Using LSTM Recurrent Neural Networks to Predict the Trajectory of Human Hand Movement in the Working Area of a Collaborative Robot-Manipulator

Аннотация

The article examines the use of LSTM recurrent neural networks for predicting the trajectory of human hand movement in the working area of a collaborative robot-manipulator. The results demonstrate high prediction accuracy for slow movements, but reveal certain limitations for fast and complex trajectories. The proposed approach is aimed at improving the safety and efficiency of the joint work of humans and robots within the framework of the concept of Industry 5.0. Key words: Industry 5.0, Collaborative Robot, Work Area, Computer Vision, LSTM, Trajectory Prediction.