Machine learning, a branch of artificial intelligence, has established itself in recent years as
a promising tool with the ability of innovating industries, making everyday life easier and
shaping the future of our society. Due to its extraordinary computational potential, machine
learning is increasingly used to solve problems in multi-robot systems. We trained the
YOLOv5 detection algorithm with the help of machine learning to recognize individual
millirobots using computer vision. We coded a program using the programming language
Python, which, with the help of the trained detection algorithm and the DeepSORT tracking
algorithm, detects and tracks millirobots in a swarm. The program stores the location data of
individual millirobots and enables the visualization of their movements. The program has
been tested in a robot test cell with a four-camera system.
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