Implementation of an Embedded-System-Based Intelligent Vehicle with an Efficient Navigation Technique in Race Course Environments

Zink, Bryan, Michael Dupuis, Chaomin Luo, Mark Paulik, Sandra Yost, and

In this project, an embedded-system-based intelligent vehicle is implemented with the model car kit, servo, electric motors, and camera provided by Freescale. The motor control, line-following and navigation algorithms are developed to propel and steer the intelligent vehicle. The intelligent vehicle is equipped with a camera configured for image collection. Image processing and navigation algorithms direct the vehicle through a race course by tracking a black line on a raised white background.  From the measured sensory information, a map of the robot’s immediate limited surroundings is dynamically built for the line-following navigation. The intelligent vehicle’s navigation is designed to follow a line as quickly as possible. A local map composed of pixels is created by camera as the vehicle moves with limited sensory information. The motor control is accomplished by H-Bridge and pulse width modulation (PWM) techniques. This intelligent vehicle will participate in the Freescale Cup Intelligent Car Racing Competition.  The vehicles will be judged on fastest time to complete the course.