Line Detection and Lane Following for an Autonomous Mobile Robot
Bacha, Andrew Reed
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The Autonomous Challenge component of the Intelligent Ground Vehicle Competition (IGVC) requires robots to autonomously navigate a complex obstacle course. The roadway-type course is bounded by solid and broken white and yellow lines. Along the course, the vehicle encounters obstacles, painted potholes, a ramp and a sand pit. The success of the robot is usually determined by the software controlling it. Johnny-5 was one of three vehicles entered in the 2004 competition by Virginia Tech. This paper presents the vision processing software created for Johnny-5. Using a single digital camera, the software must find the lines painted in the grass, and determine which direction the robot should move. The outdoor environment can make this task difficult, as the software must cope with changes in both lighting and grass appearance. The vision software on Johnny-5 starts by applying a brightest pixel threshold to reduce the image to points most likely to be part of a line. A Hough Transform is used to find the most dominant lines in the image and classify the orientation and quality of the lines. Once the lines have been extracted, the software applies a set of behavioral rules to the line information and passes a suggested heading to the obstacle avoidance software. The effectiveness of this behavior-based approach was demonstrated in many successful tests culminating with a first place finish in the Autonomous Challenge event and the $10,000 overall grand prize in the 2004 IGVC.
- Masters Theses