Abstract
This paper presents the mobile robot navigation technique which utilizes Reinforcement Learning (RL) algorithms and Artificial Neural Network (ANN) to learn in an unknown environment for mobile robot navigation. This process is divided into two stages. In the initial stage, the agent will map the environment through collecting state-action information according to the Q-Learning procedure. Second training process involves Neural Network, which utilizes the state-action information gathered in the earlier phase of training samples. During final application of the controller, Q-Learning would be used as primary navigating tool whereas the trained Neural Network will be employed when approximation is needed. MATLAB simulation was developed to verify and validate the algorithm before real time implementation using Team AmigoBotTM robot. The results obtained from both simulation and real world experiments are discussed. © Springer-Verlag Berlin Heidelberg 2013.
Original language | English |
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Title of host publication | Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) - Volume 3 |
Editors | Natarajan Meghanathan, Dhinaharan Nagamalai, Nabendu Chaki |
Place of Publication | Berlin Germany |
Publisher | Springer |
Pages | 259-268 |
Number of pages | 10 |
ISBN (Print) | 9783642315992 |
DOIs | |
Publication status | Published - 2013 |
Event | International Conference on Advances in Computing and Information Technology 2012 - Chennai, India Duration: 13 Jul 2012 → 15 Jul 2012 Conference number: 2nd https://link.springer.com/book/10.1007/978-3-642-31600-5?page=1#toc (Proceedings) |
Conference
Conference | International Conference on Advances in Computing and Information Technology 2012 |
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Abbreviated title | ACITY 2012 |
Country/Territory | India |
City | Chennai |
Period | 13/07/12 → 15/07/12 |
Internet address |