Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

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Silva, Luciana L. [UNESP]
Tronco, Mário L. [UNESP]
Vian, Henrique A. [UNESP]
Pellinson, Giovana [UNESP]
Porto, Arthur J. V.

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Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.



Classifiers, Computer networks, Conformal mapping, Extractive metallurgy, Feature extraction, Image classification, Image enhancement, Learning systems, Mobile robots, Robotics, Robots, Vegetation, Visual communication, Wireless networks, Artificial neural networks, Autonomous robots, Catadioptric visions, Conical mirrors, Environment mappings, Hierarchical neural networks, Invariant patterns, Mapping systems, Omnivision, Sensorial systems, Topological maps, Two layers, Ultrasound sensors, Neural networks

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Proceedings of the International Joint Conference on Neural Networks, p. 3292-3297.