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   Applying hybrid reinforcement and unsupervised weightless neural network learning algorithm on autonomous mobile robot navigation  
   
نویسنده yusof y. ,mansor h.m.a.h. ,baba h.m.d.
منبع journal of telecommunication, electronic and computer engineering - 2017 - دوره : 9 - شماره : 1-3 - صفحه:133 -138
چکیده    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.
کلیدواژه Autonomous navigation; AutoWiSARD; Lego mindstroms; LeJOS; Q-learning; Reinforcement learning; Unsupervised learning; Weightless neural network
آدرس industrial automation section,universiti kuala lumpur malaysia, Malaysia, france institute,bandar baru bangi,selangor, Malaysia, faculty of electrical engineering,universiti teknologi mara,shah alam,selangor, Malaysia
 
     
   
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