Development of safety method for a 3-DOF industrial robot based on recurrent neural network

In this paper, a safety method for a 3-DOF industrial robot is developed based on recurrent neural network (RNN). Safety standards for human robot interaction (HRI) are taken into accounts. The main objective is to detect the undesired collisions on any of robot links. Since most of industrial robots are not collaborative, the dependence of the method on torque sensors to detect collisions makes its ability to use very restricted. Therefore, only the position data of joints are collected to be the data inputs of the proposed method in order to detect the undesired collisions. These data are aggregated from KUKA LWR IV robot while no collisions and in another time when applying collisions. These data are used to train the proposed RNN using Levenberg-Marquardt LM algorithm. KUKA robot is configured to act as a 3-DOF manipulator that moves in space and under the effect of gravity. The results show that the modelled and trained RNN is sensitive and efficient in detecting collisions on each link of robot separately. Studying the resulted error from the developed model reveals clearly that the method is reliable.