Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach

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,...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2019-04, Vol.4 (2), p.740-746
Main Authors: Heo, Young Jin, Kim, Dayeon, Lee, Woongyong, Kim, Hyoungkyun, Park, Jonghoon, Chung, Wan Kyun
Format: Article
Language:English
Subjects:
Artificial neural networks
Collaboration
Collision avoidance
Collisions
Convolution
Deep learning
Deep learning in robotics and automation
False alarms
Feature extraction
Machine learning
Monitoring
physical human-robot interaction
Robot sensing systems
Robots
Safety
Service robots
Signal monitoring
Signal processing
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2893400