Abstract
In this paper, we advance a novel approach to the problem of autonomous robot navigation. The environment is a complex indoor scene with very little a priori knowledge, and the navigation task is expressed in terms of natural language directives referring to natural features of the environment itself. The system is able to analyze digital images obtained by applying a sensor fusion algorithm to ultrasonic sensor readings. Such images are classified in different categories using a case-based approach. The architecture we propose relies on fuzzy theory for the construction of digital images, and wavelet functions for their representation and analysis. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Micarelli, A., Panzieri, S., & Sansonetti, G. (2007). Case-based reasoning in robot indoor navigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4626 LNAI, pp. 284–298). Springer Verlag. https://doi.org/10.1007/978-3-540-74141-1_20
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