Machine learning could help tiny microrobots swim through a fluid and reach their goal without being knocked off target by the random motion of particles they encounter on their journey.
Microrobotic “swimmers” are often designed to mimic the way bacteria can propel themselves through a fluid – but bacteria have one key advantage over the robots.
“A real bacterium can sense where to go and decide that it goes in that direction because it wants food,” says Frank Cichos at the University of Leipzig, Germany.
It is difficult for the bacteria-sized microrobots to stay on course because their small size – some are just 2 micrometres across – means they are buffeted by particles in the fluid. Unlike the bacteria, they can’t correct their direction of travel, and so they tend to follow a random path described by Brownian motion.
Cichos and his colleagues decided to give their microrobot swimmers a “brain”: a machine learning algorithm that rewards “good” movements in the direction of a desired target.
“We decided it would be good to combine [the swimming microrobots] with machine learning, which is a bit like what we do in life,” says Cichos. “We experience our environment, and depending on the success of what we do, we keep that in memory or not.”