manufacturing process used to build previous versions of HAMR and other microbots, including the RoboBee, for this HAMR.
“The wonderful part about this exercise is that we did not have to change anything about the previous design,” said Jayaram. “We proved that this process can be applied to basically any device at a variety of sizes.” The researchers only had to shrink the 2D sheet design of the robot to get the desired result- to recreate a smaller robot but retaining all the same functionalities of its bigger predecessor.
HAMR-JR comes in at 2.25 centimeters in body length and weighs about 0.3 grams, and it has the ability to run about 14 body length per second-, making it one of the fastest microrobots.
The new model can also predict locomotion metrics like running speed, foot forces, and payload based on target size. “This new robot demonstrates that we have a good grasp on the theoretical and practical aspects of scaling down complex robots using our folding-based assembly approach,” said co-author Robert Wood, Charles River Professor of Engineering and Applied Sciences in SEAS and Core Faculty Member of the Wyss.
This research was co-authored by Jennifer Shum, Samantha Castellanos, and E. Farrell Helbling, and supported by DARPA and the Wyss Institute.