Collision Detection
for Industrial Collaborative Robots
: A Deep Learning Approach

in IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 740-746, April 2019
Full Paper


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 trade-off 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 performed quantitative analysis and verified the performance of the proposed framework through various experiments.

A cobot immediately stops when bumping into a human, regardless of its motion and payload. The collsion detection framework can also be applied to other robots without any additional training.