As the SAMs travel autonomously, their AI brains will continuously analyze their motion, optimizing their posture to increase speed and power. If a swimmer comes closer to a mate and increases his speed, he may keep on getting closer and closer – up to the point where he has to slow down, which in turn triggers a retreat to the optimal position.
This allows the robots to autonomously change their locomotion strategy while swimming, either individually or in a cooperative function.
“As the SAMs swim, they are constantly interacting with the environment, running that algorithm, and recalculating where they are in that formation and how fast or how efficiently they are moving. The goal is to find the best strategy for the cluster to swim together in a manner that’s both fast and efficient,” says Demir.