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Terrain Classification with Crawling Robot Using Long Short-Term Memory Network

  • Rudolf J. SzadkowskiEmail author
  • Jan Drchal
  • Jan Faigl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)

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.

Keywords

Online classification Proprioception Recurrent neural networks 

Notes

Acknowledgments

The presented work has been supported by the Czech Science Foundation (GAČR) under research project No. 18-18858S. The support of grant No. SGS16/235/OHK3/3T/13 to Rudolf Szadkowski is also gratefully acknowledged. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic

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