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http://dx.doi.org/10.14372/IEMEK.2021.16.2.35

Collision Identification of Collaborative Robots Using a Deep Neural Network  

Kwon, Wookyong (ETRI)
Jin, Yongsik (ETRI)
Lee, Sang Jun (Jeonbuk National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.2, 2021 , pp. 35-41 More about this Journal
Abstract
Human-robot interaction has received a lot of attention as collaborative robots became widely used in many industrial applications. This paper proposes a deep learning method for collision identification of collaborative robots. This method expands the idea of CollisionNet, which was proposed for collision detection, to identify locations of collisions. Collision identification is far more difficult compared to collision detection, because sensor data are highly correlated when collisions occur at close locations. To improve the identification accuracy, this paper proposes an auxiliary loss, which is called consistency loss. This auxiliary loss guides the training of a deep neural network to predict consistent predictions for each single collision event. In experiments, we demonstrate the effectiveness of the proposed method.
Keywords
Collision identification; Collaborative robot; Deep learning; Dilated convolution; Consistency loss;
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