IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Collision Identification of Collaborative Robots Using a Deep Neural Network

딥뉴럴네트워크를 이용한 다관절 로봇의 충돌 판별

  • Received : 2021.02.16
  • Accepted : 2021.04.01
  • Published : 2021.04.30
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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.

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Acknowledgement

이 연구는 2020년도 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원에 의한 연구임 (20009396).

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