Abstract
In this article, we analyze a Machine Learning model for classifying the energy consumption of a multi-legged robots over different terrains. We introduce a system based in three popular techniques: Recurrence Plot (RP), Convolutional Neural Network (CNN), and Dimensionality Reduction. We use RP for transforming the energy consumption of the robots to grayscale 2D images. Due to computational restrictions, we apply a linear dimensionality reduction technique for transforming the images into a smaller feature space. The CNN is applied for classifying the images and to predict the terrain. We present results using several CNN architectures over real-data obtained in six+ types of terrains.
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Notes
- 1.
Center for Robotics and Autonomous Systems, FEL, CVUT: https://robotics.fel.cvut.cz/cras/.
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Acknowledgements
This work has been supported by the Czech Science Foundation (GACR) under research project No. 18-18858S.
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Falck, R.H., Čižek, P., Basterrech, S. (2021). Recurrence Plot and Convolutional Neural Networks for Terrain Classification Using Energy Consumption of Multi-legged Robots. In: Matoušek, R., Kůdela, J. (eds) Recent Advances in Soft Computing and Cybernetics. Studies in Fuzziness and Soft Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-030-61659-5_1
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