Abstract
Recent advances have enabled machine learning methods to be integrated in many application domains to extract meaningful information from sensory data. Machine learning methods have been recently used in tactile sensing systems performing intelligent tasks with an effort to mimic human capabilities. This paper presents a convolutional neural network architecture for tactile object recognition in tactile sensing systems. The proposed architecture outperforms similar state of the art solutions by providing an average classification accuracy of 99.5%. This result pave the way towards the hardware implementation of such network to be integrated in the tactile sensing system.
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Acknowledgments
Authors would like to thank Eng. Mohamad Baalbaki and Eng. Fatima Saleh for their help in data collection.
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Ibrahim, A., Ali, H.H., Hassan, M.H., Valle, M. (2022). Convolutional Neural Networks Based Tactile Object Recognition for Tactile Sensing System. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2021. Lecture Notes in Electrical Engineering, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-95498-7_39
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DOI: https://doi.org/10.1007/978-3-030-95498-7_39
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