Abstract
This paper introduces a hybrid system for modeling, learning and recognition of sequences of “states” in indoor robot navigation. States are broadly defined as local relevant situations (in the real world) in which the robot happens to be during the navigation. The hybrid is based on parallel Recurrent Neural Networks trained to perform a-posteriori state probability estimates of an underlying Hidden Markov Model given a sequence of sensory (e.g. sonar) observations. The approach is suitable for navigation and for map learning. Encouraging experiments of recognition of noisy sequences acquired by a mobile robot equipped with 16 sonars are presented.
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© 1999 Springer-Verlag London Limited
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Trentin, E., Cattoni, R. (1999). A Hybrid Framework for Indoor Robot Navigation. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-98. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0811-5_27
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DOI: https://doi.org/10.1007/978-1-4471-0811-5_27
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1208-2
Online ISBN: 978-1-4471-0811-5
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