상세정보
학술지
딥 뉴럴 네트워크를 이용한 다관절 로봇의 충돌 판별
- 발행일
- 202104
- 출처
- 대한임베디드공학회논문지, v.16 no.2, pp.35-41
- ISSN
- 1975-5066
- 출판사
- 대한임베디드공학회
- 협약과제
- 20PD1200, 0.1mm 정밀도의 위치 및 속도가속도접촉력 교시가 필수적인 고난도 조립작업을 위한 범용 멀티모드 로봇 교시 디바이스 개발, 강동엽
- 초록
- 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.
- KSP 제안 키워드
- Collaborative robot, Collision detection, Deep learning method, Deep neural network(DNN), Human-Robot Interaction(HRI), deep learning(DL), identification accuracy, industrial applications, sensor data