Trajectory Generation for Industrial Robots Integrating the Bidirectional Long Short-Term Memory Algorithm

In industrial robot trajectory planning, trajectory segmentation has crucial importance in distinguishing between different geometric primitives, such as straight lines and arcs. Separating these elements facilitates allocating optimized motion instructions, customized to each segment category. This distinction ensures both improved execution smoothness and better operational performance. For this purpose, the Bidirectional Long Short-Term Memory (Bi-LSTM) machine learning algorithm has been implemented to segment trajectories into linear and arc-shaped parts, for which dedicated robotic commands can be used. First, several Bi-LSTM models with different architectures were trained using a synthetic dataset containing different shapes with labelled segments. Then, a theoretical study was performed to evaluate the accuracy of recognizing different shape segments using a test dataset. Finally, the generated trajectories, which implemented the best machine learning model, were transferred into the RoboDK software to launch the robot. Two different methods were used to generate trajectories for the UR3 industrial robot. The original trajectory was generated using linear interpolation only, while the second was generated using the machine learning algorithm. The experimental results show significant differences in terms of the smoothness and velocity profiles between these two trajectory generation methods. By enabling automatic classification of trajectory segments into line and arc primitives using the Bi-LSTM-based approach, the execution time is reduced by up to 43.5% and the vibration amplitude by up to 27.4% at higher speeds around 250 mm/s. However, this came at the cost of reduced accuracy at high speed, with reproduction error reaching 1.4–1.9 mm versus 0.46–0.7 mm for linear interpolation.