Smart microrobots learn how to swim and navigate with artificial intelligence



Researchers from Santa Clara University, New Jersey Institute of Technology, and the University of Hong Kong have successfully used deep reinforcement learning to teach microrobots how to swim, representing a significant advancement in the field of microswimming.

The creation of artificial microswimmers that can travel the globe in a manner akin to naturally occurring swimming microorganisms, including bacteria, has generated a great deal of attention. These microswimmers hold promise for a wide range of upcoming biomedical applications, including microsurgery and tailored medication administration. However, the majority of artificial microswimmers available today can only carry out a limited set of fixed locomotory gaits.

The researchers reasoned that microswimmers may learn and adapt to changing environments using AI in their work, which was published in Communications Physics. Similar to how humans learning to swim need reinforcement learning and feedback to stay afloat and move in different directions under changing conditions, so too do microswimmers, but with their own particular set of hurdles provided by physics in the tiny environment.

According to On Shun Pak, an associate professor of mechanical engineering at Santa Clara University, "being able to swim at the micro-scale by itself is a tough issue." The design of a microswimmer's locomotory gaits may easily become impenetrable when you want it to do increasingly complex moves.

The team was able to effectively train a basic microswimmer to swim and navigate in any direction by fusing reinforcement learning and artificial neural networks. The swimmer receives input on how effective specific movements are when it does certain movements. The swimmer then gradually picks up how to swim based on its interactions with the world around it.

According to Alan Tsang, associate professor of mechanical engineering at the University of Hong Kong, "the microswimmer learns how to manipulate its 'body parts' — in this example, three microparticles and extensible connections — to self-propel and turn." It accomplishes this by using solely a machine learning algorithm, not human understanding.

The AI-powered swimmer can autonomously transition between several locomotory gaits to go in the direction of any desired area.

The researchers used the swimmer's impressive abilities to prove that it could follow a complicated course without being expressly trained. They also showed how well the swimmer performed when navigating while being affected by external fluid fluxes.

Professor of mathematical sciences at New Jersey Institute of Technology Yuan-nan Young remarked, "This is our first step in facing the task of generating microswimmers that can adapt like biological cells in navigating complicated surroundings autonomously."

Future biological applications of artificial microswimmers in complicated mediums with uncontrollable and unexpected external elements will depend on such adaptive behaviors.

According to Arnold Mathijssen, a University of Pennsylvania expert on microrobots and biophysics who was not involved in the study, "This work is a key example of how the rapid development of artificial intelligence may be exploited to tackle unresolved challenges in locomotion problems in fluid dynamics." This work's combination of machine learning with microswimmers will lead to further links between these two hotly debated fields of research.

New Jersey Institute of Technology

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