2019년 12월 11일 업데이트됨
뉴로메카의 CILab(팀장: 허영진 박사)과 포스텍 로봇연구실이 공동 연구개발,
차세대 Indy(인디)에 탑재 될 '딥러닝 기반 센스리스 충돌감지 알고리즘' 이 <IEEE Robotics and Automation Letters>에 발간되었습니다.
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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