Fast Recognition of Snap-Fit for Industrial Robot Using a Recurrent Neural Network
Fast Recognition of Snap-Fit for Industrial Robot Using a Recurrent Neural Network
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DOI：
10.1109/lra.2022.3209161
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发表时间：
2023-03
影响因子：
5.2
通讯作者：
Tao Cui;R. Song;Fengming Li;Tianyu Fu;Chaoqun Wang;Yibin Li
中科院分区：
文献类型：
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作者：
Tao Cui;R. Song;Fengming Li;Tianyu Fu;Chaoqun Wang;Yibin Li
卡扣识别是工业机器人在制造业中的一项基本能力。目标是通过快速检测组件中的卡扣信号来保护易碎部件。在这封信中，我们提出了一种快速识别方法的卡扣适合工业机器人。在数据采集复杂的情况下，提出了一种自动获取标签的卡扣拟合数据集生成策略。设计了一种多层递归神经网络（RNN）用于卡扣识别。基于两个不同数据集的广泛评估表明，所提出的方法具有可靠和快速的识别。工业机器人的实时实验也证明了该方法的有效性。
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.