Abstract
In this paper a hybrid approach for solving a robot global localization problem in an office-like environment is presented. The global localization problem deals with the estimation of the robot position when its initial pose is unknown. The core of this system is formed by a virtual sensor, capable of detecting and classifying the corners in the room in which the robot acts, and an NSP (Neuro Symbolic Processor) control that infers and computes the possible robot locations. In this way, the whole global self localization problem is tackled with a hybrid approach: a classic neurosymbolic hybrid system, composed of a weightless neural network and a BDI agent (it processes the map and build the landmark connections), a neural virtual sensor (for detecting landmarks) and a unified neurosymbolic hybrid system (NSP) devoted to the computation of the robot location on the given map.
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Coraggio, P., De Gregorio, M. (2007). WiSARD and NSP for Robot Global Localization. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_47
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DOI: https://doi.org/10.1007/978-3-540-73055-2_47
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