Abstract
The terrain classification problem is a relevant task in autonomous robots, which can help in the control locomotion and motion planning of autonomous robots. We conduct several experiments in different environments, where a hexapod walking robot covers some specific terrains. In this paper, we present an experimental analysis of the binary terrain classification problem using the most important variable (current signal) related to the energy consumption of the robot. The current signal is a sequential data that evolves in time, therefore our problem is limited to develop a machine learning method for classifying this signal according to the terrain. We analyze the problem using the Long Short-Term Memory (LSTM) model, which is a Recurrent Neural Networks that has obtained good performance for time-series classification. We evaluated several binary scenarios, where each scenario presents two different types of terrains. Our results show that the LSTM model trained only with information related to the current signal is able to distinguish binary situations of terrain.
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Notes
- 1.
Center for Robotics and Autonomous Systems, Faculty of Electrical Engineering, Czech Technical University: http://robotics.fel.cvut.cz/cras/.
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Acknowledgment
This work has been supported by the Czech Science Foundation (GAČR) under research project No. 18-18858S, and the authors acknowledge the support of the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”.
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Iegorova, V., Basterrech, S. (2020). Binary Classification of Terrains Using Energy Consumption of Hexapod Robots. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_91
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