Pocket Racer: An accessible autonomous racing educational platform.

Area of Science:

• Robotics
• Artificial Intelligence
• STEM Education

Background:

• Autonomous racing platforms can enhance STEM education but are often too large or expensive for indoor educational settings.
• Existing solutions lack accessibility for high-speed maneuvers in confined spaces, hindering practical AI and robotics learning.

Purpose of the Study:

• To develop an open-source, affordable, and pocket-sized autonomous racing robot for indoor educational competitions.
• To enable undergraduate students to gain hands-on experience in artificial intelligence (AI) and robotics through accessible racing platforms.

Main Methods:

• Designed and constructed the Pocket Racer, a miniature mobile robot using off-the-shelf components and a Raspberry Pi Zero 2 W edge device.
• Developed an open-source website and dataset with build instructions to ensure platform accessibility.
• Demonstrated head-to-head autonomous racing capabilities in indoor environments.

Main Results:

• Successfully demonstrated head-to-head autonomous racing with Pocket Racer platforms.
• Achieved high-speed overtaking maneuvers exceeding 15 km/h in indoor settings.
• The platform is easily assembled and cost-effective, utilizing readily available components.

Conclusions:

• Pocket Racer provides an accessible solution for hands-on AI and robotics education, fostering practical learning experiences.
• The open-source nature and low cost of Pocket Racer promote wider adoption in university-level STEM programs.
• This platform facilitates engaging educational activities through exciting autonomous racing competitions.