Booth Id:
ROBO023T
Category:
Robotics and Intelligent Machines
Year:
2018
Finalist Names:
Ellis, Daniel (School: Saint Paul Academy and Summit School)
Hall, Michael (School: Saint Paul Academy and Summit School)
Abstract:
Autonomous vehicles have the potential to vastly increase road safety and reduce the
environmental impact of personal vehicles. While this technology is still in development, rapid
progress has been made in areas such as processing speed and machine learning integration1.
The goal of this project was to create an autonomous vehicle that incorporates cutting edge
machine learning techniques on a smaller scale, while still being accessible. To meet this goal,
we developed CARL, the Convolutional Autonomous dRiving vehicLe. CARL was built using a
3D printed chassis and accessible electronics, including a Raspberry Pi Model 3 and an Arduino
Uno. A custom lightweight convolutional neural network, called CARLnet, was developed for the
purpose of guiding the car around an arbitrarily shaped paper track. CARLnet was trained on
approximately 8000 images and achieved over 95 percent accuracy in training. In testing, the car
was able to drive around a track autonomously without human intervention. All data was
processed using the car’s onboard computer.
Awards Won:
Air Force Research Laboratory on behalf of the United States Air Force: First Award of $750 in each Intel ISEF Category