Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/16667
Title: Applying hybrid reinforcement and unsupervised weightless neural network learning algorithm on autonomous mobile robot navigation
Authors: Yusman Yusof, H. M.Asri H. Mansor
H. M.Dani Baba
Keywords: Reinforcement Learning
Q-learning
AutoWiSARD
Autonomous Navigation
Unsupervised Learning
Weightless Neural Network
LeJOS
Lego Mindstroms
Issue Date: 9-Oct-2017
Abstract: An autonomous system constructed using written computer programs based on human expert knowledge only handles anticipated and verified states. On the other hand, a self-learning algorithm allows an autonomous system to instinctively acquire knowledge, learn from experience and be more prepared to expect the unexpected. A novel hybrid self-learning algorithm which combines reinforcement and unsupervised weightless neural network algorithm learning was formulated. The self-learning algorithm was applied to an autonomous mobile robot navigation system in simulation and physical world. The result shows that the simulated and physical robot possesses the ability to self-learn by acquiring knowledge, learn and record experience without having prior information on the environment. The mobile robot was able to distinguish different types of obstacles i.e. corners and walls; and generate complex control sequences of actions in order to avoid these obstacles.
URI: http://ir.unikl.edu.my/jspui/handle/123456789/16667
Appears in Collections:Journal Articles



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