Fast Recognition of Snap-Fit for Industrial Robot Using a Recurrent Neural Network
Tao Cui, Rui Song, Fengming Li, Tianyu Fu, Chaoqun Wang, Yibin Li
Abstract
Snap-fit recognition is an essential capability for industrial robots in manufacturing. The goal is to protect fragile parts by quickly detecting snap-fit signals in the assembly. In this letter, we propose a fast recognition method of snap-fit for industrial robots. A snap-fit dataset generation strategy of automatically acquiring labels is presented in the presence of data collection is complicated. A multilayer recurrent neural network (RNN) is designed for snap-fit recognition. An extensive evaluation based on two different datasets shows that the proposed method makes reliable and fast recognitions. Real-time experiments on industrial robot also demonstrate the effectiveness of the proposed method.