Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/44158
Title: Reinforced-SLAM for path planing and mapping in dynamic environments
Author: Arana-Daniel, N.
Rosales-Ochoa, R.
Lopez-Franco, C.
Issue Date: 2011
Abstract: In this work, an artificial intelligence approach to the problem finding a path for exploring an unknown environment and at the same time creating a map with uncertainties in robot pose and measures, while locating itself with this map (SLAM problem) is used to create an intelligent, robust and efficient navigation system for robots. We propose the integration of two of the most widely used approaches for the implementation of autonomous systems, the reinforcement learning for navigation in unknown and dynamic environments, along with the SLAM (Simultaneous Localization and Mapping) type algorithms for localization and mapping the environment. Experiments in section IV also confirms the algorithm performance in presence of uncertainties on mapping and sensor readings for the path planing problem. � 2011 IEEE.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-84855811350&partnerID=40&md5=9b5acd271ff2af9f6789643aeddc8214
http://hdl.handle.net/20.500.12104/44158
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.