Lane Detection and Obstacle Avoidance in Mobile Robots
J. Johnson Rajasingh · OhioLink ETD Center (Ohio Library and Information Network) · 2010
Robots are getting more and more involved in every facet of life.They have replaced humans in many fields with their dependability and adaptability.While they have been around for more than half a century, only recently have they developed the intelligence to sense their surroundings and behave accordingly.This thesis describes an approach to navigating a robot along a roadway-type course bound with white lines and strewn with obstacles.The Bearcat Cub is the University of Cincinnati"s mobile robot developed for competing at the Intelligent Ground Vehicle Competition (IGVC).The Autonomous Challenge component of the IGVC requires the robot to navigate through an unknown complex obstacle course.The robot must sense its surroundings, detect its confines and calculate a heading.The Bearcat Cub utilizes vision and laser sensor reading to detect the lanes and obstacles respectively.This thesis describes how the robot processes the camera images, detects the lanes and calculates a heading.It also discusses how obstacles are detected and suitable avoidance methods.Finally, a new approach to combining both systems has been described.This avoids conflict in the results from both systems.Various lighting conditions and obstacle layouts have been tried and tested to ensure the algorithms effectiveness.The vision processing algorithm discussed in this thesis was tested in the 2008 Intelligent Ground Vehicle Competition with acceptable outcomes.The obstacle avoidance methods have been developed later and can be implemented in future competitions.