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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Applying hybrid reinforcement and unsupervised weightless neural network learning algorithm on autonomous mobile robot navigation.pdf | 6.51 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.