Year: 2021
Type: article
Abstract: Significance Swimming microorganisms and migrating cells have developed various strategies in order to move in nutrient-rich or other chemical environments. We apply a genetic algorithm to the internal decision-making machinery of a model microswimmer and show how it learns to approach nutrients in static and dynamic environments. Strikingly, the emerging dynamics resembles the well-known run-and-tumble motion of swimming cells. We demonstrate that complex locomotion and navigation strategies in chemical environments can be achieved by developing a surprisingly simple internal machinery, which in our case, is represented by a small artificial neural network. Our findings shed light on how small organisms have developed the capability to conduct environment-dependent tasks.
Institution: TU Wien
Language: English
Topic: Micro and Nano Robotics
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3126924883