Year of Graduation
2016
Level of Access
Open Access Thesis
Embargo Period
5-20-2016
Department or Program
Computer Science
First Advisor
Eric Chown
Second Advisor
Bill Silver
Abstract
Tasks which are simple for a human can be some of the most challenging for a robot. Finding and classifying objects in an image is a complex computer vision problem that computer scientists are constantly working to solve. In the context of the RoboCup Standard Platform League (SPL) Competition, in which humanoid robots are programmed to autonomously play soccer, identifying other robots on the field is an example of this difficult computer vision problem. Without obstacle detection in RoboCup, the robotic soccer players are unable to smoothly move around the field and can be penalized for walking into another robot. This project aims to use gradient and color signatures to identify robots in an image as a novel approach to visual robot detection. The method, "Fastgrad", is presented and analyzed in the context of the Bowdoin College Northern Bites codebase and then compared to other common methods of robot detection in RoboCup SPL.