Mobile robot navigation: neural Q-learning

Chin Yun Soh, Subramaniam Parasuraman, Ganapathy Velappa

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


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 languageEnglish
Title of host publicationAdvances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) - Volume 3
EditorsNatarajan Meghanathan, Dhinaharan Nagamalai, Nabendu Chaki
Place of PublicationBerlin Germany
Number of pages10
ISBN (Print)9783642315992
Publication statusPublished - 2013
EventInternational Conference on Advances in Computing and Information Technology 2012 - Chennai, India
Duration: 13 Jul 201215 Jul 2012
Conference number: 2nd (Proceedings)


ConferenceInternational Conference on Advances in Computing and Information Technology 2012
Abbreviated titleACITY 2012
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