Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach

By applying robots while collaborating with a human in an industrial setting to provide more flexible and productive industries, safe interaction and collision detection have become an indispensable element of the collaborative robots. In such a dynamic environment, safe collaboration scenarios are needed to be designed using reliable methods to monitor collision-related signals and avoid a dangerous collision. Since human's hand is the most exposed limb to collision during cooperation with a robot, new flexible methods should be conducted to use in industries by considering hand safety. In this study, collision monitoring is developed using force myography of a worker forearm and robot dynamic parameters. A method based on deep neural network is proposed to distinguish any occurrence of a collision between a worker's hand and robot's arm during the collaboration. The proposed approach can be applied to provide a reliable interaction with no unnecessary robot stop during working by classifying unintended collision. Various experiments have been conducted to evaluate the proposed method. The results show that the proposed scheme can successfully detect a collision and classify human intention to provide safe and reliable cooperation with a robot in an industrial environment.