Learning Perception for Indoor Robot Navigation with a Hybrid Hidden Markov Model/Recurrent Neural Networks Approach
作者:
Edmondo Trentin,
Roldano Cattoni,
期刊:
Connection Science
(Taylor Available online 1999)
卷期:
Volume 11,
issue 3-4
页码: 243-265
ISSN:0954-0091
年代: 1999
DOI:10.1080/095400999116241
出版商: Taylor & Francis Group
关键词: Mobile Robot;Indoor Navigation;Learning;Sequence Recognition;Recurrent Neural Network;Multilayer Perceptron;Dynamic Programming;Hidden Markov Model;Hybrid Architecture
数据来源: Taylor
摘要:
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 (HMM) given a sequence of sensory (e.g. sonar) observations. Discriminative training is accomplished in a supervised manner, using gradient-descent. Recognition is carried out either in a dynamic programming framework, i.e. searching the maximum a posteriori probability of state-posteriors along paths of the HMM, or in real time. The approach is suitable for navigation and for map learning. Experiments of learning and recognition of noisy sequences acquired by a mobile robot equipped with 16 sonars are presented.
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