Usage of computer vision and machine learning to solve 3D mazes

This thesis deals with incorporating artificial intelligence into a humanoid robot by making a cognitive model of the learning process. The goal is to “teach” a specialized humanoid robot, the iCub robot, to solve any puzzle, wherein a ball of a given color would be placed at the ‘start’ position of the maze, and the robot would navigate the ball through obstacles and get the ball to the ‘finish’ position. The robot would be able to move the ball through the maze by physically tilting the base of the puzzle with its hand. In the process, the robot would utilize the most efficient way possible. If no possible path exists, the robot would not begin to solve the maze. The first approach was to test the feasibility of the project and an open loop offline-learning algorithm was used to test if the robot could physically solve a given maze. Once this proved successful, the robot was then given multiple mazes that were labeled with the best path, so that it would be able to pick up on the ideal policy on its own, as a result of supervised learning. Once sufficient training was provided, the robot was tested on multiple patterns of mazes that were not seen beforehand by the robot. The robot correctly solved all test mazes that were given to it, giving it a final accuracy rate of 100%.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Kamalnath, Vishnu Nath

Contributors dc:contributor

• Smaragdis, Paris
• Levinson, Stephen E.

• Artificial Intelligence
• Machine Learning
• Robotics
• iCub
• Computer Vision
• Computer Science
• Pattern Recognition
• learning
• q-learning

Statement dc:rights

• Copyright 2013 Vishnu Nath Kamalnath

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/45440

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45440