Collision detection of sensorless cooperative robots based on long short term memory network

In man-machine cooperative environment, in order to ensure the safety of the cooperative robot and people, it is required that the cooperative robot can detect the collision quickly and accurately. Since the use of external sensors not only increases the complexity of cooperative robots, but also increases the cost, this paper proposes a collision detection method for cooperative robots without external sensors based on long and short term memory network (LSTM). Firstly, the dynamic trajectory is calculated by using the observation matrix of the dynamic model, and then the operating data of the cooperative robot is collected through the trajectory, and the dynamic parameters of the cooperative robot are obtained by using the least square method. Finally, the LSTM network model is designed to compensate the errors generated in the identification process of the dynamic parameters, and the results before and after the compensation are compared by experiments. The experimental results show that the recognition accuracy of the dynamic model can be effectively improved by the compensated results of the network model, and the collision detection accuracy of the cooperative robot can be effectively improved by comparing the actual torque with the theoretical torque calculated by the compensated dynamic model.