Research on Zero-Force control and collision detection of deep learning methods in collaborative robots

Abstract

In the process of intelligent manufacturing, collaborative robots have strict requirements in terms of safety, interaction, and flexibility. In order to solve the problem of flexible and smooth interaction of collaborative robots, this paper profoundly researches the zero-force control and collision detection method based on deep learning. First, for the zero-force control problem of collaborative robots, the complete kinetic equations of the three-time friction force model based on acceleration are established, and a genetic algorithm is used for multi-parameter identification of the friction force model. Second, for the problem of collision detection in demonstration reproduction, this paper proposes an enhanced sequence coding method based on the iTransformer network, which embeds the whole time series of each variable independently as a token by inverting the time series, to improve the generalization ability of the model. Meanwhile, considering local and global time series features, the CNN-iTransformer collision detection method combining CNN(convolutional neural network) and iTransformer network is constructed. The CNN-iTransformer can efficiently learn and retain the long-term dependencies in the input sequences, which solves the problem of inaccurate modeling of the schematic reproduction collision detection method. Finally, it is proved experimentally that the velocity-based cubic friction force model can better solve the zero-force control problem, and the CNN-iTransformer network can accurately detect the robot’s abnormal collision behavior without relying on the model.