Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/44159
Title: Reinforcement Learning-SLAM for finding minimum cost path and mapping
Author: Arana-Daniel, N.
Rosales-Ochoa, R.
Lopez-Franco, C.
Nuno, E.
Issue Date: 2012
Abstract: In this work, we propose the integration of two of the most widely used approaches for the implementation of autonomous navigation systems: the reinforcement learning for path finding, along with SLAM (Simultaneous Localization and Mapping) type algorithms for localization and mapping of the environment. These two approaches are integrated to address the problem of how a robot should explore an unknown and dynamic environment while it collects perception features in order to locate itself and, at the same time, to obtain information clues about cost traversability of an area. So, when a robot is exploring and mapping with a SLAM algorithm it is also learning to associate perception features with costs and actions to find optimal paths from the starting point to the goal point in dynamical environments. � 2012 TSI Press.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-84870811259&partnerID=40&md5=30837eac0ff732d47ead323052d55810
http://hdl.handle.net/20.500.12104/44159
Appears in Collections:Producción científica UdeG

Files in This Item:
There are no files associated with this item.


Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.