Abstract
Robot self localization is a crucial issue in autonomous robotic research. In the last years, several approaches have been proposed to solve this problem. In this paper, we describe a landmark based neurosymbolic hybrid approach to tackle the global localization problem. We use the same approach to cope with the whole problem: from landmark recognition to position estimation. The map given to the robot is interpreted by a neurosymbolic system (formed by a weightless neural network and a BDI agent) for extracting landmark information. A “virtual neural sensor” is used, during robot navigation, for detecting the landmarks in the real environment. These information (map and detected landmarks) are finally processed by a unified neurosymbolic hybrid system (NSP) for determining the robot location on the given map.
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Coraggio, P., De Gregorio, M. (2007). A Neurosymbolic Hybrid Approach for Landmark Recognition and Robot Localization. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_54
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DOI: https://doi.org/10.1007/978-3-540-75555-5_54
Publisher Name: Springer, Berlin, Heidelberg
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