View source for Publications:Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors

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{{PublicationSetupTemplate|Author=Eric A. Antonelo, Albert-Jan Baerveldt, Thorsteinn Rögnvaldsson, Mauricio Figueiredo
|PID=239330
|Name=Antonelo, Eric A. (Electronics and Information Systems (ELIS) department, Ghent university, Belgium);Baerveldt, Albert-Jan [albbaex] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Rögnvaldsson, Thorsteinn [denni] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Figueiredo, Mauricio ( State University of Maringá, Brazil)
|Title=Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation : Inhibiting Undesirable Behaviors
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
|JournalISSN=
|Status=
|Volume=
|Issue=
|HostPublication=International Joint Conference on Neural Networks, 2006. IJCNN '06
|Conference=International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver
|StartPage=498
|EndPage=505
|Year=2006
|Edition=
|Pages=
|City=Piscataway, N.J.
|Publisher=IEEE Press
|Series=IEEE International Joint Conference on Neural Networks (IJCNN)
|SeriesISSN=1098-7576
|ISBN=0-7803-9490-9
|Urls=
|ISRN=
|DOI=http://dx.doi.org/10.1109/IJCNN.2006.246723
|ISI=000245125900073
|PMID=
|ScopusId=2-s2.0-40649114292
|NBN=urn:nbn:se:hh:diva-2112
|LocalId=2082/2507
|ArchiveNumber=
|Keywords=mobile robots;neurocontrollers;path planning
|Categories=Teknik och teknologier (2)
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|Notes=<p>©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p>
|Abstract=<p>Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.</p>
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|CreatedDate=2008-11-07
|PublicationDate=2008-11-07
|LastUpdated=2013-10-15
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239330}}
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