Real-time processing of force sensor signals based on LSTM-RNN

Multi-dimensional force sensors are of great significance to improve the perception of robots. It's very important to remove the drift and noise of the multi-dimensional force sensor signal caused by environmental changes. Recurrent Neural Network based on Long-Short Term Memory (LSTM-RNN) is proposed for real-time signal processing of multi-dimensional force sensors. Firstly, Adaptive Empirical Mode Decomposition (AEMD) is verified to be effective in removing drift and noise from multi-dimensional force sensor signals. Then, AEMD is utilized to process the force sensor signal and LSTM-RNN is trained by the processed signal. In the force test experiment, the errors of different signals processed by LSTM-RNN are very small and smaller than those of RNN signal processing, which proves that the trained LSTM-RNN can effectively process multi-dimensional force sensor signals in real time.