イベントドリブン型MEMS-LSI集積化触覚センサアレイシステムを用いた物体判定

抄録

We have developed tactile sensor systems for next-generation robots. To install a large number of tactile sensors, we have proposed MEMS-LSI integrated tactile sensors. The integrated device has following features: capacitive type 3-axis force sensing, embedded diode-based temperature sensing, signal processing for sensing data digitalization, and event-driven response for efficient serial bus communication. This paper demonstrates a sensor array system as up-to 40 integrated tactile sensors which are connected on one bus line. After acquiring the sensing data from the sensor array system, we applied a machine learning technique for target object judgment. The objective of the judgment is to classify the targets into normal object and abnormal object. With the sensor array system, data preprocessing and tuned RNN/LSTM neural network models, we achieved high-accuracy, high-precision, and high-recall scores for the experiment of the judgment.