Data-Driven Robotic Art

Summary

Students use math to find patterns in a dataset and drive servo motors to create an artistic representation of relationships in the data.

K-12 Learning Goals

Data → pattern → model → physical output; creative expression through quantitative thinking.

Undergraduates Learning

Curriculum support + mechatronics integration.

Overview

Participants learn how math can be applied to a dataset and find a pattern. This pattern is used to drive servo motors to represent relationships with one another. They are creatively expressing this by creating an artistic representation of the data. Our program uses fictional data, but it can be applied on a dataset of your choosing as well.

Activity Details

• Focus: Mathematical pattern recognition in data
• Output: Servo motor-driven artistic representation
• Flexibility: Can use fictional or custom datasets
• Setting: Classroom or lab environment
• Duration: 2 hours