An autonomous path finding strategy for an Artificial Intelligence enabled Lego Mindstorms robot

Loading...
Thumbnail Image
Author (Corporation)
Publication date
2017
Typ of student thesis
Master
Course of study
Type
11 - Student thesis
Editors
Editor (Corporation)
Parent work
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Hochschule für Wirtschaft FHNW
Place of publication / Event location
Olten
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The objective of this Master thesis is to research how Artificial Intelligence can contribute to make robots more intelligent in order to cope with real world applications. The vision is a self-driving robot which uses different Artificial Intelligence methods and algorithms such as reinforcement learning and deep reinforcement learning in order to make the robot more intelligent by enabling it to learn. The basis is that robots can recognize ways and obstacles by using different sensors. We extended our research to provide the comparison between Q-learning and Deep Q-Network as pathfinding agent in grid map environment. The simulation results show that both of algorithms work well with small dimensional state spaces. Nevertheless, Deep Q-Network can perform a better performance and more stability than Q-learning when states spaces increasing. Afterward, A Lego Mindstorms EV3 applying reinforcement learning algorithms in order to find the path to the target and avoid obstacles will be proposed, developed, and discussed.
Keywords
Subject (DDC)
Project
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Review
Open access category
License
Citation
Anh, H. T. (2017). An autonomous path finding strategy for an Artificial Intelligence enabled Lego Mindstorms robot [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/39833