Neural networks design and training for safe human-robot cooperation

In the present paper, a neural network (NN) is proposed for detecting the human-manipulator collisions. Because of that purpose, three types of NNs are designed and trained named as; multilayer feedforward, cascaded forward, and recurrent NNs. The NN is designed based on the joints’ dynamics of the manipulator. In addition, the NN is trained using a dataset with and without the collisions using the algorithm of Levenberg-Marquardt to detect the collisions happened with the manipulator. During the design of the NN, only the intrinsic joint position sensor of the robot is used which enable the proposed method to be applied to any robot. The three designed neural networks are compared quantitatively and qualitatively to each other. Furthermore, the proposed method was evaluated with the KUKA LWR manipulator using one joint motion. The results of the designed systems achieve high effectiveness in detecting the collisions between human and robot.