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'딥러닝 기반 센스리스 충돌감지 알고리즘' <IEEE Robotics and Automation Letters>에 발간

2019년 12월 11일 업데이트됨

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차세대 Indy(인디)에 탑재 될 '딥러닝 기반 센스리스 충돌감지 알고리즘' 이 <IEEE Robotics and Automation Letters>에 발간되었습니다.

상세한 내용은 다음과 같습니다. 논문 관련 문의는 pr@neuromeka.com 으로 부탁드립니다.

"Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach"

Abstract:

With increased human–robot interactions in industrial settings, a safe and reliable collision detection framework has become an indispensable element of collaborative robots. The conventional framework detects collisions by estimating collision monitoring signals with a particular type of observer, which is followed by collision decision processes. This results in unavoidable tradeoff between sensitivity to collisions and robustness to false alarms. In this study, we propose a collision detection framework (CollisionNet) based on a deep learning approach. We designed a deep neural network model to learn robot collision signals and recognize any occurrence of a collision. This data-driven approach unifies feature extraction from high-dimensional signals and the decision processes. CollisionNet eliminates heuristic and cumbersome nature of the traditional decision processes, showing high detection performance and generalization capability in real time. We verified the performance of the proposed framework through various experiments.


Journal: IEEE Robotics and Automation Letters

Issue Date: APRIL 2019

Volume: 4, Issue:2

On Page(s): 740-746

Print ISSN: 2377-3766

Online ISSN: 2377-3766

Digital Object Identifier: 10.1109/LRA.2019.2893400


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