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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Terrain Classification with Crawling Robot Using Long Short-Term Memory Network
verfasst von : Rudolf J. Szadkowski, Jan Drchal, Jan Faigl
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018
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Abstract
Terrain classification is a crucial feature for mobile robots operating across multiple terrains. One way to learn a terrain classifier is to use a stream of labeled proprioceptive data recorded during a terrain traversal. In this paper, we propose a new terrain classifier that combines a feature extraction from a data stream with the long short-term memory (LSTM) network. Features are extracted from the information-sparse data stream by applying a sliding window computing three central moments. The feature sequence is continuously classified by the LSTM network into multiple terrain classes. Furthermore, a modified bagging method is used to deal with a limited and unbalanced training set. In comparison to the previous work on terrain classifiers for a hexapod crawling robot using only servo-drive feedback, the proposed classifier provides continuous classification with the F1 score up to 0.88, and thus provide better results than SVM classifier learned on the same input data.