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Demonstrates head-to-head racing capabilities in undergraduates, indicating enhanced engagement in AI education.
Accessible autonomous racing can engage undergraduate students emersed in artificial intelligence (AI) and robotics to receive an education through the excitement of maneuvering sharp corners and overtaking opponents. In particular, the construction and programming of miniature mobile racing robots can facilitate head-to-head racing competitions for use indoors within classrooms and hallways. Unfortunately, existing racing platforms remain inaccessible for overtaking maneuvers in such confined settings because they are physically large or expensive due to the computational cost of enabling such high performance. In an attempt to address these issues, we present Pocket Racer, an open-source, pocket-sized racing robot capable of head-to-head racing within indoor environments in education, i.e. university hallways or classrooms. We demonstrate head-to-head autonomous racing with our Pocket Racer platforms, enabling high speed overtaking upwards of 15 km/h. Designed to be easily assembled with off the shelf components and a low-cost edge device (Raspberry Pi Zero 2 W), our Pocket Racer platform is made accessible through an open source website and dataset detailing build instructions. By making a pocket-sized head-to-head autonomous racing platform accessible for undergraduate students, our work hopes to further hands on education in the age of physical AI.
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Cha et al. (2026) studied this question.
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